Physa City Sabkha Urbanism Application of Physarum Polycephalum as biological algorithm to compute and design a mechanism of energy re-distribution, material accumulation and emergent urban morphology in Liwa Oasis City, The Rub Al Khali Desert.
Reference Tutors: Claudia Pasquero, Maj Plemenitas, Eduardo Rico Research Team Members: Shengyu Meng; Bona Wang; Ting Jiang; Boliang Liu; Ge Sun; Sora Chang The Bartlett School of Architecture MArch Urban Design 2014-2015 Urban Morphogenesis Lab|RC16
Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis
Biological Self-organized Network Algorithm - Physarum polycephalum
Computational Self-organized Network Algorithm Particles simulation
Ecological algorithm in Urban Cluster Scale
Autonomous Network Builder Swarm Robot
Material System Composite Structure and Multifunctional Network Landscape
BIOLOGICAL URBAN NETWORK ALGORITHM: FROM PHYSARUM POLYCEPHALUM TO SELF-ORGANIZE NETWORK OASIS The Liwa Oasis city is located at the Arabian Peninsula, on the north edge of the Rub Al Khali desert. Unlike the other contemporary urban settlement area in United Arab Emirates, for instance, Dubai and Abu Dhabi, which were constructed following the governmental planning, and mainly represent the capital and political impacts, Liwa oasis is a self-organized territory which is strongly influenced by the distribution of scarce resources and topographic conditions in the desert. Because of this property, Liwa oasis was emerging as an ecological self-organize network system interacted with other resources network. For example, Liwa oasis was built on 16 century, mainly because its location was in the accessible radius of shallow groundwater resource network. Water was the most important resource in that period, because it is necessary for the nomadic people who are living and discovering in the desert. They were “scanning� the territory in the Desert without extract geological understanding, and tried to find the location which can easily access to the shallow underground water through manually digging wells. Some settlements emerged individually alongside the wells, and consequently connected together as a network oasis. Therefore, Liwa was built up as a nomadic self-organize network oasis city, mainly responding to the water resource distribution. However, today about 60% water usage in Liwa oasis is provided by the water desalination plants from the coastal area. This change means energy has replaced water as the most important local resource, when this water supplying strategy is actually using energy to replace the water consumption. Under this condition, the existing morphology of Liwa may be challenged. In addition, in the 2030 master, Liwa is going to transform from a farm area into a main tourism destination in the desert. Therefore, its resident population will be increase from around 20,000 to 65,000, and the annual tourist amount is aiming to grow from 30,000 to 90,000. Under this background, energy has become the most important driving force of the expansion of the Liwa oasis. In the Liwa territory, there is a type of remarkable landscape, between dunes in-land sabkha (salt-plate). This landscape is a main tourism attraction, and also a potential energy source because it’s large reserves of salt. In addition, in the latest 20 years developing history, Liwa was expanding by cleaning and occupying sabkhas, which are normally located at the small basins and protected from the strong winds in desert by surrounding dunes. Because the existing Liwa oasis network system is constituted by settlement and nearby sabkhas, and the salt material distribution process, so it is potential to apply as inputs into an algorithm. Instead of retracting to a traditional top-down system, a new interactive and bottom-up self-organize algorithm is necessary, to cope with the existing complicated ecological network system. For this reason, we research and experiment with the physarum polycephalum slime mold. This microorganism has similar behaviours strategy with nomadic people when they are searching and collecting resources. The behaviour of physarum is the combination of numerous cells inside. The interaction and communication of its cells can output a network generating process as optimized as artificial one. This property make it potential to process special information from site in similar way. By applying bio-3d printing technology, high-resolution observation device and picture analysis algorithm, we have developed slime mold as a biological network algorithm, to generate the time-based urban design proposals for Liwa territory. We also develop the digital simulation network algorithm from the strategy of slime mold in parallel, in order to provide design proposal in specific area, with higher accuracy. In the other aspect, In order to implement the urban design proposal generated by biological algorithm, swarm robot was introduced as autonomous builders in this system. Because the growing of physarum is the macroscopical representation of the collective movement of its cells inside, which has the similar mechanism with swarm robots. When the swarm robots carrying materials and moving around Liwa territory, it is potential to construct specific urban prototypes and new landscape. For investigating this possibility, we research the energy producing and structure application of the resources, such as salt, fiber, and starch in Liwa territory. We organize these investigations as a material system, and connected it into the biological network algorithm, by applying digital simulation. In summary, the Physa biological network algorithm generated design proposal in urban scale, and the digital network algorithm compute the design proposal in building scale, meanwhile the swarm builders and the material system are the final implements. These different research components consequently constructed a dynamic ecological network algorithm. This network algorithm is capable to establish the link between the practical requirement of society development and the existing resource oriented network urban morphology, and form a new urban morphology, from top-down to bottom-up, from stable to dynamic, from homogeneous to diverse.
