MASTER OF SCIENCE DISSERTATION Emergent Technologies and Design Programme Architectural Association School of Architecture 2012-2013 Candidates
ANNA KULIK NAPAK ARUNANONDCHAI NAPHAT CHONGRATANAKUL Tutors Michael Weinstock, Programme Director George Jeronimidis, Programme Director Evan Greenberg, Studio Master Mehran Gharleghi, Studio Tutor Wolf Mangelsdorf, Visiting Professor
4 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMME
Programme:
Emergent Technologies and Design
Student Names:
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
Submission Title:
Permafrost Pioneering
Course Director:
Michael Weinstock
Course Title:
Emergent Technologies and Design
Submission Date:
DECLARATION: "I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged"
SIGNATURE OF STUDENT(S):
Anna Kulik
Napak Arunanondchai
Naphat Chongratanakul
5 Date:
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
6 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
ACKNOWLEDGMENTS
We would like to express our gratitude to our families, friends, and advisers who have supported us through this dissertation. It has been a wonderful year in London working on this fascinating topic. We would never be able to accomplish what we have achieved without kind advices from Mike Weinstock, George Jeronimidis, Evan Greenberg and Mehran Gharleghi. Furthermore, we would like to extend our sincere appreciation to Wolf Mangelsdorf and Ukrit Pankaew as external consultants.
7 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
Icelandic Tundra
8 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
ABSTRACT
The world is changing every second, causing alterations in the environmental and earth surface condition. The Siberian Tundra, that is now uninhabitable due to its climatic condition has a great potential to be occupied by people with this environmental shift. The project aims to design a self-sustainable city under extreme climatic condition based on existing technologies. The main intention is to sustain the city with the local energy from methane gas that is naturally released from permafrost and to guarantee the structural performance of the foundations and load bearing systems, that are failing today. The city infrastructure and typologies are specifically designed to answer the needs of human, both in urban and patch scales. Energy and transportation networks are also implemented to complete an innovative city system.
9 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
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TABLE OF CONTENTS
ABSTRACT 9 INTRODUCTION 13 DOMAIN 17 Self sustainable habitat 19 Unoccupied Biomes 21 40C World :: Temperature Switch 22 Wetland 30 Wetland Species 34 Conclusions of environmental part of domain. 36 Architecture On Permafrost 39 Conclusions for the domain chapter 50
METHODS 55 Methods flowchart Environmental analysis Cellular automata Pneumatic system Circle packing Delaunay triangulation and voronoi cells Minimum spanning tree and directness ratio Genetic Algorithms and weighting mechanism
RESEARCH DEVELOPMENT
Wetland 4c world rates ground conditions caused
56 58 60 62 63 64 65 66
71
72 Areas of investigation 75 Main equation 76 Conclusions 81 Self Sustainable Habitat On Permafrost 87 Cellular automata tests. Tool Calibration 102 Color codes for Cellular Automata 103
CONCLUSIONS 112 DESIGN DEVELOPMENT
Structure and Tensairity Circle packing Spread delaunay Networks in the system Typologies distribution. Regions and cells Network for typologies Growth of networks and allocation of typologies Units And Typologies Pneumatic system Detailed calculations Form Optimization
117 118 120 122 124 126 130
132 136 138 138 146
FURTHER DEVELOPMENT
175
PERSONAL CONTRIBUTIONS
179
BIBLIOGRAPHY
190
APPENDICIES 199 Processing script CA
200
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INTRODUCTION
Due to demographic pressures there is a constantly growing demand in inhabitation of the northern hemisphere, mostly in Asia. The evidence can be found from the negotiation between China and Russia on Chinese occupation of Siberia. With existing population of 1.4 billion people, the existing amount of cities, neighbourhoods, buildings and spaces becomes insufficient. Tundra and taiga become valuable since they occupy 35% of the world’s landmass. An enormous amount of natural resources, energy and freshwater supply which are essential for human habitation can also be found in the area.
with the buildings typology to create comfortable microclimate for both the indoor and outdoor spaces. Severe natural condition of Tundra and Taiga will be our testing ground for the system development. The possibilities of the adaptation through time to climatic changes and the feedback from the system would be also explored.
However, because of an extreme environmental condition in the selected area, it is necessary for a new urban morphology to be developed. The existing urban systems are not yet suitable for Arctic Tundra as the temperature in long lasting winter can go as low as -400C. Methane gas stored under the permafrost ground is released to the atmosphere as the ice cap thaws, accelerating the global warming effect. The design focus on the relationship between availability of natural resources and the urban growth pattern, together
13 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
1 CHAPTER
DOMAIN
Yakutsk
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DOMAIN
Discussion about the 4 degree world has caught the world attention, where the average surface temperature can increase up from 4 degree to 16 degree celsius in next few decades. Consequences to the change in temperature, the biomes is predicted to be shifting up north causing migration in plants and animals species. At the northern part of the earth, tundra and taiga, permafrost ground condition exist. Underneath the layers of permafrost, methane gas is freezed and slowly release into the atmosphere every summer. Thawing ground condition moisten the soil surface creating an unstable ground condition also known as wetland. In the past this phenomena had causes a whole city to collapse. This raises an interesting question of how architecture can accommodate this changes. The study of alternative lightweight floating constructions, such as pneumatics and tensairity can be explored in order to respond to the structural approach.
17 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
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SELF SUSTAINABLE HABITAT
“ Sustainable development, sustainable growth, and sustainable use have been used interchangeably, as if their meanings were the same. They are not. Sustainable growth is a contradiction in terms: nothing physical can grow indefinitely. Sustainable use, is only applicable to renewable resources. Sustainable development is used in this strategy to mean: improving the quality of human life whilst living within the carrying capacity of the ecosystems �1
As it was described in the quotation above, the sustainable use may appear only when resources (water, energy, food, economical basis) are renewable.
A sustainable habitat in this case is an ecosystem that produces food and shelter for people and other organisms, without resource depletion and in such a way that no external waste is produced. Latest definitions of self-sustainable habitat also include the availability of water, energy and economy resources for the settlement (Figure 1). Some of the resources may evolve naturally or be produced under influence of a man.
The main intention of the project is, based on the environmental parameters of chosen ecosystem, calibrate the use and production of resources, and create a model of self-sustainable settlement for the area.
Siberian tundra and northern taiga areas become very attractive for the water and energy renewable sources, as vast amount of energy and fresh water are stored there under condition of permafrost which would be explored further in this chapter.
The use of resources has to be also intelligent, where the amount of resources that are consumed is less or equal to the amount of resources that are collected.
19 On the left page: Figure 1. Definition of Sustainable habitat
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
UNOCCUPIED BIOMES
Figure 1. Map of Biomes
Due to demographic pressure, lands become more valuable than ever before. Large empty land mass are the target of our dissertation. There are two major unoccupied biomes, where both of them has its own reason why inhabitation is undesirable (Figure 1). One of them is desert where the lack of water and the elevated temperature can be a harsh environment for any life form. The other is arctic tundra, a vast land mass of permafrost, prove difficulty to build anything on top, and extremely low temperature. 2 According to the acceleration of global warming, desert temperature will continue to rise making it even more impossible to occupy. On the other hand as the temperature climbing upward tundra’s climate will become more suitable for human, plants and animal to survive. Tundra’s vast amount of land also hold a
large methane deposits under the frozen ground, also commonly known as permafrost. Methane gas can be harvested and used by the settlement in the area creating self sustainable communities. In Tundra water can be easily collected from thaw of permafrost in Summer, whereas it can hardly be any in the desert. Even though tundra environment is well-known for it’s cold weather, limited number of sunlight hours and unstable soil condition, it possess all the resources that would satisfy every aspect of human basic needs, which include water, food, land and energy, making it an ideal unoccupied area to explore.
21 On the left: Figure 2. Definition of Sustainable habitat
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
40C WORLD :: TEMPERATURE SWITCH
There are many influencing factors that affect the increment of global temperature, where the amount of greenhouse gas and carbon-dioxide have the dominant role. The greenhouse gas emissions and concentrations have continued to rise with an increasing trend, causing the increment of carbon dioxide level in the atmosphere and leading to global warming of at least 40C by 2100 all over the globe (Figure 1). Current scientific evidence identifies the regions where the largest climate changes are projected. The Royal Society A run Earth model simulation, based on the high-end and low-end warming models, based
Figure 1: Predicted environmental change
22 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
both on the 4 0C and 2 0C world scenarios. “Observational data and models agree that the world is not warming up at a uniform rate across the globe, and this geographical variation is going to continue.� 3 The results are slightly different for each scenario, but comparable. In both high-end and low-end warming models the simulation suggest that the largest increment of temperature occur over Canada and Northern half of Asia at a rate of about 40C per 10C of global warming which is equivalent to 160C by 2100. This
large temperature increment at high northern latitudes is caused by the thaw of ice and reduced winter snow coverage, which will absorb large amounts of incoming
Figure 2: Non-uniform warming
solar radiation. (Figure 2)
�Models that simulate a large amount of snow and ice for the present-day climate are likely to simulate a large degree of warming in this region as the ice retreats. Models that simulate lower ice coverage in the Arctic will produce a smaller amount of warming� 4
major floods in many regions, the ranges of the seasonal differences in temperature would rise significantly, the ecosystems and human systems would experience a switch which requires for certain adaptation qualities.
The scientists predict that a world in which warming reaches 40C above preindustrial level would be one of the unprecedented heat waves, severe drought, and
23 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
Figure 1. Precipitation switch
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40C WORLD :: WATER LEVEL & NATURAL DISASTER
First thing that will be highly affected by the increment of global temperature is the change in precipitation rates and amount. As projected, Sub-Arctic and tundra zone precipitation will be increase by up to 90% in 2100, while many region along the equator line precipitation rates will decrease by more than 66%, which can lead some countries to eventually turns into dry arid areas.5 (Figure 1) Countries such as India and Mexico that are rich in maize and coffee agriculture, will become extremely dry making it difficult for plants to grow. Unlike humans, plants have a very small growing temperature range. At the time that the global temperature range shifts up, it will become easier for plants species to grow in northern regions.
For animals choice of area to live, availability of food and right temperatures are considered as the main elements. Naturally, animals, such as swans, migrate up North in summer to find a suitable range for reproductions, and those living the area mostly hibernate. It is predicted that almost all types of land animals, such as sheep, moose and others, will gradually shift upwards and those which has to end up meeting the coast will die out. This happen because, same as plants, animals have a small growing temperature range regardless the evolution process which is very slow.
that the water will raise equally. Gravitational force varies in range of 9.77 to 9.82 where the strongest gravitational force goes to the highest and lowest latitudes, known as North Pole and South Pole. 5 On the other hand, gravitational force weakens along the equator line. Therefore, countries along the tropics of equator zones are most likely to be the first affected by this phenomena. The rise in sea level does not only cause flooding possibilities, but affects the changes in water and air. Current scientific evidence proves that those phenomenas can lead to a storm like tornadoes, and more frequent cyclones occurrence. In such zones animals and plants will hardly survive, what would lead to a massive migrations of the species towards the North.
Contrary to the equator zone, with the changes in global mean temperature and shift in biomes, taiga will become inhabitable for both plants and animals. Metal ore such as Gold, Silver, Platinum and Copper can be found in the area. The amount of energy resources, such as Methane gas, that is available on site are more than enough to sustain a city.
Consequences to the melting of the Arctic ice cap when the temperature rises, huge volume of water dispersed around the globe. However, it does not mean
25 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PERMAFROST :: CONTINUOUS / DISCONTINUOUS
During the last 775,000 years, thousands meters deep permafrost was built up layer by layer. Throughout the time, plants grow and die in the area according to the seasonal changes. In summer, plants grow as the environmental conditions are in suitable range, and they die in winter when the ground freeze and no longer provide water or limit the roots movement. In winter, snow builds up on the ground surface becoming a good soil insulation. Once the ground temperature gets above zero again, the melting snow penetrates into small gaps between the rocks and soil elements. This cycle is then repeat, thickening the ice underground.
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Every summer, only certain depth of the ice compound underground turns into liquid state. This happen because the heat transmittance gradient from the Earth surface down is not sufficient. The permafrost ground is so thick that the heat can affect only a small part that is close to the surface. This part is known as active layer of permafrost. The dead plants buried underneath new ground surface decompose and produce methane as an as by product, which then is stored as bubbles penetrating in between ice layers. Approximately 7% of the Methane gas is stored in the total ground volume, which in total of more
Figure 1: Continuity of permafrost in the regions
than 8,307 billions m3 within that area.6 This huge amount of gas can translate into a form of energy, amount of which is considered to be the largest in comparison to other energy resources. As if this amount of Methane is released into the atmosphere, contributing to the global warming effects, the world mean temperature is predicted to increase 4 times faster.
permafrost also take over vast amount of land in the world where the water underground freeze and melts with the change in temperature. In some areas with all time air temperature below 00 degree (Canada, Siberian tundra), permafrost rarely melts as it is always insulated by a snow layer. This is where continuous permafrost can be found. (Figure 1)
Permafrost can be found all over the world but different in the depth and amount of it. Places where the least amount of permafrost is found is around Southern Siberia, so called isolated permafrost. Discontinuous
27 On the left: Continuity of permafrost in the regions
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
FROST HEAVING :: GROUND PATTERN, FREEZING PROCESS Ground condition is one of the major elements contributed in making changes on the site as the underground ice melts. Frost Heaving7, ground condition phenomena, creates pattern ground in the area. This phenomena builds up more than 50,000 years ago or up to 799,000 years in some area. As the ground temperature came down below freezing point yearly in winter, water molecules freeze starting from near ground surface. As the underground temperature gradient top down the earth surface, along the freezing state, loads of the soil creates down pressure due to the gravity and pushes the water underground up to the surface. As the liquid state water goes up and meet the freezing ice on the higher level, the water molecule join and freeze immediately. The freezing process is limited and became more difficult because of the soil layers, therefore, the ice grows in horizontal lens shape and pushes the soil away to the side. This cause the ground surface to have circular pattern.
Lithalsa. That creates gaps in the ice-lens. As snow that is built up on top thaws in summer, clean water from snow fill those small cracks in the soil elements. When winter comes again, the water in between the cracks expand and breaks soil elements into smaller pieces. The smaller pieces sink and again the frost heaving process looped pushing big soil elements and rocks to the surface.
As the ice becomes fully frozen, it contracts since the water contains chemicals such as Benzene and
28 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
On the top: Frost heaving ground patterns
SOIL QUALITY :: CHANGE IN SOIL CONDITION OVERTIME Over a period of time, ground condition changes, the repetition of Frost Heaving process together with the increase in global mean temperature alter the soil composites. As the soil component keep breaking down, smaller pieces go down the bottom and became sediment settling at the bottom. At the bottom soil level, sediments are mixed with the decomposed plants and animals which after a period of time turn into nutrients such as nitrogen and phosphorus within the water body. Increasing in the thawing rates will only make the ground condition boggy and swamp. To the point where the land turns into wetland state, lake will have excess amount of nutrients so called Eutrophic Climax. This is a condition when many more species of plants and animals can grow very fast, creating a perfect food chain in the area. Some oxygenating plants produced oxygen in the water creating comfortable condition for fish to grow, they then became a food for higher level predator in the food chain.
10 degree Celsius) is a perfect condition for over 30 species of herbs to grow. Herbs farming can be main agricultural activities in the area, with the benefits of curing people as well.
Fish and other land animals such as Reindeers and
More than 600,000 seeds found under 20-40 meters of
Mousse can became major supply for people in the area. Moreover, with a warmer environmental condition and the wetlands ground, (if lower temp can maintain
Plants that commonly grow in this soil condition includes Kachuripana which is a food source for fish and helps producing oxygen, Mutha which acts as an insect repellants, Salpani which can detoxified blood from snakes or scorpions bite, and Thankuni the fast growing vegetables. All of these herbs can grow in very cold climate and accelerate the growth starting at 4 degree Celsius. Therefore, more animals in the area in consequential. Institute of Cell Biophysics at the Russian Academy8 proved that Silene Stenophylla9, buried underground more than 50,000 years ago by ground squirrels, now can be revived.
permafrost can re-germinate at 100% success rate if the ground is no longer frozen. The seeds are full of sucrose and attract a lot of interest from scientific community.
29 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
WETLAND
Distribution and properties of permafrost will be changed in response to climatic warming. “Arctic soils contain approximately 455 GT C, or 14% of global soil carbon, of which about 50 GT C are accumulated in wetlands� 10 A few ecosystems in Arctic, including wetlands, convert part of carbon that was photosynthetically captured from the atmosphere as CO2 to methane, which is further released as a product of soil decomposition. Being under frozen condition, the wetlands below the depth of seasonal thawing are currently not involved in the carbon cycle. Though, under the scenario of warmer climatic conditions, the depth of seasonal thawing is going to increase, and the amount of generated and released methane will rise accordingly. Apart from the carbon emissions, the ground condition itself is changing in concordance with the seasonal thawing of active layer. Due to unequal distribution of permafrost, the thawing is regionally dependant, which leads to the changes is ground contour and water concentration
within the soil throughout a year. O A Anisimov investigated the permafrost model forced by several climatic scenarios to construct the projections of the ground temperature and depth of seasonal thawing. Soil thermal properties were calculated using parameterizations that take into account soil type, soil moisture and ground ice content over the area of NorthEastern Eurasian permafrost region. The results predict 10%-15% increase in depth of seasonal thawing over the most of permafrost area by 2025, and over 30% in the end of the century. An important feature in the pattern of seasonal thawing over the whole century scale predicts the larger increase in the active layer thickness in the northernmost locations along the arctic coast and in East Siberia, where wetlands are widespread.
Fraction of land area occupied by wetlands in Russian permafrost region.
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On this page: Figure 1. Fraction of land areas occupied by wetlands in Russian permafrost redion On the right: Generic wetland of Northern taiga - tundra areas.
Figure 1.Tundra landscape, Summer
32 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
PLANTS AND ANIMAL
Taiga Biome, boreal forest as the other name, is classified by type of trees that survive in the certain environmental conditions which is mostly glaciated through out the year. Types of plants that survive throughout the whole year are furs, spruces, and pines where larch, tamarack, and birch only grow in summer. These trees have characteristics of having such a small needle leaf to minimized the amount of water loss and dark color the absorb as much sunlight as they can. Evergreen spruce such as Picea, pine such as Pinus, and fur Abies are mostly found with roots no deeper than 30 inches (0.76 meters). 11
extra nutrients while feeding in summer, and then using them in winter, while sleeping. This system helps them use a limited amount of energy during the time when the ground is frozen, the conditions are difficult to survive and the food supply is constrained.
Consequences to the rich in vegetation resources and perfect environmental condition in summer, animals from both lower and higher latitudes, including swans and many insect species, migrates to the site for food and reproduction. Animals that can be found throughout the whole year such as Mousse, Reindeer, Polar bears, Ground Squirrels, Rabbits, Wolverines, and Bobcat. These animals have adaptation ability to survive in such a cold climate in winter, whereas, all of them cover with furs for insulation and unique eating habit where some of them hibernate in winter. “Hibernation is a state of inactivity and metabolic depression in endotherms. Hibernation refers to a season of heterothermy that is characterized by low body temperature, slow breathing and heart rate, and low metabolic rate� 12. The hibernating northern tundra animals are storing
33 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
WETLAND SPECIES
Figure 1. Animals in the area
In Sub-Arctic region, Taiga13, where the site is located, moose and reindeers are highly populated. Reindeers, from an ancient history have been hunted by humans and became economically sold in most of the markets. Moose, wild animals with population of more than 730,000 can be found in Russia or more than a million around the world. Reproduction season is once a year every September and it only take 8 months for the gestation. The baby then grow to the consumable size in 9 months and live up to 25 years. Moose farm now became more famous as almost every part of the moose is useful as a life stock. The meat itself is low fat and tasty. Milk from moose also has medication properties as it can be use to treat peptic ulcers, moreover, they are rich in nutrients and low lactose and often use to make cheese. Moose consumed all the plants on the ground including grasses and aquatic vegetation from adjacent forest, this reduces the cost of feeding in the farm. As they are already adapted to the environment, there is no need to provide any aditional shelters. (Figure 1)
As was explained earlier in the chapter, a large amount of plant species can be grown in the new emerging wetland condition. The plants can be both used as food and also as a medication, giving a strong economical background for any settlement that can be developed in the area. (Figure 2) To transform raw herbs into medication products14 a specific procedures are required for different type plants, since different types of plants contain useful chemicals available in different organs. There are common 9 methods of extraction of medicinal plants including Maceration, Infusion, Digestion, Decoction, Percolation, Hot Continuous Extraction, Fermentation, and Counter-current Extraction. The product from stated process is consumable medicine that can be both in liquid and capsules. To export raw medicinal plants, around 20% of the plantation area is required during the harvesting period for collection and storage.
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On the right: Figure 2. Plants species in the area
35 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CONCLUSIONS OF ENVIRONMENTAL PART OF DOMAIN.
Current research corroborates the fact that Tundra area has a great potential for human invasion. Moreover, with the ongoing global warming phenomena it becomes one of the only areas, that is sufficient for people to live in, while others become almost uninhabitable. The resources that are stored under condition of frozen ground, are becoming available through time and attract a lot of scientific interest. Methane gas that is stored all over Siberian tundra and taiga areas results into a largest amount of possible fuel in the world, while thawing water turns up to be the largest freshwater supply, that is crucial for plants, animals species and human survival. The condition of wetland for further agricultural cultivation also seams to be promising. However, having all the potential for the nearest future, the area is still absolutely uninhabited. Current conditions of the zone are making it very difficult to survive, having the air temperature of the zone that can
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go down up to -460C with a very high percentage of humidity, as it can be seen on comparison graphs of London and Oymyakon on the Figure1. Mostly people were invading Siberia for the resources it has (fossil fuel, metal ore, wood). However, as soon as the resource was depleted, the cities were dyeing out as fast as they emerged. Current investigation aims to understand how to create a sufficient city that would be capable of sustaining itself with local resources and deal with the difficult conditions to create a comfortable stable inhabitation settlement for people, that could grow and develop through time.
Maritime Continental CLIMATE: CITY:
Tundra
Cyclonic
CLIMATE:
London
POPULATION:
CITY:
8,173,194 inhabitants
POPULATION:
Polar
Oymyakon
472 inhabitants
100
80
60
40
20
0
-20
-40
-60
jan
feb
march
22 9
may
june
c
-16
july
68
12
o
3
apr
c
o
-46
49
38
aug
sep
oct
96
45
mm
18
mm
5
nov
92
89
dec
79
%
71
%
59
Figure 1. Conditions comparisons, London - Oymyakon
37 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
Inuvik city
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ARCHITECTURE ON PERMAFROST
Investigating the architecture within the environment of chosen area, structure and ground conditions are the first topics that arise. Permafrost ground condition has caused many infrastructure failure in the past year. Any type of foundation relies on the ground condition, which has to be analyzed, the possible shifts have to be predicted and calculated before any type of construction is started. Those calculations can never be absolutely accurate, especially taking in consideration the amount of frozen water (28%) within. The global warming phenomena makes this situation even more complex, as the uneven thawing of soil makes any kind of predictions hardly achievable. Some cases has been carried out as an examples of why hard engineer construction is not suitable for this type of ground condition. (Figure 1) In an area such as Inuvik, a city on the permafrost ground was built with hard engineering construction. Foundation piles are placed under buildings with
the insulating technologies designed to keep the ice underground all-time frozen. As the world average ground temperature increased, the whole city which was sitting on insulating foundation collapsed as they failed to work. “Inuvik is considered a hot spot. Their number is set as high as $121 million for buildings alone, pretty significant when you think of a town of 3,500.� 15 Nevertheless, this type of construction is still being implemented. Qinghai-Tibet Railway, similar type of construction applied to the foundation of the train track. Each foundation pile is punched underground with the insulating platform on top to cool down ice under the train track in winter, as the heat from the train is transferred to the ground and to keep underground water freeze in summer. However, active layer of the permafrost increase in depth every year. Long-term operating wise, the foundation will reach unstable condition as the ground temperature will naturally rise dramatically until the underground water never freeze again.
Figure 1. Current foundation techniques
39 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PERMAFROST MUSEUM :: YAKUTSK
There are very few construction proposals on permafrost due to the difficulty of construction techniques. Mammoth and Permafrost Museum16 project won a competition to be build in Yakutsk, Siberia with the construction idea of having the least amount of contact point to the ground for minimized heat transfer from the building. The building sits on pointing legs 20 feet above the ground, very small contact area to the ground make it laborious for heat to transfer as permafrost is thermally sensitive. (Figure 1).
Additionally, the ground will never thaw at the same rate throughout the whole ground surface as the angle and leveling vary the ability of the solar heat absorption. Therefore, it is important to have the structure flexible enough to accommodate small movement generated by the thawing soil.
However, under 4 degree world condition, permafrost
Nonetheless, there is no found evidence of specific
will no longer be able to bare any loads as the surface will become a wetland. With the pointing legs structure which sits on leveling ground contour, the whole building is more likely to collapse.
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It also seams like the architects thought about the landscape reconsideration of the Site that can be seen on the site plan for the competition (Figure 2).
proposal explained nor in design, nor in documentation provided.
On this page: Figure 1. Permafrost Museum, Leesar Architects. Section On the right: Figure 2. Same project. General plan.
LIGHTWEIGHT CONSTRUCTION :: PNEUMATIC & TENSAIRITY
Pneumatic construction is a lightweight construction technique which can be used at any scale. The system simply works with air-pressure pumped inside the envelope. Low-pressure system and High-pressure system are being used for different purposes. Lowpressure system requires only small amount of energy, such that the air that is being pumped inside is only 0.1 Bar or equal to atmospheric air pressure. This system is popularly used for light weight, self-standing structures.
Similarly to how the truss works, compression elements on top and tension elements on the bottom part are making the whole envelope stiff. Its been experimented that the tube shape tensairity system of 8 meters span can hold a weight of a car of approximately 3 tones.
The pneumatic system can be applied to any form with circular sections. Structurally it can support heavy loads
Since thermal heat and the soft ground condition are crucial for construction on permafrost, hard engineer solutions are not suitable and not reliable for long-term use. As predicted, as soon as the ice underground thaws, 7% of Methane gas is going to be released into the atmosphere and part of unfrozen water will drain.
Taking pneumatic further for more advance uses, tensairity17 system was introduced (Figure 1, 2). This is the system when tension elements are integrated to create strength to the spanning pneumatic envelope and add compression elements on the opposite side.
To accommodate this changes, the structure must have ability to allow small movement to occur. Moreover, the construction has to be lightweight and floating with wide contact area to the ground to avoid sinking. For people accommodation and creation of comfortable microclimate, insulation is one of a major concerns.
depending on the way of how it is being implemented. Moreover, because of the air layer inside the envelope, this system acts as a very good insulation.
With the loss of such an amount of volume, ground will be sinking unequally throughout the Earth surface.
AIRBEAM fill with pressurised air
compression element
tensioning element
On this page: Figure 1. Tensairity mechanism On the left: Figure 2. Tensairity implementation in the cold climatic conditions
compression element
tensioning element
43 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
LIGHTWEIGHT CONSTRUCTION :: PNEUMATIC & TENSAIRITY
Looking more into the house structure development the same goals need to be taken into account: light weight structures, local materials, high insulation qualities. As a reference was chosen a project of Shige ru Ban architects18, that is now being under the process of construction. It uses wood as the main structural material and ETFE pillows for house insulation. The technique of wood crossing create a stiff self standing structure that is
44 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
capable of bearing loads. It also creates the ‘frames‘ that act as a ETFE pneumatic pillow contouring element. Pillows are made in the shape of rhombus, where every section of it is circular. That means that the pillow wouldn’t buckle and fail. Pillows can be inflated according to the temperature and insulation needed.
Figure 1: Earth forests
The wood is essential in this case as the chosen area has enormous lands occupied by Coniferous boreal forest 19(Figure 1). It means that the resource of wood is almost renewable, if is used and treated in a correct way. The type of wood which can be found in coniferous forest is mostly soft wood of Pines, Spruces and Larches. Pines are among the most commercially important of tree species, valued for their timber and wood pulp throughout the world. They are fast-growing softwoods that will grow in relatively dense stands, their acidic decaying needles inhibiting the sprouting of competing
hardwoods. Commercial pines are grown in plantations for timber that is denser, more resinous, and therefore more durable than spruce (Picea), which also can be found in the region. Considering the fact that pine is a soft wood, it is still widely used in construction. As it is not a dry type of wood, it is also much more resistant to fire then other species.
45 On the left: Shigeru Ban Architects. New building for Omega design
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
ENERGY HARVESTING :: METHANE
Methane gas is a natural gas, which relative abundance make it an attractive fuel. It is also considered as a green gas and when it is released into the atmosphere, it contributes to the global warming 4 time more than the same amount of CO2 by affecting the degradation of ozone layer.
However, because methane is a gas at normal condition when being released, it is difficult to capture and to transport it from its source. Methane Transportation and Storage Methane, similar to every Earth’s substrate, can be stored in 3 different states: gas, liquid, solid (methane hydrate). The most used nowadays is the liquefied methane storage, due to the amount of the gas that can be conserved. Liquefied state of methane contains in 1 cubic meter 600Nm3 of natural gas while the Gas
The large amount of methane is stored under condition of permafrost in Arctic regions in the ocean floors and the high latitude tundra Earth crust. Current scientific evidence 20 identifies that the amount of methane within the frozen ground of the East Siberia is about 20,000 trillion cubic meters, or about 700,000 Tcf21. That amount of stored energy is considered to be the largest source comparing to any others that are used in present (fossil fuels).
Hydrate contains only 170Nm3 of gas and 0.8m3 of water. On the other hand, to produce and maintain the gas in
Natural Gas Hydrate (NGH)
Liquifield Natural Gas (LNG)
Modes of transport and storage
Solid
Liquid
Temperature to be maintained
-200C
-1620C
0.85 ~ 0.95
0.42 ~ 0.47
Natural Gas: about 170Nm3 Water: 0.8m3
Natural Gas: 600Nm3
Gravity Contains in 1m3
46 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Figure 1: Methane hydrate moleculas
hydrate state is much easier, more energy efficient and safe.
When the methane is finally needed to be used, it can be released by letting it warm to temperature over 00C.
To convert the state of Methane from gas to solid it is necessary to mix the molecules of it with water under pressure, freeze and maintain it under the temperature of -200C (Figure 1). After that the snow-like hydrate can be packed into cubes and stored the refrigerated containers within the same cold -200C environment. That temperature is far easier and cheaper to manage than the -1620C required for LNG (Liquefied Natural Gas) conversion and storage. At the same time, the leakage or other damage in LNG container would result in instant methane vaporization and explosion. Methane hydrates - so called burning ice - can burn, but under condition of rising temperature, would thaw and release the gas slowly enough and therefore it is not instantly explosive.
The reason why the Liquified form of Natura gas is still more used is that mainly it is used for transportation22, not for on-Site use. Economically in terms of shipping and transportation use of liquefied state is much more reasonable, as the weight of the same amount of Methane differs significantly for the hydrate and liquefied states. (7.5 times greater displacement). Though, since this project is not primarily dealing with exportation, but aims to self sustain a city, where the Methane is collected, transportation is less relevant than safety aspects and the energy usage for conversion of methane gas from one state into another.
47 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
GAS DISTRIBUTION AND CONVERSION NETWORKS
The largest amount of natural gas is used on exportation. Gas, used for regional uses is transported through the pipeline network, which is called Gas trunkline. This is a pipeline designed for natural gas transmission from production areas to consumption points and is the main means of long-distance gas transmission. Gas distribution network has its own hierarchy, based on the amount of pressure used within. There are low (up to 0.05 MPa), medium (0.05 to 0.3 MPa) and high (0.3 to 0.6 and 0.6 to 1.2 MPa) pressure gas distribution network pipelines. Source type and configuration of a gas distribution network are determined by gas consumption volumes, structure and housing density, etc. Natural gas flows into high pressure gas distribution network from the gas trunkline (main hub) through the gas distribution station and into the medium and low pressure distribution network through gas distribution
points (sub-hubs). The convertion of gas to another form of energy (electricity) can happen as in the main hub, and in sub hubs, according to the distribution network pattern. There are the following gas pipelines of gas distribution systems classified according to their function (Figure 1) : • •
•
• •
gas trunk-lines (town and inter-settlement) laid to the main gas distribution point; distribution pipelines (street-level, intra-block, interplant and others) laid from gas distribution points to customer service lines; service lines laid from the connection with the distribution pipeline to the shutdown device at the building in-feed; entrance gas pipeline – from the shutdown device; internal gas pipelines laid from the gas pipeline in-
Natural gas Electricity Figure 1. Conventional distribution of natural gas
48 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
a. Dead end
a. Distributed Natural gas Electricity
feed to the connection with the gas appliances. Until recently, industrial natural gas-fired turbines operated on the same concept as the larger centralized gas turbine generators. The emerging network of energy distribution usually had a character of dead-end network (Figure1, Figure 2a), supplying the housing units through different levels of hierarchy of pipelines from the mane spine and singular power plant. This system is not considered to be safe enough as in case of failure of a hub or connection in higher hierarchy, all the supplied units of lower hierarchy will fail as well. With technological advancements, there is a trend towards what is known as ‘distributed generation’. Distributed generation refers to the placement of individual, smaller sized gas trunk and electric generation units (Figure 2b). These small scale power plants, which are primarily powered by natural gas, operate with small gas turbine or combustion engine units, or natural gas fuel cells. Part of gas is transformed into the form of electricity, other is kept as gas. Distributed generation
Figure 2. Ways of distribution of natural gas
can take many forms, from small, low output generators to larger, independent generators that supply enough electricity to power an entire factory. Distributed generation is attractive because it offers electricity that is more reliable, more efficient, and cheaper than purchasing power from a centralized utility. Distributed generation also allows for increased local control over the energy supply, and cuts down on energy losses during transmission. Working together the distributed generators compose a network of hubs that can support each other in case of individual failure. For conversion of gas into another form of energy (electricity) in distributed generators system are used industrial turbines. They are located only in close proximity to where electricity is supposed to be used, are compact, light weight and simple to operate. The heat which would usually be lost during the process of electricity generation can easily be harnessed to use it for powering boiler or heating spaces. This increase the energy efficiency of the total system.
49 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CONCLUSIONS FOR THE DOMAIN CHAPTER
Current research, as was already mentioned, characterize Tundra and Northern taiga areas of East Siberia as a zone that potentially is very likely for human invasion. Unlikely, current construction techniques are failing under conditions that are present on site. Though, with the ongoing researches in advanced construction techniques there is a possibility of reconsideration the reality of inhabitation the Siberian tundra area. The available resources seam to provide a perfect coupling background for this experiment. Current investigation aims to understand how to create a sufficient city that would be capable of sustaining itself with local resources and deal with the difficult conditions to create a comfortable stable inhabitation settlement for people, that could grow and develop through time.
50 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
On the right: Burning methane released from the ground. Russian Siberia
2
CHAPTER
METHODS
54 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
METHODS
Current chapter will describe the methodology of the process used for the design development and tools, that were studied, analyzed, some of them rewritten and all calibrated.
working in our system.
Each of the described tools in the Chapter (CA, GA, Circle packing, Delaunay, Sonic, AI etc), were used in certain sequence which can be observed on the methods flowchart. In the end of explanation of each tool is briefly mentioned the way of how it is used in order to contribute to our system. Further on, in Research and Design development, specific rules and evaluation strategies are introduced and documented in the way they are
55 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
METHODS FLOWCHART
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57 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
ENVIRONMENTAL ANALYSIS
SUNLIGHT
FLOW ON SURFACE. SONIC
Sunlight Analysis is done with the connection of Ecotect Analysis tool to Grasshopper environment. The specific position of sun is given with site coordinates extracted from Google Earth. The amount of direct and indirect sunlight hours are calculated for the terrain of the Site and then used as a two-dimentional informational bitmap, that assumes the rules of growth for the settlement and the time of Methane collection for harvesting system.
Sonic Plug-in 1 for Grasshopper calculates the flow of lines via the path of least resistance down a sloped surface. It requires a surface(topography) and certain amount of points above the surface, that are after used for each stream. In our system it is used for analysis of topography slopes and creation of hierarchy of artificial draining water channels for the city system.
58 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
On this page: Figure 1. Solar analysis tool On the right: Figure 2. Sonic flow surface.
59 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELLULAR AUTOMATA
Figure 1: Cellular automata example
Cellular automata2 is a computational algorithm developed from a study in mathematics. It composes of grid like cells, where each cell can be converted to different states, most often “dead” or “alive”. In order for a cell to change its state, it need to check the states of its neighbor cells and according to a certain set of rules. The states change will happen simultaneously throughout the board, and repeated each time on different generations. The cellular automata can run to an infinite number of generations, where initial the states of cells play a major parts in the result of all after generations. (Figure 1) There are many different default neighborhood type and they can be customize to any configuration. The job of
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the neighborhood is to look at the status of different cells (dark gray cells) in order to decide what will the center cell(red cell) status will be in the next generation. Status of the cell is not limited to dead or alive. There can be an infinite number of status or type on each cell and they can also have more than one status at the same time, depending on the rules set. The difficulty that arise from using cellular is how to come up with a rules et in order to generate a sensible outcome. The cellular automata can be explain through a famous ruleset, Conway’s Game of Life. It’s a two-dimensional cellular automata where the state of an updated cell is according to the previous state of its neighbor. The neighborhood system used in Conway’s Game of Life
is Moore neighborhood. The updated cells will replace the previous cells generating changes in patterns of the grid. (Figure 2)
Different pattern can be seen throughout generations, there are several notable pattern that can occur in game of life, include still lives, oscillators and spaceship.
What is crucial in the Game of Life is the initial state that created by the user and the outcome is determined by these simple rules 1. Any live cell with fewer than two live neighbors dies, as if caused by under population. 2. Any live cell with two or three live neighbors lives on to the next generation. 3. Any live cell with more than three live left neighbors
Cellular Automata will be used as a calculator to simulate the growth of the settlement in the project, the rule set depending on the property of the different type of cell. Mainly it will be determining the area covered by each type of the cell, it’s location and relationship. There are 5 type of cells in the project which are harvester, distribution/storage unit, inhabitation, wetland agriculture, and livestock farm.
dies, as if by overcrowding. 4. Any dead cell with exactly three live neighbors becomes a live cell, as if by reproduction.
Figure 2: Neighborhood example
Von Neumann Neighborhood
Moore Neighborhood
Custom Neighborhood
61 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PNEUMATIC SYSTEM
Generally pneumatic system can be separated into two types, low-pressure and high-pressure pneumatic. (Figure 1). High-pressure system is usually use in piston and balloons where vast amount of energy is needed to compressed the air and pump into the envelope. Such as in the balloon with elastic envelope, approximately 10 Bar pressure is required for complete expansion. However, the high-pressure system is not always necessary for building structure purposes. Low-pressure is commonly used in many architectural projects as it requires low amount of energy and as well can be self-supporting structure. 0.1 Bar pressure or normal air pressure is normally used for structural system. In a very big scale, it is necessary to have the envelope shaped in circular sections. This is required as the air is spread in circular directions and pressure then
distributed equally on the inner envelope surface. As if the envelope is not perfectly fabricated, wrinkles along the edge can appear causing weakness in the structural strength. At architectural scale, an example of huge dome structures, two type of low-pressure pneumatic systems can be implemented. One layer pneumatic structures, most often used for temporary shelters, are when the air is pumped inside at all time circulating the interior space at the same time keep pushing light fabric envelope up. Second type is with the two layers pneumatic structure. This is when closed envelope is formed with the air-gap in between them. The air-pressure inside the envelope filled in the pre-design form and hold the structure.
Figure 1: Pneumatic system examples
0.1BAR PRESSURE
1 layer pneumatic structure
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0.1BAR PRESSURE
2 layer pneumatic structure
CIRCLE PACKING
Circle packing4 is one of the way geometry can be arranged, where circles are packed together within a certain boundary or surface in such way that overlapping of circles would never happen. Uniformed circle or in various size can be used in circle packing algorithm defining different density. It can be used as a tool to distribute multiple points of interest, equally or in certain pattern depending on the radius of each circle. (Figure2)
Figure 2: Circle packing example
63 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
DELAUNAY TRIANGULATION AND VORONOI CELLS
DELAUNAY TRIANGULATION
VORONOI CELLS
In mathematics and computational geometry, a Delaunay triangulation5 for a set P of points in a plane is a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P). Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation; they tend to avoid skinny triangles (Figure 1). This is crucial for the further investigation of three-dimensional pneumatic structures used in our system.
Voronoi diagram6 is a way of dividing space into a number of regions. A set of points (called seeds, sites, or generators) is specified beforehand and for each seed there will be a corresponding region consisting of all points closer to that seed than to any other. The regions are called Voronoi cells. It is dual to the Delaunay triangulation. (Figure 2)
Another application is the Euclidean minimum spanning tree, that is explained on the next page. Euclidean minimum spanning tree of a set of points is a subset of the Delaunay triangulation of the same points, and this will be exploited to compute it efficiently.
Figure 1: Delaunay triangulation
64 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
For our dissertation Voronoi cells are used to determine the working area of each storage collector point, that is collecting the energy from harvester elements of the system. In this case each storage point can be studied separately, and understanding of energy balances within the system can be achieved.
Figure 1: Voronoi diagram
MINIMUM SPANNING TREE AND DIRECTNESS RATIO
Amount of detour one need to measure from one point to the other is crucial in determining the connectivity of a network. Directness ratio is a comparison between a virtual path between two points to an actual path that one have to take. For example, the road from one point to the other is two kilometers but the actual distance between two points is one kilometer, the directness ratio in this case is 2.0. The ratio cannot be less than 1.
to distribution unit and to houses or other energy consumption unit. However, methane gas property is not totally the same as electricity where it take time and pressure to transfer over a distance. So the solution would be the combination of minimum spanning tree and directness ratio to create a network for efficient methane distribution.