3
Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis •
Resource and Landscape Distribution Network in Rub Al Khali Desert
•
Symbiotic Network System in Liwa Territory
Biological Self-organized Network Algorithm - Physarum polycephalum •
Physarum Polycephalum’s Selforganize and Network Generating Intelligence
•
Site Information input into Physa Biological Network Algorithm
•
Information output and Process from Physa Biological Network Algorithm
Computational Self-organized Network Algorithm Particles simulation
Autonomous Network Builder Swarm Robot
•
•
The Collective Intelligence inside the Physarum polycephalum
•
The Swarm Robot Gatherer and Builder Managed by Physa Algorithm
Digital Simulation from Physa Algorithm in Specific area of Liwa Territory
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
•
The Resource Distribution in Liwa Territory
•
The Potential Energy Producing Application of Salt Material
•
The Structural Research of Palm Tree Fiber and Salt
•
The Structural Research of Bioplastic
•
The Composite Structure from Physical Experiment to Digital Simulation
•
The Visualization of Physa Network City
CONTENTS
Introduction 2
Flow Chart
3
Biological Urban Network Algorithm: from Physarum polycephalum to Self-organize Network Oasis
6
Nomadic Self-organized Network Oasis City - Liwa Oasis
8
Resource and Landscape Distribution Network in Rub Al Khali Desert
16
Symbiotic Network System in Liwa Territory
38
Biological Self-organized Network Algorithm - Physarum polycephalum
40
Physarum Polycephalum’s Self-organize and Network Generating Intelligence
64
Site Information input into Physa Biological Network Algorithm
86
Information output and Process from Physa Biological Network Algorithm
110
Autonomous Network Builder - Swarm Robot
112
The Collective Intelligence inside the Physarum polycephalum
118
The Swarm Robot Gatherer and Builder Managed by Physa Algorithm
128
Computational Self-organized Network Algorithm - Particles simulation
130 142
Digital Simulation from Physa Algorithm in Specific area of Liwa Territory
Material System - Composite Structure
144
The Resource Distribution in Liwa Territory
148
The Potential Energy Producing Application of Salt Material
150
The Structural Research of Palm Tree Fiber and Salt
156
The Structural Research of Bio-plastic
170
The Composite Structure from Physical Experiment to Digital Simulation
184
The Visualization of Physa Network City
190
Design Report
198
Appendix
Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis •
Resource and Landscape Distribution Network in Rub Al Khali Desert
•
Symbiotic Network System in Liwa Territory
Biological Self-organized Network Algorithm - Physarum polycephalum
Autonomous Network Builder Swarm Robot
Computational Self-organized Network Algorithm Particles simulation
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis Resource and Landscape Distribution Network in Rub Al Khali Desert
8-15 Symbiotic Network System in Liwa Territory
16-37
Liwa 23째08'N 53째46'E Population: 20,196 220km to Abu Dhabi 380km to Dubai
Resource and Landscape Distribution Network in Rub Al Khali Desert
This site locates at the Rub Al Khali desert. In common sense, desert is defined as the middle of nowhere. However, it is not true, the desert is far more various and beautiful then we imagine. Even in a desert, where is full of sand, the sand also can become an excellent media to represent the various climate and geological condition. For example, due to different sand supply, wind power and ground surface conditions, the landscapes pattern of sand dune could be very diverse. The sand dune types which have distributed in Rub Al Khali Desert include barchans dunes, liner dunes, dome dunes, etc. In the other aspect, desert is not as dry as many people may consider. Some low-lying areas in desert with may have enough water supply from underground and also the rainfall collected and concentrated by sand bed, will generate oasis, which may become the main settlement areas in desert. When some other regions may also have limited water supply, but because the strong evaporation caused by sun light, the water become steam and left many sabkha, which means the salt plate. Similar to sand dune, the Sabkhas also have various form depend on different condition. For example, the typical classification of sabkha are costal sabkha, and inland sabkha. The landscape condition is an important factor in whole ecological system. Therefore, due to the diverse condition above, the species distributed abundantly and variously in the desert. Similarly, because of different ecological condition, the species distributed on the dessert is diverse as well. Furthermore, the desert is full of resource, especially energy resource, including fossil energy resource, such as oil and coal, and also sustainable resource, for instance, solar energy and wind energy. The exposure of the diversity of Rub Al Khali desert let us know how interesting and abundant of it, and it give us the motivation and also potential to develop new methodology of urban design in this area.
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Landscape Diversity in Desert Different areas in the same desert will have diffrent geolocial and climate conditions. Thesre diversity will represent on the various lanscape types.
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11
Ecological Diversity in Desert Because of different ecological condition, the species distributed on the dessert is diverse as well.