MINIMUM SPANNING TREE Minimum Spanning tree7 is simply used to determine the way of vertices connection in two or three dimensional space with a shortest spanning of connections. (Figure 3) It is commonly used for electrical grid design to reduce the cost of electrical wire installation. In our case the minimum spanning tree will be used to generate methane gas pipeline system spanning from harvesters
Figure 3: Minimum spanning tree
H4
H3 45
H1
40
H6
H1
H2 30
100
40
H9
45
H7
50
H8
15
75 25
H2 30
80
H10
H6
75
20
H5 90
40
30
75 25
H4 45
45
75
30 20
H10
H3
45
100
40
H5 90
H9
45
80
H7
50
H8
15
65 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
GENETIC ALGORITHMS AND WEIGHTING MECHANISM
Complex forms and systems emerge in nature from evolutionary processes, and their proprieties are developed gradually through the processes that operate on successive versions of genome and phenome, taking into consideration the variety of mutations, recombinations, natural selection and survival of the fittest through generations.
Galapagos for Grasshopper, Rhino, 2010). Governed by the algorithms, that mimic the biological evolution, they usually involve the techniques such as reproduction, recombination, natural selection, survival of the fittest. Recombination and random mutations provide the diversity in populations, selection, based on the fitness criteria, acts as a factor increasing quality.
“A compelling goal is to instrumentalise the natural processes of evolution and growth, to model essential features of emergence and then combine these within a computational framework�
For more complex systems, that are closer to natural selection process, several fitness criteria can be introduced. The importance and priority of each can be ascertained in the algorithm by defining the weight of each fitness criteria, introducing a weighting system to the algorithm. (Figure 1)
In the computational environment the logic of the genetic algorithm is extracted, simplified, and transformed into a set of mathematical principles and bases of operations, becoming a tool used for generating designs, evolving forms and structures in morphogenetic processes. The tools and softwares, based on genetic engines, are developing dramatically fast (Genr8 for Maya, 2001,
Figure 1: Weighting system
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In this dissertation genetic algorithms with and without differential weighting impeded in the system are used for positioning of the networks, work with connectivity and definition for typologies optimization
3 CHAPTER
RESEARCH DEVELOPMENT
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RESEARCH DEVELOPMENT
In research development, we focus on a selected site which is around 10 kilometers away from Yakutsk, Russia. (Figure 3) Local permafrost is composed of four main components which are organic matters, ice, carbon dioxide, and methane1. Methane gas is a great energy resource that can be harvested from the permafrost. Therefore it is essential to select a site which has the highest amount of methane deposit. At the site, deepest permafrost sediment can be found, comparing to any other place in Siberian taiga or tundra. Investigating into environmental conditions of the Site it can be observed that it has a high temperature swing between 24oC in summer and can drop down to -39oC
On the left: Figure 1. Active layer of permafrost shift Figure 2. Environmental conditions of the Site On this page: Figure 3. Location of the Site
in winter2. This is why it is essential for the project to bring up the topic of comfortable microclimate for people to survive there. The light, wind speed and snow accumulation have to be considered as well. (Figure 2) In summer, once the active layer boundary begins to shift downward because of the average temperature change, organic matters and ice thaw. (Figure 1) Trapped in the ice methane, being lighter than air3, raise into the atmosphere. Released into the air methane would contribute to global warming significantly4, unless it is collected before. If collected, then it could be used very efficiently as the local energy source5.
71 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
WETLAND 4C WORLD RATES GROUND CONDITIONS CAUSED
Further on, chosen Site was checked for the rates of natural thawing of peat to indicate the correct ground condition of the area and the total amount of methane that would be released.
The hard engineering construction is hardly believable to work in this area7, where ground condition varies from the absolutely frozen to a swamp, and the level of it shifts unequally through time and is unlikely predictable.
As can be seen on the monthly diagram the ground is completely frozen for the months of January to the end of April, and is thawing during the other 7 months6.
A different system should be explored, that would answer the question of stability, and would be less affected by the ground movements and changes.
The depth of thawing ground can reach up to 0.25 meters depth per season, that is considered as an active layer of permafrost. (Figure 1) Only during this time, frozen methane is released and, as was explained earlier, can be possibly harvested for energy usage. If there can be found a certain way of thawed water drainage during this period and also the way to increase the ground temperature, than the speed of thawing rates would fasten dramatically. Otherwise, water would freeze again, and the speed of methane release would slow down until the active layer shifts significantly. (Figure 2)
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On the right: Figure 1.Rates of thawing peat worldwide Figure 2. Rates of thawing peat on Site, Monthly data
73 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
STRUCTURE light weight floatable structure stable structure
platform system
building morphology URBAN MOPHOLOGY
ENERGY capturing system storage system distribution system
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MICROCLIMATE heating system
insulating system building configuration adaptable system
AREAS OF INVESTIGATION
Intention of the following chapter is to develop, test and evaluate number of tools, that could strategically help with development of a new city-fabric, that could emerge through time. Since the characters of the area are very specific, and project is Site-oriented, it frames the main topics to deal with to determine new morphology: Energy, Structure, Climate. (Figure 1) This chapter is dealing with a preliminary design of a harvester and platform units, that would have the ability to stay on such a problematic ground condition, and with creation and evaluation of tools for the efficient functional distribution of cells, responding to each of the topics for the emerging settlement. System will be dynamically growing according to relationships inbetween cells, alike in Cellular Automata process.
The system is responding to seasons and considers the changes according to the time scale. In this case, harvester units are not going to collect any energy during the winter time, storages will use the hibernation principles to sustain the population for these periods. The modification of units is depending on the previous stage of units as well as on the conditional statements - environmental data - , which would also be explored in this chapter. To guarantee the efficiency of work of harvesting mechanism and energy movement, another level of complexity was added to the system. The analysis of ground contour for successful water drainage and the efficient network for energy distribution through the city are also explained in this chapter.
Created system is scenario based and always starts with initial set of parameters (further in this chapter will be explored and explained various scenarios for initial rules, its results, analysis, conclusions and evaluation). After the initial condition is set, system will be emerging through time, based on the calculations of the relationships in-between the input parameters (harvester-storage, storage-house, storage-pharmaceutical etc.)
75 On the left: Figure 1. Strategy diagram
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
MAIN EQUATION
For further investigation in a better use of materials, ETFE was selected according to the strength and durability which suit the purpose of the use for insulating lower spaces by trapping the solar heat. ETFE was used in many green house projects, for example The Eden Project in Cornwall, United Kingdom8. Due to the air pressure inside of the envelope it is stiff and at the same time really light weight. The heat can never be accurately calculated as there are many unreliable factors affecting the heat transfer. However, specific material properties are given for calculations. Calculations and math are the initial base for any step taken in this project. Since there is no systems created so far, that try to achieve the similar goal, there is no other way to assure the correct effect, but to rely on math. The permafrost ground condition consist of 28% freezing water9 that naturally thaws in summer when the soil absorbs the solar energy and the ground temperature
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increases. Active layer is predicted to grow in depth every year because of the global warming phenomena. First calculation was carried out to calculate the depth of the active layer in summer when the solar heat were absorbed by the permafrost ground without any additional construction. From average total of 350 Watt of solar radiation per meter square in Siberia, only 105 Watt is absorbed by the ground. More than half of the energy gone from the air convection and the reflection of the earth surface. With this normal condition, the ground will melts 8.01 meters per year. Then, the additional construction with ETFE pillow was implemented to understand how much more efficiently the solar heat energy is trapped. Thawing rates calculations were carried out with one layer of air-gap and two layers of air-gap in between the ETFE envelope to compare the heat trapping efficiency. Those experiments can be observed and compared in the following 3 pages.
77 Mian equation
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
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79 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
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CONCLUSIONS
For the situation without an aditional ETFE member and with the most efficient one implemented the thawing rates are 8.82 meters per year and 10.2 meters per year respectively. According to the calculations, 3 layers of ETFE is 27.29% more efficient than normal. The calculations are based on an assumption as if there is no convection in the air gap and no reflection in EFTE material, as if they are painted in black. The envelope is 5 mm thick sheet and has 0.5 meters distance between each layer. Additionally, global mean temperature is predicted increase dramatically in next 100 years, meaning that the depth of active layer will also increase through time.
81 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
UNITS PRIMARY DESIGN
From the calculation was concluded that three layers of ETFE pneumatic membranes will be used, as it is considered to be the most efficient for the speed increment of thawing rate of permafrost. However, those pneumatic membranes need a stable foundation to support them. Investigation into the existing foundation systems proved that they are not suitable be build over thawing ground, an alternative system will be needed. Light weight pneumatic foundation are introduced in the design as a structural elements where they will be
placed underneath the structural frame, as it is shown on the diagram. Structural frame will be working as a pedestrian connection within the whole system, and within the frame both the pneumatic ETFE pillow or a structural platform can be positioned. (Figure 1, 2, 3) In harvester units, local methane gas storage will be also placed connected to the frame to store excess methane in the system. As permafrost in particular area completely thaws the ETFE membrane can be deployed and replaced with a platform system. That allows extra infrastructure to construct over.
pneumatic support structure methane pocket
main frame structure
harvester pillows
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platform
On this page: Figure 1. Deployable units, frame and foundation Figure 2. Section of units On the right: Figure 3. Exploded diagram of the unit system
83 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CALCULATIONS:: MELTING RATES OF PERMAFROST, ENERGY PER YEAR
CALCULATION:
VALUES AND INFORMATION:
Pmax = 70,000 ppmv (part per mil. volume)
Pmax = Permafrost max = [ppmv]
1m3 =100cm * 100cm * 100cm = 1,000,000 cm3
Vmetre = Amount of CH4 per 1 meter depth = [m3/m] = [m2]
70,000cm3 of CH4 per 1,000,000 cm3 of permafrost.
TR = Thawing Rate = [m/year]
Vmetre = 70 m3 per meter depth over 1000m2 of surface.
Vtot = Amount of CH4 total = Vmetre* TR = [m3/year] CV = Calorific Value = [kJ/kg] Etot = Amount of total energy = Atot* CV = [mJ/year]
TR= 10.2 m/year Vtot = TR*Vmeter = 10.2m/y * 70m2 = 714m3/year of CH4 1moleCH4 = 16 gr = 22.4 litres = 0.022m2 [*ref] Vtot = (714/0.022)*16 = 519,672 gr = 520 kg/year Calorific Value = 55.500 kJ/kg Etot= 520*55.5 = 28,860mJ/year = 29 GJ/year
Ground Component
Maximum methane content was found in frozen soils of continental alas deposits and Eastern-Northern Siberia up to 70000 ppmv(part per million volumn)10 , that is equal to 7% of the soil content. (Figure 1) Taking in consideration the thawing rate of permafrost 10.2 meters per year, based on the calculation with use of harvester with 3 layers of ETFE film (page 69), the total amount of CH4 released per year can be calculated. (Figure 2) With the knowledge of Calorific Value of Methane, and the amount of it harvested, we get the total amount of energy that can be harvested during the year from meter square area of the ground. (equation on top)
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Carbon Dioxide Methane
7%
Water Content
5% 28%
60% Soil Contaminant
On this page: Figure 1. Ground component On the right: Figure 2. Active layer thawing rates per year Figure 3. Active layer thawing rates in general
North part of Canada
Green land
Chita, Russia
Magadan, Russia
Yakutsk, Russia
Vilyuysk, Russia
M. Scale 0 active layer line
active lay er line
acti v
e la yer lin
2013 2020
3.5 m3 CH4/m2 100
2043
300 22.5 m3 CH4/m2
400
500
http://science.jrank.org/pages/5107/Permafrost.html
5.2 m3 CH4/m2
200
e
http://www.academia.edu/3278961/Comparison_of_Methane_Content_in_Upper_Permafrost_of_Eastern_Siberia_and_Alaska
volume/area
POPULATION OVER SPACE
Figure 1: Population distribution
From the calculation of methane release rate, 1 meter square of area will release 0.7m3 of methane, that equals to 0.02 GigaJoules of energy per year. To sustain a person with only methane energy, an area approximately 1,300 m2 or 36.5 by 36.5 meters is required as one person need roughly 40 gigajoules per person (value will be carefully explained in design development). Since there will be many people living in the city, it is impossible to spread them out as shown in Figure 1. (1 person per 1,300 m2). So instead of having one person per required area, people can live together while having the same amount of harvester as supplier. The proportion between supplier and inhabitation are crucial in the growth pattern where too many harvesters can over exceed the system with methane gas, too much inhabitation would exhaust up all the stored
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energy. To create a scenario-based tool that simulates such relationships Cellular Automata logic was used.
SELF SUSTAINABLE HABITAT ON PERMAFROST
Figure 2: Sustainable habitat on permafrost
As the aim of the project is to create a sustainable settlement over a permafrost, different natural features are selected to supply the new emerging settlement. One of the major resources of the area - methane can be harvested in many ways. The energy that can be produced out of such amount of methane can be used for electricity and heating systems, sustaining the whole settlement. With advancement in technologies, methane also can be stored as a compressed gas, therefore, small area is required for storage. Water, excess resources as by product from the thawing ice and precipitations, can be also collected, filtered, stored an distributed through the networks within the settlement. Wetland ground, that emerge as by result of the ice thawing process, is reach in nutrients due to the buried underneath plants decomposition, and is a perfect
ground for agricultural cultivation. Such ground condition is also very suitable for medicinal plants cultivation, therefore pharmaceutical economy can be successful in this area. Increasing the amount of agricultural plants will provide food for both fish, land animals, humans. Land animals that can be often found in the region, like reindeers and moose, can be used for livestock production, such as meet, milk, cheese etc. To sum up, changes due to the global warming phenomena can create a new complete life cycle in the selected area area, northern Siberian taiga and tundra. This unoccupied area will become appropriate area for people to live in. New population can start growing in this self-sustainable environment. (Figure 2)
87 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CA CELLS FOR SELF SUSTAINABLE SETTLEMENT
To simulate the growth of the settlement in the project, logic of Cellular automata is used, the rules set depending on the property of the different type of cell. Mainly it is determining the area covered by each type of the cell, it’s location, relationship, functional distribution, and time.
with necessary goods and also are fundamental for economical independence. (Detailed design of each system further in Design Development chapter) Each of the units have a set of parameters (Figure1). According to those parameters one unit can convert to another through time.
Taken from the self sustainable habitat on permafrost principle, there are 5 type of cells extracted to use in the project. Those are harvester, distribution/storage unit, inhabitation, wetland agriculture, and livestock farm. Harvesters are floating units, that increase the temperature of the ground, capturing the released methane form thawing permafrost and redirecting it to storage-collector. From there the required amount of energy is redistributed through the networks, and
The relationship between units and the possibility of conversion can be seen on Figure 2. Empty land will always have a harvester as a first transformation, harvester can switch to storage, agricultural unit or a livestock unit (animal farm). Both agricultural and livestock units can be after transformed into a residential inhabitation units with various density.
remaining part is kept as methane hydrate. Agricultural and livestock units are supplying the growing city
On this page: Figure 1. Units in the system On the right: Figure 2. Relationship in between units
CH4 Storage(Distributor)
Agriculture CH4 Storage(Collector)
Amimal Farm
Empty Harvester
Agriculture
Habitation (1person)
Habitation (2persons)
Amimal Farm
Habitation (3persons)
Habitation (4persons)
Habitation (5persons)
89 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELLULAR AUTOMATA. MAPS
Three initial set of maps are used for Cellular Automata as information to be read and based on: Topographical and sunlight map, Flow map, Map of possible wetlands. Each pixel of the map is equivalent to 36.5m by 36.5m as it equals to the area that sustain one person. The same dimensions are also used for Cellular Automata cell size. Each of the maps embed different information and react differently on different type of cell. The information can influence on timespan of each unit, speed of thawing process, potential of one or another cell to appear in examining location point. For instance, as shown on map 1, harvesting units will work more efficient on the area that is more exposed to the sunlight, therefore will be able to convert into a different unit earlier than the same units working in the
shaded areas. Housing units are also checking for a suitable sunlight condition. Only in the area where the sunlight requirements are met, housing unit will then have a permission to appear. The two other maps are based on soil condition, as it will influence the structural factors of emerging units. In the areas, that are overlapped with high strong water-flows (map 2) and potential of land conversion into a swamp (wetland with high percentage of water accordint to its ground altitude, map 3), the emergence of a housing unit is exceedingly unlikely. Each of the maps is done by the analysis of the existing three-dimensional terrain of the site, than converted into a two-dimensional bitmap to be further fed for the algorithm.
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On the right: Figure 1. Environmental maps 1,2,3
91 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELULAR AUTOMATA. MAPS
Figure 1: Wetland altitudes
Similar to water flow reasons, the wetland map is determing to the cells ‘where not to appear’. The map is based on a simplified topography levels, that are translated into a planar surfaces. Each of the planar surfaces have its own color within a gray-scale range. The closer level is to the river - the higher is a chance that it is going to have a high percentage of water in soil. Therefore, there will be a higher chance of a new aricultural wetland units to emerge within the zone, and the less possibility for a housing unit to appear due to structural reasons. (Figure 1)
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CELULAR AUTOMATA. RULES
CH4 Storage(Distributor)
Agriculture CH4 Storage(Collector)
Amimal Farm
Empty Harvester
Agriculture
Habitation (1person)
Habitation (2persons)
Amimal Farm
Habitation (3persons)
Habitation (4persons)
Habitation (5persons)
Figure 2: Units relationships
Cellular automata in this dissertation is used as a tool that allows to understand functional distribution and zoning of the emerging city through time. The base for it is information encoded in two-dimensional bitmaps maps of topography, flows and water. Taking in consideration the time-line and calculations for happening on-Site processes, one cell can convert into another.
dictate the rules of growth to their neighbors. The initial set is usually a specific amount of harvesters grouped around a storage cell. The recombinations, sizes, amount of those would be explored further in this chapter. Apart from the initial set all the growth happens from an empty ground cells. (Figure 2)
To start, there is always an initial set of cells, that after
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As it can be seen on the diagram (Figure 1), empty cell is checking the information of the bitmaps as initial rules. On the map of wetlands levels, the cell has a higher possibility to convert into another state if its position is lower, closer to the river - on a bitmap where the point has a value more than 60% of white. The same time, with similar way of reading the information, cell checks the sunlight map and the value within the range of 50 to 80% has a high probability for harvester placement. Through time, it also takes into consideration the energy difference value from the previous generation. If that value is positive, the growing rate of harvesters will slow down in order not to overload the storages with methane. Being a floating unit, harvester doesn’t require the flowmap to be checked in order to appear. Three values, obtained from the bitmaps, are deduced to an average number (Sum/length-list). Then the unit rechecks the sunlight map and the relationship to the neighbors it has. Consequently, when the area is shadowed and there is less than 30% of sunlight, empty cell needs at least seven neighbors-harvesters around it to appear. When the amount of sunlight is higher, for instance more than 60%, the empty cell would require no more then 3 neighbors-harvesters to convert to harvester state. For the situation when cell is located on a river, it converts to an empty-black unit, until it reaches the other side of the river. Harvester unit in its turn have 3 possible conversions: Storage Collector, Agriculture, Livestock cell. Storage Collector unit is supposed to receive the energy from harvesters, and then redistribute it through the system. To convert into a Storage Collector cell, unit has to check the flow bitmap at first, as Storage is required to deal with structural issues. Storage Collector will have the chance to apper only if the water amount in the checked location point is less then 25%, as only in this case the ground condition doesn’t cause any difficulties for the structure. If this requirement is positive, further rules for storage emergence are as simple as that: if there is a certain amount of harvesters that require a
storage space, and there is no even one storage within the neighborhood, the unit can convert into a Storage Collector unit. Apart from Storage Collector, Harvester unit can also convert into Agricultural and Livestock cells. Agricultural and Livestock cells are the base for economical and sustainable development of the city. As it was explained earlier, some of agricultural units that are supposed to emerge on intense wetland condition are used for pharmaceutical agriculture only. The plants that are growing within those units are after collected, dried out and transported to other places for further pharmaceutical processes. This create a strong economical basis for the city development. The Harvester units and Livestock units create the conditions for self-sustainable development, supplying the growing city with its own energy and food. The conversion of Harvester in either of the units happens only if the main requirement is reached: the full amount of methane is taken from the ground during the frost thawing process(Calculation pages 70 - 74). The thawing rates, as was explained in the calculations, are location-depending. That means that the speed of thawing directly depends on the amount of sunlight the area is getting. The time for permafrost to thaw and to release methane is fluctuating within the range of 336 to 400 months, respectively to the amount of sunlight hours in the area (not considering the initial settlement, it has a different rate to inhabitate area in specific amount of time). The calculation of sunlight hours of each location point is embedded in the algorithm, counting the data of the Sunlight Terrain bitmap. If the time requirement is positive, then the further rules for conversion of the unit are depending only on Sun Terrain bitmap. Agricultural units require sun, while Animal farms require shadow. As explained on the diagram, if the sunlight of the location point is more than 60%, Harvester unit will convert into Agricultural cell; if less, it will convert into a Livestock unit
97 On the left: Figure 1. Transformation rules for harverter element from the ground and to the other functions
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELULAR AUTOMATA. RULES
Storage Collector unit, after some time, can also transform into other cells. Fore instance it can become a Storage Distributor, an Agricultural or Livestock units. Storage Distributor unit is distributing the energy to the living residential cells, and happens before any of
the residential sells appear. It appears only when the neighborhood is ready for residential inhabitation, what happens when all the cells of the neighborhood have converted from Harvester cells into either Agricultural or Livestock cells. (Figure 1)
convert into Storage Distributor cell. If flow and water requirements are not enough in terms of structural support, cell would transform into another additional Agricultural or Livestock units. The important feature of those additional Agricultural and Livestock units is their disability of further transformation into any other cell. It happens because the wetland and flow ground condition would be too unstaible for any other cell within the system.
If the first requirement is positive it means that the unit is ready for transformation into the further state. Cell rechecks the terrain information maps and if the wetland and flow conditions are good enough for structural needs of both Storage Collector unit and prospective inhabitation units, and there is enough of sunlight to maintain future living area growth, Storage Collector will
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On the right: Figure 1. Transformation rules for storage element to the other functions
99 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELLULAR AUTOMATA. RULES
As soon as there is a Storage Supplier appeared in the neighborhood, the initial Agricultural and Livestock units (that appeared from Harvester cell directly) have the potential to transform into Housing units. As in any other conversion, the first information to check would be the base map bitmaps, to assure the ability of the structure to support emerging Cells and the enough amount of sunlight hours for the inhabiting zone. Therefore, first conditions for the Housing unit to emerge are the amount of water within the ground condition that is less than 35%, and more than 60% of sunlight within the zone.
Second criteria that is tested is the average energy difference through generations where one generation equals to one year. If energy difference is more than 20 gigajoules, there is a chance of Housing unit to appear on the checking location point.
As shown on the diagram (Figure 1), if the energy difference is 20 gigajoules, there is a 2% possibility of house to emerge, and if the energy difference equals 1000 gigajoules, the chance for the Housing unit emergence would be already 50%, making 1 gigajoules equal to 0.05% of emergence possibility accordingly. With same energy difference rates and population
growth, when the amount of population within one neighborhood reaches certain amount, second(more dense) typology of Housing unit emerges. As long as the amount of second Housing unit doesn’t exceed 50% of the amount of first Housing units, second typology can be built. Using similar rules, but increasing populations, the Habitation units 3,4,5 can be introduced in the system. (Figure 1)
However, even if all the criteria described above are accomplished, it still doesn’t guarantee the emergence of the house in the space. The chance of house to appear is directly proportional to the average energy difference.
101 On the lest: Figure 1. Transformation rules for agricultural and livestock elements to the other functions
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELLULAR AUTOMATA TESTS. TOOL CALIBRATION
To test and calibrate designed tool several experiments were done. Some of them were aiming to find best initial position and settlement for the growth of city, others were more population-density oriented. Every test is done within the timescale of 77 years (2013-2100), having each generation equal to one month. Monthly data is embedded for the analysis of solar access and melting rates for Harvester units. Every generation, the given data is extracted and placed into a spreadsheet, the shown print-screens of experiments are taken at the final stage of 2100, a final arrangement of each experiment. (additional information from the
tests can be observed in appendices). All of the tests were analyzed in terms of lifespan, metabolic rate of the city energy use, amount of storages, self sustainability, total population, and population growth rates. The results of the analysis were used for further evaluation. Each of the tests is carefully studied, documented and concluded. Out of the tests conclusions the final rules for the design development CA functional distribution would be set.
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On the right: Cellular Automata color code
Color codes for Cellular Automata
103 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
TEST 1
Arrangement settlement 1
of
initial
Arrangement settlement 1
of
initial
Arrangement settlement 1
of
initial
The main purpose of the first test is to check the
influence of initial arrangement of the settlement on the growth pattern. For this experiment every test consist of equal amount of harvester and methane storage unit for a fair comparison, where each test started with 32 harvesters and 4 storage units. From the experiment we can see that they do have similar but slightly different performance pattern. The first configuration has the slowest growth but it has the lowest peak comparing to other configuration in methane storage space needed which is good to reduce the maximum methane storage size. The second configuration performance slightly differs from the first test. The third test can be seen as the fastest growing configuration. However, on the “city area population ratio� graph shows that on a later years the ratio is higher that the other two. That means that people occupy more space per person than is required that is not efficient. Since the speed of the city growth is not essential, but the minimized storage capacity is crucial, scenario 1 will be carried on to other experiments.
105 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
TEST 2
The second experiment focus on two different initiation
time for housing unit appearance, at year 3 and year 30. The emergence of the house in the first configuration happens on the year 3, when the permafrost is not completely thawed but the amount of stored energy is enough for first people supply. Second configuration is determining the time for the first house emergence only at the point when permafrost is completely released, what takes 30 years to happen. There is a huge difference between the two configurations, and the most crucial one is the stored energy amount and sizes of storages. For the second configuration scenario the size of storages is enormous. It also make it hardly believable that the settlement will be able to emerge without any people living in. Initiation of housing at the year 3 is used for other experiments.
107 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
TEST 3
Experiment 3 is aiming to understand the importance of the location for the first set of cells. Four different locations with various amount of sunlight and water within the ground condition were chosen to be tested. The results of four configurations vary significantly. The resulting aggregations differ in the speed of growth, that is affected mostly by the sunlight access, and in balance between agricultural and livestock units that supply the settlements. Comparing the results, the best initial positions seem to be shown on the test 1 and 2. However, when the city is growing from only one location, the resulting settlement has a strong central part of the city on the initial location of growth and very non-confident emergent part on another side of the river. The last exercise (test 4) is starting from 3 locations (including the two positive ones). It gives a very balanced relationship in-between population, livestock and agricultural amenities. It also performs well as a city with multiple centers, that are more or less equally developed. Though, due to the amount of population and the speed of city growth, the energy amount has a decreasing trend starting at the year 2075. That means that the amount of harvesters is no more capable to supply people with energy, and the city will die out. As a conclusion of this set of experiments, two locations with higher performance are taken for the further investigation as two independent starting points.
TEST 4
The experiment 4 is density orientated, where the pattern of growth is depending on the initial amount of people living in the housing cell.
The quantity of two, three and four people per cell were checked. The resulting aggregations have different performances in different criteria. The first one is eliminated from the list as it has the highest area of the storages, comparing to the other two, and at the same time lowest population. The other two settlements have similar performance, however the city fabric of the last is very disperse, while in the second the cells are compactly arranged next to each other. Following the logics of the community growth and interconnection of people within the city, the second arrangement is considered to be the most efficient. The quantity of 3 people per living cell is taken as the initial number for further development.
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CONCLUSIONS
After evaluation of the written tool the final initial set of parameters were selected. Those are the following: • • • •
The city growth starts from 2 locations Both of the locations have an appropriate amount of sunlight The first housing unit emerge within the system on the 3 year of growth The population in the lowest in density cell is 3 people.
On the right page can be seen the final functional distribution run, on it’s year of 2033. (Figure 1) Now, when the relationship in between cells is set, functional distribution can be translated into design. What is important to remember is the fact that the color of cell in CA environment (as it is represented on the diagram on the left) only identifies the region of settlement for design, not the morphology.
according to the result of the previous generation. Though, an important criticism would be the fact that is not a perfect set of cells that could be emerging in the city. Critically analyzing the system now we can observe that, for example, it doesn’t include any type of ‘social’ cell emerging trough time, that could be after translated into a kinder garden, school or even a cafe. In design development chapter we did introduce a new type of typologies for such spaces, but its position and number within the system in the final output is questionable, as it doesn’t really correspond to any of the relationships in between the growing communities, that emerge through time.
The tool has a certain level of intelligence, having the ability of re-evaluation of its rules every generation
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On the right: Figure 1. final set distribution year 2033
4
CHAPTER
design development
storage
DESIGN DEVELOPMENT
As an output from research development we got the rules and initial setups for the growth of final distribution.
distances of spans of pneumatic structures, and would be explored more carefully in the following chapter.
To translate it into design, the algorithm was connected to grasshopper environment where further changes were happening interactively. Every cell was read as a region when certain function (harvesters, houses, agriculture, etc) was supposed to be allocated. The way of how the region is dynamically translated into the structure can be seen on Figure 1.
The way of housing allocation within the region, networks growth and pedestrian flows within the city will be also explored in this chapter. Having a dynamic growing system, that calibrates itself through time, we are not only working on the final output, but investigating into the settlement and community growth through time, that is adapting to the changes.
All the units, as it was explained in preliminary design, are using the same structural support - triangular frames. The top part - pillows, platforms - can be demolished and reinstalled according to the function they accommodate (Figure 2). The sizes of the structure are constrained by the
On the left: Figure 1. Translation of CA regions to structural framework On this page: Figure 2. Deployable units
117 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
STRUCTURE AND TENSAIRITY
Due to uneven ground conditions, thawing frost, wetland condition there is a necessity of use of lightweight structures, that can support big loads. Tensairity and pneumatic systems were carefully studied for this case as an open research, physical and digital experiments. For the emerging city system 2-dimentional tensairity was not enough as there has to be a solution to carry various housing or storage systems loads. Tensairity system test was carried out to understand and explore the possibility of applying the same system into 3 dimensional structures. To justify, tensairity system was previously working on tube-like pneumatic envelope, where on-top are compression elements that are being tensioned by the strings which tied around the air bag. (Figure 1)
For further exploration, basic triangular shaped compression plain was used instead of a linear compression beam. The model was constructed with card-board paper of 5 mm thickness. Each side of the triangle is 350 mm span with total surface area of 53000mm2. First experiment was carried out where a layer of pneumatic is attached under triangle platform. 0.1 Bar or normal air-pressure is pumped inside the envelope. A can containing liquid of 0.33 kg weight is applied one by one. The structure fails to work at 2.6 kg weight loaded. Second experiment was then set up to compare the structural capability. The same card board and same air-pressure were used, though tensioning system
10.6kg
2.6kg
50mm
5mm Cardboard
5mm Cardboard +50mm tensairity
maximum load : 8 cans (2,664 mL) (2.6kg)
maximum load : 32 cans (10,656 mL) (10.6kg)
350mm span 530 cm2
350mm span 530 cm2
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ratio: Depth:length
weight carried
0.02kg/cm2
5 cm 35 cm
0.014kg/cm2
50 cm
1:7
200 kg/m2
1:8 1:9 1:10 1:11 1:12 1:12 1:13 1:14 1:15
196.3 192.8 186.2 179.6 173.0 166.4 159.8 153.2 146.6
1:16
140 kg/m2
kg/m2 kg/m2 kg/m2 kg/m2 kg/m2 kg/m2 kg/m2 kg/m2 kg/m2
800 cm
was applied underneath the pneumatic to tension the compression platform. Load is then gradually applied by the same method until the structure fail. The structure was efficiently working, carrying up to 10.6 kg load which is 4 times more than structure without tension elements.
As the results from the physical experiments, limitation is set. To make sure the structure of the 3D tensairity system perform effectively, the size can vary only to the certain point. For instance, it is necessary to keep the angles at every corners of the triangle no less than 55 degree to maintain almost equal side triangle.
As a conclusion to the experiments, the three dimensional tensairity is proved to be working efficiently. In comparison to the bigger scale experiment, it can be stated that the structure capability is relied on the thickness of the pneumatic system, the air envelope, proportional to the length of the span. To illustrate, at thickness to spanning length ratio 1:7, can carry 200 kg per m2. Increase in ratio proportionally, at ratio 1:16, the structure can bare the load at 140 kg per m2. (Figure 2)
This is done as once the side of the triangle is too long it looses the structural stiffness. To clarify, the center of the triangle is the thickest part and gradually gets thinner as it moving to the edges. The structures are stronger with thicker air layer. When one side became very long, it creates a long spanning area that has a very thin layer of air, this is where the structure can fail. (Figure 3)
Failing structure
section
On the left: Figure 1. Three dimentional tensairity physical experiment On this page: Figure 2. translation of the experiment to the working ranges Figure 3. Computational analysis.
119 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CIRCLE PACKING
Considering the results of physical experiments the distances and ratios for the multidimensional tensairity can be used. Within the area, the tensairity will still vary significantly, depending on the topography, level of moist within the soil, etc. To create a pattern of the 3 dimensional tensairity Circle packing algorithm was used. As an output of circle packing algorithm central point of each circle is found and used for the further development as a connection point of three dimensional tensairity structures. Just as Cellular Automata, the base information for Circle packing algorithm is read from a two dimensional
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gray-scale bitmap. (Figure 1) The domain for circle diameters is determined by tensairity span lengths. Reading the map, the circles have a longer radius on the areas that have higher percentage in alpha-channel (whiter). This happens as those areas have the most amount of water content in the ground condition, and need higher amount of floating foundations and shorter span of construction. Circle packing algorithm created 300,000 circles of the radius’s within the range 8-20m. The 300,000 central points were extracted and interconnected using the component of Delaunay mesh. “Delaunay triangulation for a set P of points in a plane is
a triangulation DT(P) such that no point in P is inside the circumcircle of any triangle in DT(P).� The reason of choice of delaunay triangulations is that it maximize the minimum angle of all the angles of the triangles in the triangulation; they tend to avoid sharp angle triangles. This is the most significant issue for the three dimensional tensairity structures to avoid failure of the structure. Usually Delaunay triangulations are planar. However, in this dissertation work with terrain is playing a significant role. Before triangulation, all the points that were the resulting central points of Circle packing algorithm are projected on the terrain surface. Then, three dimensional cloud of points was interconnected using the Delaunay triangulation logic, and after was translated into a first step of design approach - three dimensional tensairities with pedestrian pass-way in between.
On the left: Figure 1. Ground stability map On this page: Figure 2. Computational sequence Figure 3. Unit section
On the diagram (Figure 2) is shown a central part of the Site (it is also marked on the map Figure 1), which was done using the algorithms described above. The diagram is showing Circle packing, Control points (central points) projection on the surface, three dimensional Delaunay triangulation, and translation of the last into the first design output - structure and tree-dimensional pneumatic structures on floating foundations (Figure 3)
Circular Sections
121 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
SPREAD DELAUNAY
The figure on the right shows distribution of the structural frames on the year 2033 - in this case city has 20 years growth cycle. The growth through time can be seen on the bottom of the page.
Therefore the year 2033 is chosen as a final distribution for the design proposal. (Figure 1)
20 years is considered as a lifespan of one working generation. Moreover, if the zone will be developing as expected, most probably other technologies would emerge for the system in 20 years, meaning that the full logic of growth rules can be changed.
2013
2017
2024
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Here and on the right: Figure 1(abcd). Growth of structure through time
20 years growth cycle
2033
NETWORKS IN THE SYSTEM
There are two types of networks developed for the system: energy network and pedestrian network. Both of them have their own hierarchy and logic. Pedestrian network becomes important when the first inhabitation unit appears. Energy network start its development with the first Harvester and Storage emerged in the system. Energy network have 3 main levels of hierarchy. The first level of hierarchy is the interconnection between Storages, both Storage Suppliers and Storage Collectors. The system is a distributed type of network, where every storage works as a hub supporting each other in case of individual failure. Network is generated on the logic of minimum spanning tree, directness ratio and shortest path trough the Delaunay triangulation edges (faces form the surfaces for units development, all
Generation 1
New emerged Storages Collectors
the networks are happening on the edges). The reason for use the 3 mentioned techniques is minimization of the total length of tubes used for the system, optimizing the cost and the energy needed to transport energy from one hub to another. As it can be seen on the diagram (Figure 1), at first system sets the locations of each storage, then it takes Delaunay mesh edges as a graph of lines, and creates a minimum spanning tree connection in between them. Analyzing the resulting network it creates the necessary additional connections when the directness ratio of connection from one hub to another exceeds 2. Growing through time new emerging hubs are connected to existing network within the same logic. When Storage Collector hub through time is transformed into
Generation 2
Storages Collectors from previous generation
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Emerged Storages Distributors
Generation 3
Minimum Spanning tree connections
Additional connections based on directness ratio analysis
Generation N
Regions of Suply, Voronoi Cells
Generation N
Storage unit location. Center of region
a Storage Supplier hub (marked as orange), the network in between those hubs is no more shared with previous ones. The new emerged part of network is analyzed once again for the directness ratio parameter, and in case of necessity additional lines are contributed to the system. Another level of hierarchy is the network that connects Harvesters to Storage Collectors, and the one that connects Storage Suppliers to the houses. The logic for those two network is following. Every storage unit (either Collector or Supplier) has its own corresponding region consisting of all points closer to that storage than to any other. To find the regions Voronoi cells algorithm was used, that is dual to
On the left: Figure 1. Growth of main energy network through time On this page: Figure 2. Storages supply from harvesters network
Shortest path of Supply
Energy network within the region
Delaunay triangulation. Every Storage cell now is read as center of a region-polygon (Figure 2). Inside of each polygon the shortest path between every single point of the region and the central point is found and connected. This is how the efficiency of speed of energy distribution can be guaranteed for the system. This network is emerging every generation, recalculating the regions in case if a new storage was created. On the diagrams is shown how the created tool is working, and how the networks can be evaluated for the system. Though the diagrams don’t yet represent the real scale of Harvesters and Storages distribution within the area alike it is coming from Cellular Automata functional distribution.
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TYPOLOGIES DISTRIBUTION. REGIONS AND CELLS
Translating data of Cellular Automata to inhabitation units abide following rules.
Analyzing the difference within the population through time, the growth of it within each region can be identified.
Cellular Automata inhabitation cells are combined to regions according to the color zone of cell, where the color define its density, amount of people it can accommodate. Based on this, the total population within each region can be counted real-time, on the basis of a month, year, decade, that brings the freedom of decision making within the timeline scale.
For example, as shown on Figure 1, on the year 2013 there are 20 light blue cells, each of cells is able to accommodate 1 person, and total population of the neighborhood is 20 people. In 1 year (2014), 27 new light blue cells emerged, 8 cells from the previous generation converted into darker blue cells (grew in population, Darker blue cell is occupied by 2 people), making it in total 47 number of cells with population of 55 people. Therefore, in one year the population grew from 20 people to 55, 27 of which have to be accommodated within the expanded region 1 (light blue cells), and other 8 people have to be accommodated within an emerged region 2, where other 8 people are already living from previous generation. The dark cells form the new region
For the population growth and housing distribution it is important to identify each region growth and growth of its population in the scale of 1 year, as there is only a specific time, when something can really be built within the area. (Jan-March, due to the frozen ground condition and still accessible amount of sunlight hours. )
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House for 2
House for 4
House for 6
Region 01 Region 02 Region 03 Region 04 Region 05 Preferable, higher priority
due to the densification of the zone. Same process is repeated each year. There are 5 possible regions, according to the color of cells in Cellular Automata, that can be occupied by people from 1 to 5 per cell accordingly. (Figure 2) Each of the regions emerge from the previous region through time due to densification of the zone, as it was explained before. There are 3 different typologies that are determined by number of people that can be occupying it. (House for 1, 2 or 3 people) Each region has its prioritized typology determined by density. (Figure 2) Region 1 can only have the typology of H1, as it is a starting point of developing neighborhood and the population is very low comparing to a large occupied territory. Regions 2 and 3 can
On the left: Figure 1. Region development through time On this page: Figure 2. Typologies priority in region
have both typologies of H1 and H2, but prioritizing the second - therefore optimizing the time and amount of material for construction. Just alike, Regions 4 and 5 can have all possible typologies, but prioritizing H3 to H2, and H2 to H1. Following this logic, region 5, emerging from region 1 and evolving as region 2, 3, 4, through time will contain the maximum amount of typologies and will have the most diverse city fabric. The Diagram 3 (next spread) is showing the growth and housing typologies allocation, following rules, through time. Like this the growth of the settlement not only as a final output, but in any moment of its growth, can be understood.
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TYPOLOGIES DISTRIBUTION. REGIONS AND CELLS
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NETWORK FOR TYPOLOGIES
As it was explained earlier, the delaunay triangulation edges can be directly translated into the lowest level of hierarchy of pedestrian networks. It happens as such as each of the units used in the system has the frame structure with a pathway on the side, and the central part can be removed-changed according to the needs (Explained earlier, p. 82 ‘Units primary design’, and p. 117, ‘Design Development’).
region they are supposed to be allocated. The region is directly taken from the CA functional distribution system. Floating parameters of coordinates of housing units are connected to Galapagos Evolutionary solver as gene pool. The fitness criteria is trying to minimize the total length of minimum spanning tree in between housing units with each other and with storages.
As soon as the first house emerge in the system a new level of hierarchy is created. It is based in the minimum spanning tree logic with consideration of directness
After the optimized result is found,it is analyzed for the directness ratio parameter. If the directness ratio parameter from one hub to another (now both storages and houses are considered as hubs) exceed 2, additional
The storages are taken as stable points, the houses have floating parameters of coordinates within the
The resulting network is fed to the shortest path as a graph of lined, and the algorithm is searching for the most optimized way of translation of those path into a
ratio, and the most optimized result is found using Genetic Algorithms.
Storage distribution and amount of new houses per region
Storages as stable element
connections are contributed to the system (Figure 1).
Minimum spanning tree result after Galapagos
Emerging housing units of different density
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Region borders
Shortest path of minimum spanning tree through pedestrian triangulation
Minimum Spanning tree connections
Additional connections based on directness ratio analysis
Storage distribution and amount of new houses per region
Minimum spanning tree result after galapagos
Shortest path of minimum spanning tree through pedestrian triangulation
Existing houses and networks from previous generation Storages as stable element
Emerging housing units of different density
Region borders
network that follows delaunay triangulation edges. Every generation new houses and storages are emerging in the growing city. The allocated in previous generations houses are now added to the system as static elements. New houses, running the same algorithm, try to find the position within the region, that is optimized already both for the new storages and for existing aggregation.
Minimum Spanning tree connections
Additional connections based on directness ratio analysis
it has. After identification of the typologies process is completed, (p.118-121) they are allocated within the regions they correspond to starting from the most dense region the the least dense one.
The system becomes more complex as soon as another region (Higher density region) appears in the system. As it was explained earlier, every generation regions are recalculated. The algorithm has the same logic, but runs as many times per generation, as many regions
On the left: Figure 1.Typologies network computational sequence On this page: Figure 2. Growth of typology networks
131 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
GROWTH OF NETWORKS AND ALLOCATION OF TYPOLOGIES
The figure on the right shows distribution of typologies and networks on the year 2033. The continuous growth year by year can be seen on the next spread. As was mentioned before, year 2033 is chosen as a final distribution for the design proposal. Settlement is poly-centric, both of the centers are equally strong and independently developed with the total population of 1200 people. (700 people in the Northern center, 500 people in the Southern). Comparing to the major cities in the area (Yakutsk, Viluisk, Oyamyakon with the populations of 280,000 1; 10,000 2; and 472 people 3 accordingly) such density is considered to be appropriate for the growth of the settlement in just 20 years.
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On the right: Final distribution of the networks, year 2033
20 years growth cycle
2033
2013
2014
2015
2020
2021
2022
2027
2028
2029
2017
2018
2019
2024
2025
2026
2031
2032
2033
UNITS AND TYPOLOGIES
Main frame structure
Harvester unit
Storage 01: storage unit, collect methane from neighbor harvester
Supplier/ Storage 02: store and change methane into usable form of energy via turbine
Typology 01: inhabitation unit for 2 people
Typology 02 and 03: inhabitation unit for 4 and 6 people
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Typology 04: public use space
The overall design layout is based on starting frame structures growing with the city. First element that is being deployed is the harvester unit. It is composed of a 3 layers ETFE pneumatic pillow to heat up the ground. After the permafrost thaws and methane gas is completely released, some of the structures are removed and replaced by the storage. The storage connect to pipes running along the main frame structure transporting the methane gas.
occupying the space of 6 frames structure. Inside it contains turbine to generate electricity and distribute to housing unit through the electricity pipe. All the inhabitation units are designed to be fit within the area of 6 triangular frame for access to electricity in the center. However, for larger infrastructures, trunk tower is separately designed to have its own pneumatic support. (Figure 1)
The second type of storage or so called supplier is also
137 On the left and here: Figure 1. Units in the system
Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PNEUMATIC SYSTEM DETAILED CALCULATIONS OF THE ENERGY NEED
On the contrary to the reduction in the energy use for heating system through time, the amount of energy needed to maintain the whole city structure will keep rising correspondingly to the growth and expansion of the city, yet tensairity is also one of the major energy consumption. Since the pneumatic will be taking a big role in the city structural system, they can be separate into different uses given different functions. However, all of them use the same air-pressure which is 0.1 Bar (normal air pressure) as it requires small amount of energy to operates. First, pneumatic implemented in the harvester unit. This is when the pneumatic is use to fill in the gap between ETFE layers for an insulation purpose. Besides, to hold up the structures another pneumatic system is integrated for a structural support purpose. This pneumatic will be varies in size depending on the location and the amount of load they have to take. For instance, some unit may become so close to the river with watery ground condition, therefore, bigger pneumatic balloon support is needed to keep the structure floating. The next important pneumatic system is used together with other elements composing a strong structural support units. Here the air is inserted into the envelope connecting to the compression elements above and tension elements on the bottom, so called tensairity system. The pneumatic system here is working as a truss, handling and transferring the loads. The volume of air required is depending on the size of the compression elements, in this case, flooring panels. However, it is
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essential here to keep the envelope fully filled with air at all time to maintain the structural stiffness. Lastly, habitations and infrastructures in this city will be partially pneumatic. Its is important that all the structures have to be light weight and allow small movement to accommodate the change of the earth surface contour. However, here the pneumatic is not being implement for any load supports. Consequences to the big swing in temperature through out the whole year with the change in seasons, the air-pressure inside the envelope of the pneumatic system varies. Naturally, the air contract when the temperature is colder in winter and expand again in summer. As it contracts, the total volume of the air reduced, consequently, the pneumatic structures can failed to work. To maintain the structural strength, all of the pneumatic structures will be pumped only in winter as the ETFE envelope material can bare high-pressure system as well 4. Therefore, the air does not have to be pump when the temperature change. All of these systems required a huge amount of energy to operates while pneumatic pump usually work at 80% energy out put efficiency. The calculation is carried out to calculate how much energy is need per m3 volume of air. As a result, 11,750 joules is needed. In the calculations, it is important to note that the calculation is carried out based on an assumption that the pneumatic system works perfectly with no leakage, therefore, only one or initial air-pressure is pump in.
Given equation W
PB VB In (PA/PB)
PA ambient pressure PB pneumatic pressure VB volume of air (m3)
note _ initially one time air-pump _ ETFE is capable of high-pressure pneumatic system _ pneumatic pump works at 80% efficiency _ air pressure at -8 oC is 1.17 Bar at temperature -8 oC 1.18 MPa 1 m3 In (1.17/1.18)
9,400 Joules
pnuematic pump work at 80% efficiency rates 9,400 Joules energy input
80 100 11,750 joules per m3 0.00001175 gigajoules per m3
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ENERGY USES: PLANTS ELECTRICITY
Harvester
1 Methane Harvested
STORAGE 1
2 Collect and extract all the methane
partially stored for winter use energy use in winter
CH4
STORAGE 2 & SUPPLIER
3 Transform CH4 into usable source of energy partially stored Methane stored refridgerated process
partially piped out send out extracted methane from air
Electricity distribution: send out electricity to living module
Transform into usable form of energy Turbine generator change Methane to Electricity
Methane, main energy resources available on site, is the major compound that is used to drive a city. They are naturally released from the air-pocket underground in summer when the ground temperature is above zero degree. The harvester, insulating unit, is responsible for collecting all of the released gas within the coverage area and send it out to the receiving storages near by. At the storage the methane gas is extracted from the atmospheric air. Pipe line is placed at higher altitude to capture the methane gas, because methane gas is lighter than the other gas compound. From all the methane collected, Part of it will be store as a refrigerated gas and another part is then pipe to the second storage, supplier unit. (Figure 1)
Pipe gas: send out gas for uses eg.cooking Structure use: maintain pneumatic pressure in structure system Energy running heating system: to run air-pump heating system Energy for Agriculture use: for plants freeze-dry Energy to run system: running turbine and refridgerate process
This form of energy is also essential for uses in other process as well, such that the inferior importance is the energy needed to send back to run the turbine and refrigerating process to make sure it keep running. Another system that requires energy is the construction structure, where in order to maintain the structure stiffness of the pneumatic and tensairity structure, it is important to maintain constant air-pressure. Furthermore, some of the energy is needed to run the whole city heating system to make sure people are living in a comfortable micro-climate. Aside, gaseous form of energy is partially carry out to single habitat use. (Figure 2)
Subsequently, at the second storage, energy again separates into two parts, one is preserved and the other one is transformed into usable form of energy. Through the turbine generator, methane is combusted to produce electricity and further distributed to houses around the hub for general uses.
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On this page: Figure 1. Energy usage flow diagram On the right: Figure 2. Energy distribution system
Storages connect to each other to give hands when the neighbor one fails to work.
CH4
CH4
CH4
CH4
S1
gas collected by CH4 1 Methane harvester is send to the
CH4
Methane farming zone
MAIN ENERGY FLOW, DISTRIBUTION SYSTEM
closest first storage.
CH4
gas piped to the 2 Methane second storage, change form
and distribute to every module
Energy
Energy
Energy
Energy storage change form of 3 Second methane into usable electricity
S2
Energy
Energy
Storages connect to each other to give hands when the neighbor one fails to work.
City, living zone
Energy
using the turbine and distribute to surrounding units
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HEATING SYSTEM CALCULATIONS
In order to set up heating system, detailed calculations has to be carried out. The calculations aimed to find the right size of the radiators requires to heat up the room. The size of the radiators is relating to the out put temperatures and the room size. It has been importantly carried out to make sure the heating system will provide interior comfortable micro-climate. The calculation guidance is provided by the energy saving trust, Department of energy & Climate change, GSHP association, HHIC, UHMA, and Heat pump association.5 The calculation was carried out in a room of floor area
20 m2. As if the room is well-insulated from all sides, the room heat loss is calculated at 2376.7 Watts or 118.8 w/m2. According to the Heat pumps guidance, room specific heat loss band has to be calculate and compare to the type of a heating system in a given table. To illustrates, the table gives the number of out put factor, in this case, 2.4. This number is then times by the heat loss (108.8 Watts) to see the out put heat energy required. Calculated, 2,376.7 x 2.4 = 5,702 Watts. This number is then use to find the size of the radiator to make sure it is big enough to give out this amount of heat to maintain the room temperature at 21 oC. In this case, to produce this much energy, the size of the radiator requires is 1600mm Length, 700mm Height, 135 Deep. (selected according to the Heat pump guidance)
3.5 = 1,583 Watts. At the specific site location, there are 5,843 hours of heating needed per year. If the system use energy at 1,583 Watts and running for 5,843 hours, the total energy required for heating system in area of 20 m2 per year is 33.3 Gj. It is important to note that in this case, the room must be well-insulated both wall and roof and refrigerated liquid that runs inside the heat radiator is at 45 oC or more. Moreover, the energy is calculated as if it runs for 24 hours. This number is included in energy need per person as one of the extent controlling the dimension of an inhabitation per person. Regardless to the change in global temperature, it is predicted that by 2100 the global mean temperatures will be increasing by an average of 16 oC. This huge amount of change in temperature has an impact on the heating system requirement. Under these circumstances, the warm period will be as much as 2 months longer than present. Which in this case, less amount of time is take for operating hours of the heating system. Correspondingly, less and less amount of energy will be use through time.
The air-source heat pump produced heat energy 3.6 times more than in put energy, therefore, if the required out put energy is 5,702 Watts, the in put energy is 5,702 /
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On the right: Air pump heat source
Air-source heat pump -8 oC external air temperature pipe out to radiator
65oC
compressed liquid
hot water pump out
refridgerate liquid
21 oC
2.4 m
room air temperature
45oC 3m
Area: 30 m2
Radiator 16x0.7x0.13m.
10 m
Average temperature range, Takutsk Jan
Feb
Mar
Apr
May
Jun
Jul
26 22
Aug
Sep
Oct
Nov
Dec
22
12
12 9
13 9
-2 1
-4
1
0 0C
-12 -12
-29 -35
-12
-23 -34
-27 -31
2013
-38
-42
-40
Predicted change in 4 degree world, Takutsk
Heating energy usage Jan Feb Mar Apr
May
Jun
Jul 42
38
ASSUMPTION
NOTE
22 14
- Operates 24 hours daily for 8 months a year when the exterior temperature is below 10 oC - 8 months of operating hours of heating system -2 = 5,843 hours
-4
External air C o
-3 to 10 -7
3.6
-15
3.1
-19
-29
-35 -42
9
-12
-27
-38
= 118.8
2.4
1 Room Heat loss Out put factor Out put Heat requires
2376.7 2.4 = 5,702
Heat pump working rates
3.6
Out put Heat requires Heat pump working rates in put energy
5,702 3.6 = 1,583
heating time
energy use for heating
-4
m2 watts w/m2
m2
0 0C -7
-12
5,843 1,584 5,843 3600 = 33,319,123,200 = 33.3
Room heat loss calculation reference: http://www.aeon.uk.com/heat-loss-calculator.aspx
Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Dec
9
in put energy heating requirement
144
Nov
28 20
13 Compare to table Out put factor
Oct
12 2376.7 12
1
-12 Heat output (scale factor) 3.5 -13
external air temperature 22 Room floor area Room Heat loss
12
Sep
38
o -8 26 C
28
_ Room is well insulated _ Using standard radiator emitters
Aug
Watts m2 watts
-23
times in put energy
-18
watts times in put energy -34 -31 watts hours per year watts hours seconds joules Giga joules
-40
2013
-42
-34
-27
-35
-31 2013
-38
-40
Predicted change in 4 degree world, Takutsk Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
42
26
22
22 14
12
Nov
Dec
28
12 9
-4
Oct
38
38 28
Sep
12
13 9
-2 1
-4
1
0 0C -7
-12 -12
-13 -19
-29
-35 -42
-12
-23 -18
-27 -31
-34 2013
-38
-40
4 Degree world ASSUMPTION ASSUMPTION _ Room is well insulated _ Using standard radiator emitters
-8 oC external air temperature Room floor area Room Heat loss
NOTE - Operates 24 hours daily for 6 months a year when the exterior temperature is below 10 oC - average temperature increase by 16 degrees - 5-6 months of operating hours of heating system = 4,300 hours
Compare to table Out put factor
External air oC
Heat output (scale factor)
Room Heat loss Out put factor Out put Heat requires
-3 to 10
3.5
-7
3.6
-15
3.1
20 2376.7 = 118.8
2.4
2376.7 2.4 = 5,702
m2 watts w/m2
m2
Watts m2 watts
Heat pump working rates
3.6
times in put energy
Out put Heat requires Heat pump working rates in put energy
5,702 3.6 = 1,583
watts times in put energy watts
heating time in put energy heating requirement energy use for heating
4,300 1,584 4,300 3600 = 24,520,320,000 = 24.5
hours per year watts hours seconds joules Giga joules
Room heat loss calculation reference: http://www.aeon.uk.com/heat-loss-calculator.aspx
145 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
FORM OPTIMIZATION
precipitation drainage
insulation skin
access to energy
light weight structure
According to the site conditions, it is essential to make sure the inhabitation units answer the needs of insulation. As a reflection to environmental factor, the form is optimized mainly to reduce the heat loss. In comparison with two basic geometries, rectangle and semi-sphere, as if both of them containing the same volume, the semi-sphere have less surface area which refers to the less surface for heat loss. (Figure 1)
and flexible structure is required. Wood is one of the natural resources that is widely available in Siberia, and on chosen site. Naturally the wood from pines is strong enough to be used for structural purposes. Moreover, the wood itself contains certain amount of water that makes it almost resistant to fire, comparing to the other wood types. In addiction, pines wood is partially porous giving them a light weight.
In order to minimize the use of energy in the city, minimization of space is needed. Therefore, minimum area per person is given at 20 square meters. With the given amount of ground area, each inhabitation unit is individually optimized to find the smallest ratio number between the total surface area to the enclosed volume on semi-sphere based geometry. Because of the unstable ground condition, light weight
With the wood crossing construction technique, it allows small movement to occur while maintaining the stiffness of the structures. In between the woof frames the pneumatic pillow is added for insulation purposes. The transparency of each pillow can be variable according to the use of the inner space. The thickness can also be adjusted according to seasons.
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On this page: Figure 1. Optimization of typologies On the right: Figure 2. Typology 3 section. Drain of snow and energy distribution
SECTION: TYPOLOGY 03
The first typology was designed to accommodate two people, given a small area per person, the semi-sphere structure is strong enough to hold the load of itself.
of the platforms joining, therefore, it allows access from pipes from all directions without disturbing the tensairity platform.
However, moving on to the second typology, where it is designed for vertical expansion, the form of the house has to be modified. Here the central trunk was added, not only to maintain the structure but also to provide access to electricity, water, and gas. Waste materials from housing activities can also be excrete via this channel. The trunk will be located right on the center
The form of the typologies two and three, as shown in the section below, still allows the precipitation to drain along the building envelope to reduce the amount of loads the structure have to carry (Figure 2). Yet, it is also optimized to reduce heat loss. Two levels and three levels flooring can here be applied in those structures.
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SECTION: TYPOLOGY 04
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On top: Social space typology section
For all public spaces, such as communal space, school, shopping mall, and offices, several trunk structures are introduced. The trunk is mainly designed for a structural support and insulation, where the same system of having pneumatic pillows is also applied. However, the ETFE materials are differentiated at some part by filming ETFE with partially reflective surfaces. This allows sunlight reflection as it enters the trunk structure, providing sunlight to every levels ‘from inside’. The trunks will have their own pneumatic supporting structures on the bottom as it will be situated on the ground, providing connections to the pipelines system. Energy, water, and gas will be carried on to the upper levels through the trunk.
In central space, several trunks will be placed close together and form a big enclose space by an envelope. The overall form is sloping towards the ground for precipitation drainage purposes. The space inside can be adjusted for future expansion to accommodate larger population.
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5
CHAPTER
design proposal
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SECTIONS LONG ONES
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6
CHAPTER
further development
FURTHER DEVELOPMENT
To sum up the project, the city system that was developed has its focus mostly on urban scale and energy. Typologies were already designed to answer the human needs together with the main networks. However, in the smaller scale, systems such as social networks and detailed design of each typology is still a topic to explore. For further development, speaking about the networks, we would like to research ways of water collection, filtration, storage and distribution systems and how they can be integrated into design. We would also focus on social networks, the ways of how people move in the area, what type of social infrastructure can be developed. Cultures and tradition of people living in Siberia may reflect back into the more detailed design of the typologies. Microclimate on site is significantly important in this area with extreme environmental conditions. Building aggregations patches and its design could be further optimised in order to create comfortable microclimate within the city.
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7
CHAPTER
personal contributions
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PERSONAL CONTRIBUTIONS ANNA KULIK
The physical context of the emerging city and flow of data within its systems are playing a crucial role in this dissertation. Tundra environment is considered to be doubtful for inhabitation due to its harsh environmental conditions, temperature, humidity, snowfalls, etc. Furthermore, the methane gas is released to the atmosphere every summer when the frozen ground thaws. Possible health effects of breathing in methane at high concentrations resulting in oxygen deficiency, lack of muscular coordination, emotional upset, loss of consciousness, respiratory collapse and death. These aspects so far were making the area very unattractive for human inhabitation, and as a result it is the huge land mass that is non-occupied till now.
in this dissertation. The methane gas, if been harvested and stored, is not anymore causing the risk for human health, but on contrary becomes a great resource for the energy supply (The largest, comparing to the other fossil fuels existing on the planet). What is more, the plants species, buried under the frozen ground are attracting a lot of scientific interest to this zone. The emergent settlement that is well designed, that is appropriate for its climate, culture it is situating in, the city that is capable of reacting over the long and short term to changes in demography, climate, water supply, energy systems, food and so on is the greatest ambition since the very beginning of the dissertation.
Historically cities were emerging in this unpleasant for human being environment only because of the natural resources – oil, gas, coal, in other words, fossil fuel. Then, as soon as the resource was depleted the cities were dyeing out as fast as they appeared.
Such a city, that is very ecologically and energy oriented has to be reactive and responsive. This means it is situated in a higher order within the city-systems (water, energy distribution, city growth) that are reactive and responsive to dynamic changes of the climate, flow patterns when the energy resources are constrained, and others.
With the global warming phenomena environmental conditions in this area are becoming more suitable for human life, as it was carefully studied and documented
Dynamic data flow is fundamental for this type of research. However, it is important to distinguish whether the data that can be extracted is always
179 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
relevant, when we are talking about the city system. The general data has to be carefully sorted, until there are left only specific subsets of it, that potentially may have some further impact on the system and are capable of making changes. In case of this dissertation, the research investigated into exploration of two very specific subsets of data, environmental and energy flow, and concentration on the relationship of those subsets in the emerging patch of a city. The relationship of two sets of data through time was partially dictating the rules for the city growth, where the analysis of those relationships was contributing into adaptivity quality of emergent settlement according to changes in Data. Searching for the way of simulation of city growth according to the energy flow within the city, the logic of Cellular Automata was used as a tool. It was designed based on the climatic data and the energy data, calculated from relationships in-between different type of cells, where every single of them had a specific value coupled to the energy source – either as a supplier, consumer, or distributor. (Cells and relationships, “Research Development�). The simulated climatic temperature data was based on the existing conditions of the site and the 40C world
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scenario, changing through seasons as it happens in present and gradually rising every year, according to the predictions and simulations of the 40C world scenario. The amount of sunlight hours were taken as the fixed ranges per month, based on the Wikipedia information, multiplied by the percentage of shadow in the area according to the terrain where the settlement was supposed to grow. The introduction of the terrain data (sunlight shadowing, water flow through the area) gave desired context specificity for the project. The data is taken on the monthly basis, since the seasons are varying significantly but gradually. This type of information is not that much accentuated in the dissertation itself, as it has a less value of importance throughout the whole project. Nonetheless, it was crucial for the development and evaluation of the main algorithm that was simulating the regional distribution for the growth patterns. The CA is basically working as a customized energy calculator that has several fitness criteria, where the main ones are: minimization of storages sizes, balance in between energy harvested and energy consumed, constant and reasonable population growth. Following the logic of hibernation (tundra animals hibernation,
Domain chapter), the energy was partially stored in the summer season, and then slowly released in winter when due to the cold temperatures the harvesting of methane was constrained. Very clear example shows that the growth patterns that have a very high amount of harvester units can over exceed the system with methane gas, while a high number of populations would exhaust up all stored energy. Fitness criteria regarding the agricultural facilities and livestock are also embedded in the system and calculated simultaneously. However they have a relatively low importance comparing to the data based on energy amount and flow optimization. The result of the optimized CA provides the continuous sustainable growth of the settlement, distributing the regions and calibrating the amount of cells. However, this is still a very diagrammatic result that is counted for the system. The amount of typologies and the networks are optimized for the energy flow within the system, but that is only the lowest level of complexity. The typology itself could be involved in the system dynamically also.
microclimatic strategies for further investigations. However, thinking of the typologies as the elements of the energetic system that was created, different approach could be added. Gradually over a period of time, higher level of designing buildings could be investing into the way of coupling energy sources and morphologies. The building then would be analyzed for the amount of energy it requires and uses, increase or decrease the flow of energy through it and correspond within the neighborhood, creating a new sequence of morphological relations. . Looking into further investigation of the project, that could become a very complex system of high level of information, morphological relations, spatial and material relations, as a long term research it would potentially contribute to the evolution of intelligence in the cities. However for this dissertation my personal interest was mostly focused on the energy-ecological aspects of the growing settlement.
So far, the typologies were optimized for the minimization of heat loss, precipitation drainage and surface to area ratios. That does make a lot of sense as a respond to the Site physical context and partially address the
181 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
NAPAK ARUNANONDCHAI
Its been more than 270,000 years, naturally the earth changes and brings new environmental condition. Different regions around the world changes differently according to different condition in different area. How the city grows became a very interesting question to me, as every single city is adapted according to many factors, mainly influences from people living in it. Such that, environmental condition could be the major domination which early affects people and consequently people
effecting typologies. One of the natural phenomena that people does not pay attention to is the permafrost condition. One of the factors that actually pay highest contributes in the global warming. Who knows how much changes in environment can have effects on architecture. This frozen ground condition was proven to have a very high have an impact on architecture. Not only by structures but also by the people. Proven that through time it is almost impossible for buildings to handle all the changes in environmental condition. Especially in cold climate countries, for examples Russia, structural failures has caused so much problems. Consequences, too expensive to be rebuild. It is a problems that architecture should take responsibility to. In relation to the change in environment, it is mainly affected by increase in global temperatures. Thus animals and plants have much smaller growth range
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than human. Small changes in the environmental condition can leads to the change in growth rates or life span. Shifting in biomes towards the North therefore causing species and animals migration together with the environment. This changes the whole ecology system in the area. Some land animals such as sheep that could not cross the river when they already reach the limits of migration at the North coast can be die out. Extinction of animals can happen in a short periods of time as if they could not migrate. However, animals and plants live easily as if there is sufficient food, unlike humans, to build a whole new city together with moving people to the new area is crucial. The impact of the changes in the earth surface also very interesting to me. Since now there is no flying architecture yet, buildings are still relying on the ground condition. Such that to build a high rise building, a deep foundation is need to make sure it is anchor to the strong soil layer. On the other hand, soil condition is not always the same through out the whole earth surface. As same as weather zoning, ground condition varies in stiffness and moisture content. In Siberia, almost the whole ground composed of freezing water which thaws and freeze with the change in ground temperatures. This lot of water penetrates in between small gaps of the soil deep down underground. In some area it could be more than 500 meters. This
way it is almost impossible for anything to build on. However, in the past people still build a building on top without knowing that it will fail because of the global warming issue. Unreliable ground condition as same as another unpredictable changes in climate caused an effect on architectures, especially forms and materials. Interestingly, the construction technologies is being developed at all time, open possibility to build almost every form of buildings. Taking this as a benefit, form of the building can be more optimized. Its is also unbelievable that long lost plant species underneath the permafrost can also be born again. With this natural phenomena, changes in eco system has a positive impact in the taiga area. The whole ecosystem can change from the area that is almost impossible to live into the area that rich in natural resources, in addition, a whole new ecosystem can form in the new area when all the requires conditions is met. Such as growth temperature range, soil condition, water availability, etc. There are many effecting factors and needs for a city to survive, including food, shelters, economics, activities, and medicine. However, architecture wise, buildings aggregation and typologies in both urban scale and building scale also takes an important roles. More of my personal interest is how people can
survived and adapted according to the changes in the environment, given that how a city can grow in such an extreme environmental condition which is difficult to survive. In the dissertation project, the city grows within the main idea of sustaining itself with amount of energy we can harvest. Assumption based calculations were carried out as an input to control the growth of the city. Also the area required for different functions, such as agriculture, livestock, and inhabitation. This can easily be separate into two main parts, the harvesters areas which harvest and provide energy, and the energy consumer, such as inhabitation and partially agriculture zones. The city growth was controlled based on numbers from the calculations to make sure the energy that is being consume is less than the amount of energy harvester can harvest. Moreover, the building aggregation is also based on a logic of making sure every house have easy access to the energy storages and to the other house. In addition, typologies introduced in our system is a typology for two, four, and six people, as a results from the cellular automata city growth experiments. The typologies are individually optimized to minimize the heat loss with the volume to surface ratio. Energy needing for heating systems, agricultural process, and many other operations are also included in the calculations.
183 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
Over a periods of time, in the area under the built structures, when the permafrost finished melting and no energy left, together with the limitation of energy transportation distance, the city will die out. However, in the next 100 years it is certain that, technologies can be unexpectedly develop to sustain the city a little longer. However, putting people together is not substantial for creating a desirable living environment. Interactions and relations between different people are also important because on the selected site, specific group of migrants are mostly from siberia which has their own strong cultures. Looking into a deeper level of a city, especially in our designed city. Yet the city is designed to create a living spaces for migrating people with complete food, energy, and economics or agricultural system and networks. On the other hand it raises the questions of social network within it, since the scale of the city is so small containing small number of population. At this very small scales city, social network become significantly important. However, as a reflection to my dissertation project, the designed aggregation of houses can be more site and person specific if adding another layers of social interactions. Such that looking deeper into local people behavior and how people interacts could help in term of designing specifically for specific groups of migrating people.
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Different countries around the world have their unique cultures and activities. Specific activities mostly influences by the nature. For instance, how people live and eat is depending on the resource available around the area and how people dress depend on the climate. Such that on our site, near Yakutsk in Siberia, people live in the past as a hunter hunting moose as it is the most common animals found on site. As same as reindeers and wild rabbits, mainly use as a livestock for living. Those animals are widely found and highly populated in the site area, more over, almost every parts of the moose can be consume as a food. This kind of cultures or activities is actually crucial to create a city because the city, including buildings aggregation, typologies, and main urban planning should also correspond to how people actually live. Such that the computational network analysis should only be partially use in the design together with the other half containing information of how people in the area move through spaces which might be different in different area. As a conclusion, there are few points that has not yet been concern in the design, however, included in the further development which I think it can be more interesting and more specific to the site, such as cultures and how it is effected by the change of global warming and earth condition.
NAPHAT CHONGRATANAKUL
The algorithm that we wrote for the project is based on the researches and observation of the site that we made through out in Permafrost Pioneering. However, the algorithm can undergo many improvement in many different aspect. In the dissertation, we have investigate different algorithms and the applications of them on the design, in which are carefully chosen and carry out. Since the project are dealing mostly with proportion of different unit and its spatial relationship, we choose Cellular Automata as a main algorithm that designate different zone and function over time. But instead of using final outcome of the last generation, every generation are being used and analyze, reflecting on the next generation. The result was quite astonishing where we can sustain a settlement for over one hundred years Nonetheless, the algorithm seem to have multiple flaw when translate it in to reality. In a typical Cellular Automata operation, to get the optimum result, genetic algorithm will be use in order to find the most sensible set of rules that give the best result of the last generation according to different fitness criteria. However, this method cannot be use in our circumstance where the outcome of all generations are being utilize. In addition, there cannot be a single ideal set of rules in our project, since it’s all depend on where the stating point is and what is the surrounding
terrain are. Each rules may fit in to a particular terrain and others might not. The method that we use in the project is to analyze at the end of every generations and determine new rules from those evaluation. Rules are changing in relation to different situation. An example from the project, if there are too much energy stored in the system, less energy harvesters will be deploy and more inhabitation zone will appear. On the other hand, when the energy in the storage got decreases to a certain point, more harvester will be deploy and no inhabitation will be construct. Therefore, the aim of using the Cellular Automata is to create a system that is smart enough to understand what to do in different situation and try to balance the energy amount to the minimum but enough to sustain the whole settlement and leave some for potential growth. However, there are multiple inaccurate value that has been spotted in the system, one of them is the energy value that are being harvested. In the research we found out that the permafrost will be thawing at 10.2m per year, where it is done in an ideal condition, meaning that the rate of methane release from the frozen ground is at its maximum, considering all the sunlight hours at the site. In reality the release rate is depend on many other factors, such as how much water are covering the ground and trap the sun energy on the surface, or the cloud
185 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
coverage of different time. It is difficult to impossible make the an accurate assumption on the methane release rate. The only way to obtain the genuine data is to set up a a physical experiment at the site, where few harvesters will be deploy over permafrost and amount of captured methane are measured. In the growth pattern simulation, population in the new emerging settlement are increases solely based on energy stored in the system, but that number will be realistic only if we assume that people start to move in right after there is a permission to build more inhabitation unit. People need a reason to move to these new territory, and ultimately enhancing their quality of life. To do so, we need to ensure that the settlement has enough resource to persuade them, in this case not only energy resource but economic resource. In order to make the simulation more realistic availability of food, water, agricultural land and economic opportunity would be an additional factors that have to dictate the growth of the population. A person consume at an average of 40 gigajoules per year is assumed in the experiment, this number is being use for the entire calculation. The number based on how much energy is needed to heat up a interior room of 20 m2 for one person in a specific heat insulation value,
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plus extra energy that one will be using for other utility. However, this specific value seem to be inaccurate, it is because people tend to consume more energy while the population are being increase. New activities are often introduce, where it can be seen throughout developing cities around the world. Public infrastructure such as schools, hospital, or social space will be needed. At the same time, as the settlement grow into a city, pedestrian network may no longer be efficient since the distance between one place to another is increasing, public transportation will be needed. Harvesters will also require more pressure to pump methane in to the city center as the distance become greater. Because of those reasons the energy consumption rate will exponentially increase. Eventually, the system might not be suitable to sustain a large number of people but are fit to facilitate a smaller systems in multiple locations working together simultaneously. In this case, simulation on a multi-centric systems scale will be an interesting investigation to understand a global resources and connectivity network between them. Additionally, the result of the global simulation can influence the rules for local systems growth pattern. In the project we tried to find the most suitable starting point where it is a balance between a location that is close enough to the river for wetland plantation to grow,
close enough to a dry land where animal can be raise and terrain that angle toward the sun as much as possible to increase the thawing rate. But if we have multiple city system that is not too distant from each other there might be an opportunity for resources exchanging where one city is focusing on a production of one thing. For example, food production city, pharmaceutical plants industry city, construction material supply city(in this case wood production), or methane farming city with low inhabitation. In this way It may be more productive than putting all the responsibility within a singular system since different terrain contain different natural features. Due to the moisten soil around the site, it is difficult to establish a ground transportation system that link between those cities. Settlement will be required to emerge within a certain distance from the river network as it will become a crucial channel that provide the cargo exchange and people transportation route. This will also be a primary connection to other existing cities around the site, such as Yakutsk. However, river will be frozen in winter, so ice breaker ship will be the main type of transportation. Despite those inaccuracy on the input value for the simulation, the experiment from the algorithm show that
the city will be able to sustain itself for more than one hundred years, but what will happen after that. There are two options worth while investigating, one of them is to abandon the city after it’s getting too large, then find new multiple site that still have permafrost ground to establish new settlements. In this option, People need to migrate to a new settlement, but still have the same free energy that run the city. The second option is to sustain the same settlement with methane energy that are harvested from other site. The advantage of this is inhabitation does not need to move to a new location. However, it will require more energy for long distance energy transferring system. But as the arctic are getting warmer, possibly up to 16oc in the next 100 years, permafrost might be completely thaws and alternative energy might be able to implemented. This two options can be investigated from further development of the current algorithm version. Algorithm are made for us to have an ability to simulate the real situation. However, we can only see the result as a prediction, since we cannot include every single factors into the algorithm. In the construction phase of the project, we can use the same system with a real input. As the settlement grow in the real situation, the more preside data can be use to stimulate the simulation.
187 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
8
CHAPTER
bibliography
BIBLIOGRAPHY TEXTS AND QUOTES
DOMAIN CHAPTER
1.
IUCN, UNEP, WWF (1991). ‘Definitions of sustainability’. Web: http://www.ecifm.rdg.ac.uk/definitions.htm. p.19
2.
B. Baldwin (2012). Tundra conditions. Web: http://prezi.com/k0aauqhbwv1h/yakutsk-siberia/ p.21
3.
Royal Society A, M. G. Sanderson, D. L. Hemming
and
R. A. Betts (2010). ‘Regional temperature and precipitation changes under high-end
(>40C) global warming’. p.22 4.
Royal Society A, R.Warren (2010). ‘The role of interaction in a world implementing adaptation and migration solutions to the climate change’. p.23
5.
The World Bank (2012). ‘Turn down the heat’. p.25
6.
A. Brouchkov (2003). Comparison of Methane Content in Upper Permafrost of Eastern Siberia and Alaska. Web: http://www.academia. edu/3278961/Comparison_of_Methane_Content_in_Upper_Permafrost_of_Eastern_Siberia_and_Alaska p.26
7.
J.A. Ross (2002). Patterned ground from frost-heaving in polar desert. Web: http://jennyross.com/gallery/v/gallery/Arctic/Arctic-Misc/JERoss_ ArcticMisc-39.jpg.html p.28
8.
Wikipedia the free encyclopedia (year NA). Silene stenophylla Web: http://en.wikipedia.org/wiki/Silene_stenophylla p.29
9.
Wikipedia the free encyclopedia (year NA). Silene stenophylla Web: http://en.wikipedia.org/wiki/Silene_stenophylla p.29
10.
O. Anisimov (2007). ‘Potential feedback of thawing permafrost to the global climate system through methane emission’. p.30
11.
Wikipedia the free encyclopedia (year NA). ‘Taiga’. Web: http://en.wikipedia.org/wiki/Taiga p.33
12.
Wikipedia the free encyclopedia (year NA). ‘Hibernation’. Web: http://en.wikipedia.org/wiki/Hibernation p.33
13.
Wikipedia the free encyclopedia (year NA). ‘Taiga’. Web: http://en.wikipedia.org/wiki/Taiga p.34
14.
Department of Botany, Burdwan University, Burdwan-713104, India (2007). ‘Useful plants of wetlands in nadia district, west bengal goutam bala and ambarish mukherjee’ p.35
190 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
15.
S. Brown, Northwest territories association of communities (2011). ‘Thawing permafrost sinks buildings, hikes costs in North’. Web: http://www. cbc.ca/news/canada/story/2011/11/16/north-big-fix-permafrost.html p.39
16.
Leeser Architects (2007). ‘Mammoth and Permafrost Museum’. Web: http://www.leeser.com/work/cultural/wmpm/ p.40
17.
Wikipedia the free encyclopedia (year NA). ‘Tensairity’. Web: http://en.wikipedia.org/wiki/Tensairity p.43
18.
Shigeru Ban Architects (2012). ‘Building for Omega’. Web: http://www.shigerubanarchitects.com/works.html#in-progress p.44
19.
Wikipedia the free encyclopedia (year NA). ‘Taiga, Coniferous forests’. Web: http://en.wikipedia.org/wiki/Taiga p.43
20.
A. Brouchkov (2003). Comparison of Methane Content in Upper Permafrost of Eastern Siberia and Alaska. Web: http://www.academia. edu/3278961/Comparison_of_Methane_Content_in_Upper_Permafrost_of_Eastern_Siberia_and_Alaska p.46
21.
US department of energy (2011). ‘Energy Resource Potential of Methane Hydrate’. p.46
22.
Wikipedia the free encyclopedia (year NA). ‘Methane Clathrate. Natural gas hydrates versus liquified natural gas in transportation’. Web: http://
en.wikipedia.org/wiki/Methane_clathrate p.47
METHODS CHAPTER
1.
Studio NU (2012). ‘SONIC 4 GH’. Web: http://www.studionu.net/ceed3/?p=2342 p.58
2.
Wikipedia the free encyclopedia (year NA). ‘Cellular Automata’. Web: http://en.wikipedia.org/wiki/Cellular_automaton p.60
191 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
BIBLIOGRAPHY TEXTS AND QUOTES
3.
Wikipedia the free encyclopedia (year NA). ‘Pneumatics’. Web: http://en.wikipedia.org/wiki/Pneumatics p.62
4.
Wikipedia the free encyclopedia (year NA). ‘Circle Packing’. Web: http://en.wikipedia.org/wiki/Circle_packing p.63
5.
Wikipedia the free encyclopedia (year NA). ‘Delaunay Triangulation’. Web: http://en.wikipedia.org/wiki/Delaunay_triangulation p.64
6.
Wikipedia the free encyclopedia (year NA). ‘Voronoi disgram’. Web: http://en.wikipedia.org/wiki/Voronoi_diagram p.64
7.
Wikipedia the free encyclopedia (year NA). ‘Minimum spanning tree’. Web: http://en.wikipedia.org/wiki/Minimum_spanning_tree p.65
RESEARCH DEVELOPMENT CHAPTER
1.
Permafrost In
a
Warming World ‘The Effect of Climate Change on Permafrost’. Web: http://www.wunderground.com/resources/climate/
melting_permafrost.asp. p.71 2.
Yakutsk ‘Climate’. Web: http://en.wikipedia.org/wiki/Yakutsk p.71
3.
Lighter than air ‘Usage as lifting gas’. Web: http://en.wikipedia.org/wiki/Lighter_than_air p.71
4.
Overview of Green House Gases ‘Methane Emissions’. Web: http://epa.gov/climatechange/ghgemissions/gases/ch4.html p.71
5.
Methane ‘Uses (Natural Gas)’. Web: http://en.wikipedia.org/wiki/Methane p.71
6.
IPCC Arctic GCM scenarios (year NA). Web: http://igloo.atmos.uiuc.edu/IPCC/ p.72
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7.
Thawing Permafrost Sinks Buildings, hikes costs in North ‘Northern Communities turn to innonative solutions to tackle thawing soil’. Laura Write, CBC News. (2011) Web: http://www.cbc.ca/news/canada/north/story/2011/11/16/north-big-fix-permafrost.html p.72
8.
Eden Project (2001). Web: http://en.wikipedia.org/wiki/Eden_Project p.76
9.
Permafrost, http://science.jrank.org/pages/5107/Permafrost.html p.76
10.
Comparison of Methane Content in upper Permafrost of Eastern Siberia and Alaska, American Geophysical Union (2003), Brouchkov A; fukuda M. Web: http://adsabs.harvard.edu/abs/2003AGUFM.C21B0812B p.84
DESIGN DEVELOPMENT CHAPTER
1.
Yakutsk (2010). Web: http://en.wikipedia.org/wiki/Yakutsk p.20
2.
Viluisk (). Web: p.20
3.
Oyamyakon (2010). Web: http://en.wikipedia.org/wiki/Oymyakon p.20
4.
Etfe properties (NA). Web: http://en.wikipedia.org/wiki/ETFE p.26
5.
Heating system guidance Web: http://www.b-es.org/sustainability/air-source-heat-pump-guidance/
193 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
BIBLIOGRAPHY IMAGES
DOMAIN CHAPTER
1.
River Eyjafjarรฐarรก
and the reflections.
Jon Ingi Caesarsson Jsn (2013). http://www.flickr.com/photos/53151484@N00/8409465822/in/
photolist-dP7Hd3-cKgaph-fPepmo-aUxnXX-2XPEuT-fgMiDg-deSnmN-8rVZEA-dxAFsL-93LMV6-7Q3S64-4cp6xk-ej5ALJ-eQtnzg73t78w-73p8nR-8Vb2Mw-6VmxFh-4Fb8WZ-a6yScx-bCiDPz-bo8cRW-7ppk-4tmQPr-9NLjfY-dRaqyz-7E9j6t-djby8q-aszfcd-8rLRomdRSLFF-dRAv4P-bzNjH8-f6CCML. p.8 2.
Yakutsk. http://www.discussionworldforum.com/forum/showthread.php?t=2551 p.16
3.
Tundra landscape. http://darkspenthouse.punbb-hosting.com/viewtopic.php?id=346 p.20
4.
Worlds Biome. http://en.wikipedia.org/wiki/Biome p.21
5.
4o Turn Down the Heat, Why a 4oC Warmer World Must be Avoided, A Report for The World Bank by Potsdam Institute for Climate Impact Research and Climate Analytics. p.22, p.23, p.24
6.
Frost heaving:
http://jennyross.com/gallery/v/gallery/Arctic/Arctic-Misc/JERoss_ArcticMisc-39.jpg.htm p. 28 -29
7.
Potential feedback of thawing permafrost to the global climate system through methane emission. O A Anisimov. State Hydrological Institute, St. Petersburg, Russia http://www.listofimages.com/banff-national-park-mountain-river-alberta-calgary-tree-nature.html p.30
8.
Moutain Skierfe,Sweden. Martin Kramer. http://www.artflakes.com/en/products/sarek-rapadalen p.31
9.
Banff National Park, Canada. http://www.wallpapersat.com/wallpaper/banff-national-park-canada.html p.32
10.
Moose:
11.
Reindeer:
12.
Rabbit:
13.
Wetland plants: http://www.academia.edu/1644313/USEFUL_PLANTS_OF_WETLANDS_IN_NADIA_DISTRICT_WEST_BENGAL_GOUTAM_
http://www.britannica.com/blogs/2011/10/shadow-moose-wyomings-herds-decline/ p.34 http://www.animalpictures123.org/pictures/reindeer/ p.34 http://www.talkphotography.co.uk/forums/showthread.php?t=26336 p.34
BALA_and_AMBARISH_MUKHERJE p.35
194 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
14.
Invik Town (1961). Thttp://jproc.ca/rrp/inuvik.html p.38
15.
Permafrost Technology Foundation, Design Manual for New Foundations on Permafrost, September 2000, Page 64. p.38
16.
Mammoth Museum, Leeser Architect, Yakutsk (2007). http://www.treehugger.com/sustainable-product-design/its-a-mammoth-museum-byleeser-for-yakutsk.html p.40, p.41
17.
Tensairity Bridge, France. http://www.dw.de/image/0,,4530843_4,00.jpg p.42
18.
Headquarters for Swatch and Omega by Shigeru Ban (2013). http://www.dezeen.com/tag/wood/ p.44
19.
Thawing permafrost SETH BORENSTEIN (2011). http://news.yahoo.com/thawing-permafrost-vents-gases-worsen-warming-180138926.html p.51
RESEARCH DEVELOPMENT CHAPTER
1.
Introduction to Heat Transfer(2003), Frank P. Incropera, David P. Dewitt, Theodore L. Bergman, Adrienne S Lavine. Publicher: John Willy &Sons p.77
2.
Permafrost, http://science.jrank.org/pages/5107/Permafrost.html p.85
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9
CHAPTER
appendicies
APPENDICIES
The following chapter contains the information that for some reason wasn’t considered to be relevant enough to be shown in the body of the book, but still is crucial for this dissertation. It contains the main written script and extra testsevaluations for Cellular Automata tool. Grasshopper definitions and python mini-scripts are not shown in the chapter as are considered to be a basic knowledge information
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APPENDICIES PROCESSING SCRIPT CA
// Permafront City Calculation import oscP5.*; import netP5.*; OscP5 oscP5; NetAddress myRemoteLocation; Table table; // GLOBAL VARIABLE int sx, sy, zoom; int[][][] melter; int[][][] house; int[][][] houseTwo; int[][][] houseThree; int[][][] houseFour; int[][][] houseFive; int[][][] xHouse; int[][][] methaneStorage; int[][][] methaneSupplier; int[][][] activeSupplier; int[][][] agriculture; int[][][] animal; int[][][] waterUnit; boolean run; int year = 0; float energy=0; float energyA=0; PImage img; PImage img2; PImage img3; PImage img4; PImage img5; PImage img6; float graphX = 0; float graphY = 0; float energyP = 0; float[] xpos = new float[4000]; float[] ypos = new float[4000]; float[] xposCH4 = new float[4000]; float[] yposCH4 = new float[4000]; float graphYCH4 = 0; float energyGain = 0; float energyUse = 0; float energyDifferent = 0;
200 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
float energyGainA = 0; float energyUseA = 0; float energyDifferentA = 0; int[][][] melterAge; int[][][] houseAge; int[][][] houseTwoAge; int[][][] houseThreeAge; int[][][] houseFourAge; int oneHouseNumberX = 0; int twoHouseNumberX = 0; int threeHouseNumberX = 0; int fourHouseNumberX = 0; int fiveHouseNumberX = 0; //Connection to Grasshopper IntList storageX; IntList storageY; IntList melterX; IntList melterY; IntList supplierX; IntList supplierY; IntList houseOneX; IntList houseOneY; IntList houseTwoX; IntList houseTwoY; IntList houseThreeX; IntList houseThreeY; IntList houseFourX; IntList houseFourY; IntList houseFiveX; IntList houseFiveY; float initialMelterRate; float CinitialMelterRate; float melterBuildingRate;
//=============== VOID SETUP =============== void setup() { background(255); smooth(); zoom = 8; sx = int(1920/zoom); sy = int(1080/zoom); table = new Table(); OscProperties myProperties = new OscProperties(); myProperties.setDatagramSize(100000); myProperties.setListeningPort(12001);
oscP5 = new OscP5(this, myProperties); myRemoteLocation = new NetAddress(“10.15.171.241”, 12000); table.addColumn(“Year”, Table.INT); table.addColumn(“Month”, Table.INT); table.addColumn(“Energy”, Table.INT); table.addColumn(“Energy Havested”, Table.INT); table.addColumn(“Energy Used”, Table.INT); table.addColumn(“Energy Difference”, Table.INT); table.addColumn(“Cubic Meter of Methane”, Table.INT);
// TYPOLOGIES melter = new int[sx][sy][3]; house = new int[sx][sy][3]; houseTwo = new int [sx][sy][3]; houseThree = new int[sx][sy][3]; houseFour = new int[sx][sy][3]; houseFive = new int[sx][sy][3]; xHouse = new int[sx][sy][3]; methaneStorage = new int[sx][sy][3]; methaneSupplier = new int[sx][sy][3]; activeSupplier = new int[sx][sy][3]; agriculture = new int[sx][sy][3]; animal = new int[sx][sy][3]; waterUnit = new int[sx][sy][3]; // AGE & RUN melterAge = new int[sx][sy][3]; houseAge = new int[sx][sy][3]; houseTwoAge = new int[sx][sy][3]; houseThreeAge = new int[sx][sy][3]; houseFourAge = new int[sx][sy][3]; run = false; size(sx*zoom, sy*zoom); frameRate(3); //noStroke(); fill(200); background(255); img = loadImage(“TERRAIN READING.jpg”); img2 = loadImage(“TERRAIN BACKGROUND.jpg”); img3 = loadImage(“TERRAIN FLOWS.jpg”); img4 = loadImage(“TERRAIN FLOWS DISPLAY.jpg”); img5 = loadImage(“TERRAIN WETLAND.jpg”); img6 = loadImage(“TERRAIN WETLAND HIDEF.jpg”); for (int i = 0; i < xpos.length; i++) { xpos[i] = 0;
}
ypos[i] = 0;
//image(img2, 0, 0); } float initial = 12; float meltingTime = 0; //=============== VOID DRAW =============== void draw() { noStroke(); storageX = new IntList(); storageY = new IntList(); melterX = new IntList(); melterY = new IntList(); supplierX = new IntList(); supplierY = new IntList(); houseOneX = new IntList(); houseOneY = new IntList(); houseTwoX = new IntList(); houseTwoY = new IntList(); houseThreeX = new IntList(); houseThreeY = new IntList(); houseFourX = new IntList(); houseFourY = new IntList(); houseFiveX = new IntList(); houseFiveY = new IntList(); if (run) { year++; if (year>12*5) { initial = initial+0.7; } } initial = constrain(initial, 12, 336); image(img2, 0, 0, 1920, 1080); if (keyPressed) { if ( key == ‘f’ ) { image(img4, 0, 0, 1920, 1080); } } if (keyPressed) { if ( key == ‘a’ ) { image(img6, 0, 0, 1920, 1080); } } textAlign(LEFT);
201 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
textSize(15); fill(100); text(“initial”+initial, 5, 500);
}
scale(zoom);
if (houseTwo[x][y][1] == 1) { if (run) { houseTwoAge[x][y][0]++; } fill(171, 202, 233); ellipse(x, y, 1, 1); houseThree[x][y][0] = 0; houseTwo[x][y][0] = 1; house[x][y][0] = 0; melter[x][y][0] = 0; xHouse[x][y][0] = 0; methaneStorage[x][y][0]=0; }
for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { img.loadPixels(); int loc= x+y*sx; float red = red(img.pixels[loc]); float blue = blue(img.pixels[loc]); meltingTime = map(red, 255, 0, initial, 400); // COLOR MELTER if (melter[x][y][1] == 1) { fill(143, 244, 189); ellipse(x, y, 1, 1); melter[x][y][0] = 1; house[x][y][0] = 0; //IF AGE MORE THAN “X” THEN THERE IS A POSSIBILITY THAT IT WILL TRUN INTO A HOUSE if (run) { melterAge[x][y][0]++; } if (melterAge[x][y][0] > meltingTime) { xHouse[x][y][0] = 1; melter[x][y][0] = 0; } } if (xHouse[x][y][1]==1) { xHouse[x][y][0]=1; } // COLOR HOUSE if (house[x][y][1] == 1) { if (run) { houseAge[x][y][0]++; } fill(191, 222, 253); ellipse(x, y, 1, 1); houseTwo[x][y][0] = 0; house[x][y][0] = 1; melter[x][y][0] = 0; xHouse[x][y][0] = 0; methaneStorage[x][y][0] = 0; agriculture[x][y][0] = 0;
202 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
//COLOR HOUSE 2
//COLOR HOUSE 3 if (houseThree[x][y][1] == 1) { if (run) { houseThreeAge[x][y][0]++; } fill(151, 182, 213); ellipse(x, y, 1, 1); houseThree[x][y][0] = 1; houseTwo[x][y][0] = 0; house[x][y][0] = 0; melter[x][y][0] = 0; xHouse[x][y][0] = 0; methaneStorage[x][y][0]=0; } //COLOR HOUSE 4 if (houseFour[x][y][1] == 1) { if (run) { houseFourAge[x][y][0]++; } fill(131, 162, 193); ellipse(x, y, 1, 1); houseFour[x][y][0] = 1; houseThree[x][y][0] = 0; houseTwo[x][y][0] = 0; house[x][y][0] = 0; melter[x][y][0] = 0; xHouse[x][y][0] = 0; methaneStorage[x][y][0]=0; }
//COLOR HOUSE 5
}
if (houseFive[x][y][1] == 1) { fill(121, 142, 173); ellipse(x, y, 1, 1); houseFive[x][y][0] = 1; houseFour[x][y][0] = 0; houseThree[x][y][0] = 0; houseTwo[x][y][0] = 0; house[x][y][0] = 0; melter[x][y][0] = 0; xHouse[x][y][0] = 0; methaneStorage[x][y][0]=0; }
// COLOR AGRICULTURE if (agriculture[x][y][1] == 1) { fill(143, 244, 189, 100); ellipse(x, y, 1, 1); agriculture[x][y][0] = 1; } // COLOR WATER UNIT
// COLOR METHANE STORAGE if (methaneStorage[x][y][1] == 1) { fill(255, 200, 69); ellipse(x, y, 1, 1); methaneStorage[x][y][0]=1; house[x][y][0] = 0; xHouse[x][y][0] = 0; melter[x][y][0] = 0; } // COLOR METHANE SUPPLIER if (methaneSupplier[x][y][1] == 1) { fill(255, 230, 99); ellipse(x, y, 1, 1); methaneStorage[x][y][0]=0; methaneSupplier[x][y][0]=1; house[x][y][0] = 0; xHouse[x][y][0] = 0; melter[x][y][0] = 0; } // NO MELTER if (melter[x][y][1] == 0) { noFill(); ellipse(x, y, 1, 1); melter[x][y][0]=0; } //COLOR ANIMAL FARM if (animal[x][y][1] == 1) { fill(208, 193, 162, 120); ellipse(x, y, 1, 1);
animal[x][y][0] = 1;
}
}
if (waterUnit[x][y][1] == 1) { fill(0, 0, 0, 0); ellipse(x, y, 1, 1); waterUnit[x][y][0] = 1; }
// RULE if ( run ) { for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { //IMAGE PROCESSING SUNLIGHT img.loadPixels(); int loc= x+y*sx; float red = red(img.pixels[loc]); float blue = blue(img.pixels[loc]); //IMAGE PROCESIING FLOW img3.loadPixels(); int loc2= x+y*sx; float flow = red(img3.pixels[loc2]); //IMAGE PROCESIING ALTITUDE img5.loadPixels(); int loc3= x+y*sx; float level = red(img5.pixels[loc3]); flow = constrain(flow, 0, 255); // convert to neighbor number float numberOfMelterNeighbor = map(red, 255, 0, 2, 6); float randomTimeLevel = map(level, 150, 0, 0, 30); float randomTimeLight = map(red, 255, 0, 0, 40);
203 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
float melterBuildingRateX = map(energyDifferentA, -100, 1000, 0, 40); float CmelterBuildingRateX = constrain(melterBuildingRateX, 0, 40); initialMelterRate= map(year, 0, 180, 3, 1); CinitialMelterRate= constrain(initialMelterRate, 1, 3); int melterCount1 = melterNeighbor1(x, y); int melterCount2 = melterNeighbor2(x, y); int houseCount = houseNeighbor(x, y); int houseTwoCount = (houseTwoNeighbor(x, y))*2; int houseThreeCount = (houseThreeNeighbor(x, y))*3; int houseFourCount = (houseFourNeighbor(x, y))*4; int methaneStorageCount1 = methaneStorageNeighbor1(x, y); int methaneStorageCount2 = methaneStorageNeighbor2(x, y); int waterUnitCount1 = waterUnitNeighbor(x, y); int methaneSupplierCount = methaneSupplierNeighbor(x, y); int houseTotal = houseCount + houseTwoCount + houseThreeCount + houseFourCount; //float randomMelter = random(randomTime); melterBuildingRate= random((CmelterBuildingRateX+randomTimeLevel+randomTimeLight)/CinitialMelterRate); if (melter[x][y][0] == 0 && melterBuildingRate < 1 ) { if (melterCount1 > numberOfMelterNeighbor && blue <= red+50 ) { melter[x][y][1] = 1; } } // Water Unit if (melter[x][y][0] == 0 && blue > red+20) { if (melterCount1 > 2) { waterUnit[x][y][1] = 1; melter[x][y][1] = 0; } } float randomWater = random(10); if (melter[x][y][0] == 0 && blue > red+15) { if (waterUnitCount1 > 1 && randomWater < 1) { waterUnit[x][y][1] = 1; } } if (melter[x][y][0] == 0 && blue <= red+15 && agriculture[x][y][0] == 0) { if (waterUnitCount1 > 2) { melter[x][y][1] = 1; } } // Mature Melter float houseBuildingRate = map(energyDifferentA, 1000, 0, 2, 50);
204 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
houseBuildingRate = constrain(houseBuildingRate, 2, 50); float randomHouse = random(houseBuildingRate); if (xHouse[x][y][0] == 1) { //House if (methaneSupplierCount > 0 && randomHouse <1 && red > 200 && flow>220 && level<160 && energyDifferentA > 20) { melter[x][y][1] = 0; house[x][y][1] = 1; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; animal[x][y][1] = 0; xHouse[x][y][1] = 0; houseTwo[x][y][1] = 0; } // Animal Farm if (methaneSupplierCount == 0 && red<=230) { melter[x][y][1] = 0; house[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; xHouse[x][y][1] = 0; animal[x][y][1] = 1; } // Agriculture (normal condition) if (methaneSupplierCount == 0 && red>230) { melter[x][y][1] = 0; house[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 1; animal[x][y][1] = 0; xHouse[x][y][1] = 0; } // Agriculture (with storage) if (methaneSupplierCount > 0 && randomHouse >= 1 && red>220) { melter[x][y][1] = 0; house[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 1; animal[x][y][1] = 0; xHouse[x][y][1] = 1; } // Animal Farm (with storage) if (methaneSupplierCount > 0 && randomHouse >= 1 && red<=220) { melter[x][y][1] = 0; house[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; animal[x][y][1] = 1; xHouse[x][y][1] = 1; }
} //Upgrade Rate float house2BuildingRate = map(energyDifferentA, 1000, 0, 1, 50); house2BuildingRate = constrain(house2BuildingRate, 1, 50); float houseTwoRate = random(house2BuildingRate); // House Two if (house[x][y][0] == 1 && houseAge[x][y][0] > 12 && energyDifferentA > 20 && twoHouseNumberX <= oneHouseNumberX/2 && houseTwoRate < 1 && houseTotal > 4) { houseTwo[x][y][1] = 1; house[x][y][1] = 0; melter[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; xHouse[x][y][1] = 0; } // House Three if (houseTwo[x][y][0] == 1 && houseTwoAge[x][y][0] > 24 && energyDifferentA > 30 && threeHouseNumberX <= twoHouseNumberX/2 && houseTwoRate < 1 && houseTotal > 8) { houseThree[x][y][1] = 1; houseTwo[x][y][1] = 0; house[x][y][1] = 0; melter[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; xHouse[x][y][1] = 0; } // House Four if (houseThree[x][y][0] == 1 && houseThreeAge[x][y][0] > 36 && energyDifferentA > 40 && fourHouseNumberX <= threeHouseNumberX/2 && houseTwoRate < 1 && houseTotal > 14) { houseFour[x][y][1] = 1; houseThree[x][y][1] = 0; houseTwo[x][y][1] = 0; house[x][y][1] = 0; melter[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; xHouse[x][y][1] = 0; } // House Five if (houseFour[x][y][0] == 1 && houseThreeAge[x][y][0] > 48 && energyDifferentA > 50 && fiveHouseNumberX <= fourHouseNumberX/2 && houseTwoRate < 1 && houseTotal > 22) { houseFive[x][y][1] = 1; houseFour[x][y][1] = 0; houseThree[x][y][1] = 0; houseTwo[x][y][1] = 0;
}
house[x][y][1] = 0; melter[x][y][1] = 0; methaneStorage[x][y][1] = 0; agriculture[x][y][1] = 0; xHouse[x][y][1] = 0;
// TURNING MELTER TO METHANE STORAGE if (melter[x][y][0] == 1) { float storageRandom = random (400); if (melterCount2 >= 5 && methaneStorageCount2 == 0 && storageRandom < 1) { melter[x][y][1] = 0; house[x][y][1] = 0; methaneStorage[x][y][1] = 1; } } // TURNING METHANE STORAGE TO METHANE SUPPLIER if (methaneStorage[x][y][0] == 1) { if (melterCount1 <= 1 && red > 200 && flow > 220 && level < 160) { methaneStorage[x][y][1] = 0; methaneStorage[x][y][0] = 0; methaneSupplier[x][y][1] = 1; methaneSupplier[x][y][0] = 1; melter[x][y][1] = 0; } } // TURNING METHANE STORAGE TO AGRICULTURE if (methaneStorage[x][y][0] == 1) { if (melterCount1 <= 1 &&(red <= 200 || flow <= 220 || level >= 160)) { if (red > 220) { methaneStorage[x][y][1] = 0; methaneSupplier[x][y][1] = 0; agriculture[x][y][1] = 1; melter[x][y][1] = 0; } if (red <= 220) { methaneStorage[x][y][1] = 0; methaneSupplier[x][y][1] = 0; animal[x][y][1] = 1; melter[x][y][1] = 0; } } } if (methaneSupplier[x][y][0]==1){ if (houseTotal > 0){
205 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
}
}
}
}
}
activeSupplier[x][y][1]=1;
// UNIT COUNT float melterEnergy=0; int melterNumber=0; int storageNumber=0; int oneHouseNumber=0; int twoHouseNumber=0; int threeHouseNumber=0; int fourHouseNumber=0; int fiveHouseNumber=0; int agricultureNumber=0; int methaneSupplierNumber=0; for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (melter[x][y][1]==1) { img.loadPixels(); int loc= x+y*sx; float red = red(img.pixels [loc]); int yearY = -60; float yearA = red/255; float meltingTime = 300+(yearA*yearY); melterNumber++; float melterEnergyX = (224/meltingTime); melterEnergyX = constrain(melterEnergyX, 0, 1); melterEnergy = melterEnergy + (melterEnergyX);
} if (methaneStorage[x][y][1]==1) { storageNumber++; } if (house[x][y][1]==1) { oneHouseNumber++; } if (houseTwo[x][y][1]==1) { twoHouseNumber++; } if (houseThree[x][y][1]==1) { threeHouseNumber++; } if (houseFour[x][y][1]==1) { fourHouseNumber++; } if (houseFive[x][y][1]==1) { fiveHouseNumber++;
206 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
}
}
} if (agriculture[x][y][1]==1) { agricultureNumber++; } if (methaneSupplier[x][y][0]==1) { methaneSupplierNumber++; }
// Conversion float energyPerUnit= 40; energyPerUnit= energyPerUnit+0.02252252; int m = 1; int houseNumber= ((oneHouseNumber*1)*m)+((twoHouseNumber*2)*m)+((threeHouseNumber*3)*m)+((fourHouseNumber*4)*m)+((fiveHouseNumber*5)*m); float gainPerHour = energyPerUnit/2227.6; float gainPerMonth = 0; float gainPerMonthAverage = 3.33333333; if (run) { energyP = energy; if (year%12==0) { gainPerMonth = gainPerHour*18.6; } if (year%12==1) { gainPerMonth = gainPerHour*98.0; } if (year%12==2) { gainPerMonth = gainPerHour*232.5; } if (year%12==3) { gainPerMonth = gainPerHour*273.0; } if (year%12==4) { gainPerMonth = gainPerHour*303.8; } if (year%12==5) { gainPerMonth = gainPerHour*333.0; } if (year%12==6) { gainPerMonth = gainPerHour*347.2; } if (year%12==7) { gainPerMonth = gainPerHour*272.8; } if (year%12==8) { gainPerMonth = gainPerHour*174.0; } if (year%12==9) { gainPerMonth = gainPerHour*105.4;
} if (year%12==10) { gainPerMonth = gainPerHour*60.0; } if (year%12==11) { gainPerMonth = gainPerHour*9.3; } energyGain = melterEnergy*gainPerMonth; energyUse = houseNumber*8.3333333333; energy = (energy + energyGain) - energyUse; energyDifferent = energyGain-energyUse; energyGainA = melterEnergy*gainPerMonthAverage; energyUseA = houseNumber*8.3333333333; energyA = (energy + energyGain) - energyUse; energyDifferentA = energyGainA-energyUseA;
} int energyMegajoules = round(energy*1000); int methaneCubicMeter = energyMegajoules/39; int frozenMethane = methaneCubicMeter/160; int textYPos = 10; int textXPos = 1; int storageNumberX=storageNumber; if (storageNumberX==0) { storageNumberX=1; } float methanePerStorageTank = frozenMethane/storageNumberX; //Text Display textAlign(LEFT); textSize(1.5); fill(100); text(“Melter Unit Count: “+ melterNumber, textXPos, textYPos); text(“Methane Storage Unit Count: “+ storageNumber, textXPos, textYPos+3); text(“Population: “+ houseNumber, textXPos, textYPos+6); text(“Residential 1 Unit Count: “+ oneHouseNumber, textXPos+25, textYPos+6); text(“Residential 2 Unit Count: “+ twoHouseNumber, textXPos+50, textYPos+6); text(“Residential 3 Unit Count: “+ threeHouseNumber, textXPos+75, textYPos+6); text(“Residential 4 Unit Count: “+ fourHouseNumber, textXPos+100, textYPos+6); text(“Residential 5 Unit Count: “+ fiveHouseNumber, textXPos+125, textYPos+6); text(“Agricultural Unit Count: “+ agricultureNumber, textXPos, textYPos+9); text(“Year: “+year/12, textXPos, textYPos+12); text(“Month: “+ year%12, textXPos, textYPos+15); text(“Energy: “+round(energy)+” gigajoule”, textXPos, textYPos+18); text(“Methane Per Storage Tank: “+ methanePerStorageTank+” mCube”, tex-
tXPos, textYPos+21); text(“Energy Gain: “+round(energyGain)+” gigajoule”, textXPos, textYPos+24); text(“Energy Use: “+round(energyUse)+” gigajoule”, textXPos, textYPos+27); text(“Energy Different: “+round(energyDifferent)+” gigajoule”, textXPos, textYPos+30); text(“Average Energy Different: “+round(energyDifferentA)+” gigajoule”, textXPos, textYPos+33); text(“Number of Supplier: “+methaneSupplierNumber+” units”, textXPos, textYPos+36); text(“Initial Melter Rate: “+CinitialMelterRate+” units”, textXPos, textYPos+39); textAlign(RIGHT); textSize(6); fill(255, 95, 73); text((year/12)+2013, sx-textXPos, textYPos+4.5); oneHouseNumberX=oneHouseNumber; twoHouseNumberX=twoHouseNumber; threeHouseNumberX=threeHouseNumber; fourHouseNumberX=fourHouseNumber; fiveHouseNumberX=fiveHouseNumber; // Stop When Energy is empty if (energy < 0) { run = false; } // Graph Display float graphXscale = 0.2; if (run) { graphX = graphX+graphXscale; graphY = (sy)-(energy/2000); graphYCH4 = (sy)-(methanePerStorageTank/200); for (int i = 0; i < xpos.length-1; i++) { xpos[i] = xpos[i+1]; ypos[i] = ypos[i+1]; } for (int i = 0; i < xposCH4.length-1; i++) { xposCH4[i] = xposCH4[i+1]; yposCH4[i] = yposCH4[i+1]; } xpos[xpos.length-1] = graphX; ypos[ypos.length-1] = graphY;
}
xposCH4[xposCH4.length-1] = graphX; yposCH4[yposCH4.length-1] = graphYCH4;
for (int i =0; i<xpos.length; i++) { fill(255, 50, 50);
207 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
}
ellipse(xpos[i], ypos[i], .3, .3); fill(50, 50, 255); ellipse(xposCH4[i], yposCH4[i], .3, .3);
float numberOfXline = sx/graphXscale*10; graphXscale=(graphXscale*12)*10; float density = 1; density=density*1333; density=sqrt(density); density=1000/density; for (int lx = 0; lx < 40; lx++) { for (float l = 0; l < 5; l=l+.4) { fill(60, 60, 60, 80); ellipse(graphXscale*lx, (sy-5)+l, .15, .15); ellipse(lx*density, l, .15, .15); } textAlign(CENTER); fill(100); textSize(1); text(((lx*10)+10), (graphXscale*lx)+graphXscale, sy-6); text((lx+1)+” km”, (lx*density)+density, 7); } //Table Record if (run) { TableRow newRow = table.addRow(); newRow.setInt(“Year”, ((table.lastRowIndex())+1)/12); newRow.setInt(“Month”, (((table.lastRowIndex())+1)%12)+1); newRow.setInt(“Energy”, round(energy)); newRow.setInt(“Energy Havested”, round(energyGain)); newRow.setInt(“Energy Used”, round(energyUse)); newRow.setInt(“Energy Difference”, round(energyDifferent)); newRow.setInt(“Cubic Meter of Methane”, round(frozenMethane)); //Image Record //saveFrame();
} saveTable(table, “data/Data.csv”); //==========NETWORKING==========
for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (methaneStorage[x][y][1]==1) { storageX.append(x); storageY.append(y); } }
208 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
} for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (melter[x][y][1]==1) { melterX.append(x); melterY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (activeSupplier[x][y][1]==1) { supplierX.append(x); supplierY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (house[x][y][1]==1) { houseOneX.append(x); houseOneY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (houseTwo[x][y][1]==1) { houseTwoX.append(x); houseTwoY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (houseThree[x][y][1]==1) { houseThreeX.append(x); houseThreeY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) { if (houseFour[x][y][1]==1) { houseFourX.append(x); houseFourY.append(y); } } } for (int x = 0; x < sx; x++) { for (int y = 0; y < sy; y++) {
if (houseFive[x][y][1]==1) { houseFiveX.append(x); houseFiveY.append(y); }
} } OscMessage pos = new OscMessage(“/pos”); for (int i = 0; i < storageX.size(); i++) { pos.add(storageX.get(i)); pos.add(storageY.get(i)); } pos.add(“&”); for (int i = 0; i < melterX.size(); i++) { pos.add(melterX.get(i)); pos.add(melterY.get(i)); } pos.add(“&”); for (int i = 0; i < supplierX.size(); i++) { pos.add(supplierX.get(i)); pos.add(supplierY.get(i)); } pos.add(“&”); for (int i = 0; i < houseOneX.size(); i++) { pos.add(houseOneX.get(i)); pos.add(houseOneY.get(i)); } pos.add(“&”); for (int i = 0; i < houseTwoX.size(); i++) { pos.add(houseTwoX.get(i)); pos.add(houseTwoY.get(i)); } pos.add(“&”); for (int i = 0; i < houseThreeX.size(); i++) { pos.add(houseThreeX.get(i)); pos.add(houseThreeY.get(i)); } pos.add(“&”); for (int i = 0; i < houseFourX.size(); i++) { pos.add(houseFourX.get(i)); pos.add(houseFourY.get(i)); } pos.add(“&”); for (int i = 0; i < houseFiveX.size(); i++) { pos.add(houseFiveX.get(i)); pos.add(houseFiveY.get(i)); } if (run) { oscP5.send(pos, myRemoteLocation); }
} void keyPressed() { if ( key == ‘c’ ) { //clear the world melter = new int[sx][sy][2]; house = new int[sx][sy][2]; houseTwo = new int[sx][sy][2]; houseThree = new int[sx][sy][2]; methaneStorage = new int[sx][sy][2]; melterAge = new int[sx][sy][2]; agriculture = new int[sx][sy][2]; xHouse = new int[sx][sy][2]; animal = new int[sx][sy][2]; year=0; energy=0; graphX=0; float[] xpos = new float[400]; float[] ypos = new float[400]; run = false; } if ( key == ‘ ‘ ) { // paused/play life run = !run; }
} void mousePressed() { //switch on/off methaneStorage[int(mouseX/zoom)][int(mouseY/zoom)][1] = 1; melter[int((mouseX/zoom)+1)][int(mouseY/zoom)][1] = 1; melter[int(mouseX/zoom)][int((mouseY/zoom)+1)][1] = 1; melter[int(mouseX/zoom)+1][int(mouseY/zoom)+1][1] = 1; melter[int((mouseX/zoom)-1)][int((mouseY/zoom)-1)][1] = 1; melter[int((mouseX/zoom)-1)][int((mouseY/zoom)+1)][1] = 1; melter[int((mouseX/zoom)-1)][int(mouseY/zoom)][1] = 1;
}
melter[int(mouseX/zoom)][int((mouseY/zoom)-1)][1] = 1; melter[int((mouseX/zoom)+1)][int((mouseY/zoom)-1)][1] = 1;
// Count the number of adjacent cells ‘on’ int methaneStorageNeighbor1 (int x, int y) { return methaneStorage[(x + 1) % sx][y][0] + methaneStorage[(x + 2) % sx][y][0] + methaneStorage[(x + 3) % sx][y][0] +
209 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
methaneStorage[x][(y + 1) % sy][0] + methaneStorage[x][(y + 2) % sy][0] + methaneStorage[x][(y + 3) % sy][0] +
methaneStorage[(x + 1) % sx][(y + 1) % sy][0] + methaneStorage[(x + 1) % sx][(y + 2) % sy][0] + methaneStorage[(x + 1) % sx][(y + 3) % sy][0] + methaneStorage[(x + 2) % sx][(y + 1) % sy][0] + methaneStorage[(x + 2) % sx][(y + 2) % sy][0] + methaneStorage[(x + 2) % sx][(y + 3) % sy][0] + methaneStorage[(x + 3) % sx][(y + 1) % sy][0] + methaneStorage[(x + 3) % sx][(y + 2) % sy][0] + methaneStorage[(x + 3) % sx][(y + 3) % sy][0] +
methaneStorage[(x + sx - 1) % sx][y][0] + methaneStorage[(x + sx - 2) % sx][y][0] + methaneStorage[(x + sx - 3) % sx][y][0] + methaneStorage[x][(y + sy - 1) % sy][0] + methaneStorage[x][(y + sy - 2) % sy][0] + methaneStorage[x][(y + sy - 3) % sy][0] +
methaneStorage[(x + sx - 1) % sx][(y + 1) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + 2) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + 3) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + 1) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + 2) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + 3) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + 1) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + 2) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + 3) % sy][0] +
methaneStorage[(x + 1) % sx][(y + 1) % sy][0] + methaneStorage[(x + 1) % sx][(y + 2) % sy][0] + methaneStorage[(x + 2) % sx][(y + 1) % sy][0] + methaneStorage[(x + 2) % sx][(y + 2) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + 1) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + 2) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + 1) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + 2) % sy][0] +
methaneStorage[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + sy - 3) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + sy - 3) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + sx - 3) % sx][(y + sy - 3) % sy][0] +
methaneStorage[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + sx - 1) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + sx - 2) % sx][(y + sy - 2) % sy][0] +
}
methaneStorage[(x + 1) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + 1) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + 2) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + 2) % sx][(y + sy - 2) % sy][0];
int methaneStorageNeighbor2 (int x, int y) { return methaneStorage[(x + 1) % sx][y][0] + methaneStorage[(x + 2) % sx][y][0] + methaneStorage[(x + 3) % sx][y][0] + methaneStorage[x][(y + 1) % sy][0] + methaneStorage[x][(y + 2) % sy][0] + methaneStorage[x][(y + 3) % sy][0] + methaneStorage[(x + sx - 1) % sx][y][0] + methaneStorage[(x + sx - 2) % sx][y][0] + methaneStorage[(x + sx - 3) % sx][y][0] + methaneStorage[x][(y + sy - 1) % sy][0] + methaneStorage[x][(y + sy - 2) % sy][0] + methaneStorage[x][(y + sy - 3) % sy][0] +
210 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
}
methaneStorage[(x + 1) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + 1) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + 1) % sx][(y + sy - 3) % sy][0] + methaneStorage[(x + 2) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + 2) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + 2) % sx][(y + sy - 3) % sy][0] + methaneStorage[(x + 3) % sx][(y + sy - 1) % sy][0] + methaneStorage[(x + 3) % sx][(y + sy - 2) % sy][0] + methaneStorage[(x + 3) % sx][(y + sy - 3) % sy][0] ;
int methaneSupplierNeighbor (int x, int y) { return methaneSupplier[(x + 1) % sx][y][0] + methaneSupplier[(x + 2) % sx][y][0] + methaneSupplier[(x + 3) % sx][y][0] + methaneSupplier[x][(y + 1) % sy][0] + methaneSupplier[x][(y + 2) % sy][0] +
methaneSupplier[x][(y + 3) % sy][0] + methaneSupplier[(x + sx - 1) % sx][y][0] + methaneSupplier[(x + sx - 2) % sx][y][0] + methaneSupplier[(x + sx - 3) % sx][y][0] + methaneSupplier[x][(y + sy - 1) % sy][0] + methaneSupplier[x][(y + sy - 2) % sy][0] + methaneSupplier[x][(y + sy - 3) % sy][0] + methaneSupplier[(x + 1) % sx][(y + 1) % sy][0] + methaneSupplier[(x + 1) % sx][(y + 2) % sy][0] + methaneSupplier[(x + 2) % sx][(y + 1) % sy][0] + methaneSupplier[(x + 2) % sx][(y + 2) % sy][0] + methaneSupplier[(x + sx - 1) % sx][(y + 1) % sy][0] + methaneSupplier[(x + sx - 1) % sx][(y + 2) % sy][0] + methaneSupplier[(x + sx - 2) % sx][(y + 1) % sy][0] + methaneSupplier[(x + sx - 2) % sx][(y + 2) % sy][0] + methaneSupplier[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + methaneSupplier[(x + sx - 1) % sx][(y + sy - 2) % sy][0] + methaneSupplier[(x + sx - 2) % sx][(y + sy - 1) % sy][0] + methaneSupplier[(x + sx - 2) % sx][(y + sy - 2) % sy][0] +
}
methaneSupplier[(x + 1) % sx][(y + sy - 1) % sy][0] + methaneSupplier[(x + 1) % sx][(y + sy - 2) % sy][0] + methaneSupplier[(x + 2) % sx][(y + sy - 1) % sy][0] + methaneSupplier[(x + 2) % sx][(y + sy - 2) % sy][0];
int houseNeighbor (int x, int y) { return house[(x + 1) % sx][y][0] + house[x][(y + 1) % sy][0] + house[(x + sx - 1) % sx][y][0] + house[x][(y + sy - 1) % sy][0] + house[(x + 1) % sx][(y + 1) % sy][0] + house[(x + sx - 1) % sx][(y + 1) % sy][0] + house[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + house[(x + 1) % sx][(y + sy - 1) % sy][0]; } int houseTwoNeighbor (int x, int y) { return houseTwo[(x + 1) % sx][y][0] + houseTwo[x][(y + 1) % sy][0] + houseTwo[(x + sx - 1) % sx][y][0] + houseTwo[x][(y + sy - 1) % sy][0] + houseTwo[(x + 1) % sx][(y + 1) % sy][0] + houseTwo[(x + sx - 1) % sx][(y + 1) % sy][0] +
}
houseTwo[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + houseTwo[(x + 1) % sx][(y + sy - 1) % sy][0];
int houseThreeNeighbor (int x, int y) { return houseThree[(x + 1) % sx][y][0] + houseThree[x][(y + 1) % sy][0] + houseThree[(x + sx - 1) % sx][y][0] + houseThree[x][(y + sy - 1) % sy][0] + houseThree[(x + 1) % sx][(y + 1) % sy][0] + houseThree[(x + sx - 1) % sx][(y + 1) % sy][0] + houseThree[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + houseThree[(x + 1) % sx][(y + sy - 1) % sy][0]; } int houseFourNeighbor (int x, int y) { return houseFour[(x + 1) % sx][y][0] + houseFour[x][(y + 1) % sy][0] + houseFour[(x + sx - 1) % sx][y][0] + houseFour[x][(y + sy - 1) % sy][0] + houseFour[(x + 1) % sx][(y + 1) % sy][0] + houseFour[(x + sx - 1) % sx][(y + 1) % sy][0] + houseFour[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + houseFour[(x + 1) % sx][(y + sy - 1) % sy][0]; }
int melterNeighbor1 (int x, int y) { return melter[(x + 1) % sx][y][0] + melter[x][(y + 1) % sy][0] + melter[(x + sx - 1) % sx][y][0] + melter[x][(y + sy - 1) % sy][0] + melter[(x + 1) % sx][(y + 1) % sy][0] + melter[(x + sx - 1) % sx][(y + 1) % sy][0] + melter[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + melter[(x + 1) % sx][(y + sy - 1) % sy][0]; } int melterNeighbor2 (int x, int y) { return melter[(x + 1) % sx][y][0] + melter[(x + 2) % sx][y][0] + melter[x][(y + 1) % sy][0] + melter[x][(y + 2) % sy][0] + melter[(x + sx - 1) % sx][y][0] + melter[(x + sx - 2) % sx][y][0] + melter[x][(y + sy - 1) % sy][0] + melter[x][(y + sy - 2) % sy][0] +
211 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
PROCESSING SCRIPT CA
}
melter[(x + 1) % sx][(y + 1) % sy][0] + melter[(x + 1) % sx][(y + 2) % sy][0] + melter[(x + 2) % sx][(y + 1) % sy][0] + melter[(x + 2) % sx][(y + 2) % sy][0] + melter[(x + sx - 1) % sx][(y + 1) % sy][0] + melter[(x + sx - 1) % sx][(y + 2) % sy][0] + melter[(x + sx - 2) % sx][(y + 1) % sy][0] + melter[(x + sx - 2) % sx][(y + 2) % sy][0] + melter[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + melter[(x + sx - 1) % sx][(y + sy - 2) % sy][0] + melter[(x + sx - 2) % sx][(y + sy - 1) % sy][0] + melter[(x + sx - 2) % sx][(y + sy - 2) % sy][0] + melter[(x + 1) % sx][(y + sy - 1) % sy][0] + melter[(x + 1) % sx][(y + sy - 2) % sy][0] + melter[(x + 2) % sx][(y + sy - 1) % sy][0] + melter[(x + 2) % sx][(y + sy - 2) % sy][0] ;
int waterUnitNeighbor (int x, int y) { return waterUnit[(x + 1) % sx][y][0] + waterUnit[x][(y + 1) % sy][0] + waterUnit[(x + sx - 1) % sx][y][0] + waterUnit[x][(y + sy - 1) % sy][0] + waterUnit[(x + 1) % sx][(y + 1) % sy][0] + waterUnit[(x + sx - 1) % sx][(y + 1) % sy][0] + waterUnit[(x + sx - 1) % sx][(y + sy - 1) % sy][0] + waterUnit[(x + 1) % sx][(y + sy - 1) % sy][0]; }
212 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
213 Anna Kulik, Napak Arunanondchai, Naphat Chongratanakul
CELLULAR AUTOMATA TESTS . ANALYSIS
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Accumulated energy [GJ]
500
375
250
125
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2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
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2020
Harvester
2025
2030
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Housing Unit (4 people per unit)
214 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
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2095
2100
Accumulated energy [GJ]
500
375
250
125
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2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
216 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
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2055
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2080
2085
2090
2095
2100
Accumulated energy [GJ]
500
375
250
125
0 2013
2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
218 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
150000
112500
75000
37500
0 2013
2020
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2100
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2055
2060
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2070
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2080
2085
2090
2095
2100
Accumulated energy [GJ]
500
375
250
125
0 2013
2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
220 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
150000
112500
75000
37500
0 2013
2020
2025
2030
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2100
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2080
2085
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2095
2100
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2080
2085
2090
2095
2100
Accumulated energy [GJ]
500
375
250
125
0 2013
2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
222 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
2070
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
150000
112500
75000
37500
0 2013
2020
2025
2030
2035
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2045
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2095
2100
Accumulated energy [GJ]
500
375
250
125
0 2013
2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
224 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm
CELLULAR AUTOMATA TESTS . ANALYSIS
150000
112500
75000
37500
0 2013
2020
2025
2030
2035
2040
2045
2050
2055
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2100
2040
2045
2050
2055
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2075
2080
2085
2090
2095
2100
Accumulated energy [GJ]
500
375
250
125
0 2013
2020
2025
Average methane hydrate per storage [m3]
8000
6000
4000
2000
0 2013
2020
Harvester
2025
2030
2035
Housing Unit (4 people per unit)
226 Architectural Association School of Architecture Emergent Technologies and Design Dissertation 2012-13
Agriculture Farm
Animal Farm