12
13
Doha
Duba
Abu
Liwa
Resource and Lanscapes around Liwa Oasis The nearby area of liwa, is full of resource. Like water resource, which will allow liwa to become a vegetation area. And also for energy, like oil field and solar plant. 14
Water
Solar Energy Plant
Oil Field
Oasis
Date Farm
Interdune sabkha between Large Barchan Dune
ai
Dhabi
15
Water wells Water well network 0
2.5
10km
Network System in Liwa Territory
The Liwa Oasis city is located at the Arabian Peninsula, on the north edge of the Rub Al Khali desert. Because the limitation of the appropriate geological condition and life resources distribution are limited and only concentrated in some areas in the desert, Liwa oasis is more similar to a self-organized city which is strongly influenced above elements. As a self-organized field, Liwa is continuously adjusting to adapt to the dynamic natural and social environment. Liwa firstly emerged as an area of date farm before 16 century, depend on its abundant ground water resource. In the period of about 300 years, Liwa gradually grew to be the economic focal point in this area, until the booming of pearl industry in late 19 century and the oil industry in early 20 century in the coastal area of Persian Gulf. The transformation of industry structure resulted in the population migration from Liwa to coastal area. However, Liwa still continued to serve for and be developed by the people who want to live in and explore the Rub Al Khali desert. Because Liwa is the origin of human to explore and transport in desert. In our research, we collect the different types of information in Liwa region, aiming to analysis and describe the morphology of Liwa. In consequence, we abstracted these information as several network system, which constitute a symbiotic system with Liwa region. In details, the human activity network is the reason why people will settle in Liwa; the water well and desalination water supply network provide the most important water resource make people survive in Liwa; the topographic network help Liwa to isolate with the harsh environment in desert in some extend. In this chapter, we will explain all these networks about how they are interacting with Liwa, and also expose the conflicts inside, and then try to propose potential propose.
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Topographic Network
One main reason of why Liwa oasis could be survived in this desert is its special topographic condition. Generally, Liwa is an east-west direction crescent basin in the middle of two large dune fields. The higher land in the south and north side of Liwa can help it to avoid the direct threaten from the sand storm in the desert. At topographic aspect, Liwa emerged firstly because of it is low ever and flatter than nearby area.This terrain feature allow liwa to survive in the strong wind of desert, and also benefit from the rainfall water collected by sand. These unique topographic situation could abstracted as a network system, to better explain these property.
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20 Meter Interval UAE Topographic Map
20 Meter Interval Topographic Map of Liwa Region
Tessellation Network Map of Liwa Area
One main reason of why Liwa oasis could be survived in this desert is its special topographic condition. Generally, Liwa is an east-west direction crescent basin in the middle of two large dune fields. The higher land in the south and north side of Liwa can help it to avoid the direct threaten from the sand storm in the desert. In the other aspect, this terrain will also help the rainfall water which was collected by sand dune to concentrate in Liwa oasis. These unique topographic situation could abstracted as a network system, to better explain these property. Here is a tessellation network map which represent the steepness density of Liwa area. We can see the above features significantly in this map.
Steepness Density
Obstacle areas with high steepness
High Density
Farmland Sabkha Low Density
Reference: Liwa Crescent Story https://www.environmentalatlas.ae/ resourceOfLife/ecologyOfWater
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0
2.5
10km
The Sahllow Ground Water Resource in Liwa
The Liwa Crescent hosts a well known as a date farms located in the north edge of Rub Al Khali desert. This coincides with some of the most productive aquifers in the desert, forming a natural ‘water bank’ that has allowed human occupation of this otherwise barren region for thousands of years. The fresh water of Liwa is contained within layers of medium to fine grained windblown sands. The upper parts of the aquifer are highly porous and free of clay and silt which normally restrict water movement. These conditions are ideal for the absorption and storage of rain water which in past millennia was far more abundant than it is today.
Reference: Liwa Crescent Story https://www.environmentalatlas.ae/ resourceOfLife/ecologyOfWater
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Liwa
[Network Oasis city on the edgy of the Rub Al Khali] Section of Aquitard/Aquiclude <=1,000 ppm >1,000 -1,500 ppm >1,500 - 4,000 ppm >4,000 - 7,000 ppm >7,000 - 10,000 ppm >10,000 ppm
225
Al Bateen
200
Liwa
Al Qafa / Bu Hasa
175
125 100
Fresh
75 50
Brackish
25 0
Arabian Gulf Saline
-25
Abu Dhabi
-50
Al Ain
-75 2530000
2540000
2550000
2560000
2570000
2580000
2590000 Emirate of Abu Dhabi Latitude (UTM y40)
South
0 km
50 km
100 km
2600000
2610000
2620000
2630000
2640000
2650000
2660000
North
150 km
[Network Oasis city on the edgy of the Rub Al Khali] Section of Aquitard/Aquiclude
Arabian Gulf
Abu Dhabi
<=1,000 ppm
Al Ain
>1,000 -1,500 ppm >1,500 - 4,000 ppm
Emirate of Liwa
Abu Dhabi
>4,000 - 7,000 ppm >7,000 - 10,000 ppm >10,000 ppm
225
Section of UAE and Rub Al Khali Desert Show the Ground Water Condition of Liwa Liwa Region
Al Bateen
200
Al Qafa / B
175 150 Elevation (mMSL)
Elevation (mMSL)
150
125 100 75
25
50 25 0 -25 -50 -75 2530000
South
2540000
2550000
2560000
2570000
2580
Underground Water Condition of Liwa Region
Due to the topographic and geological condition in Liwa, Liwa region become a basin where the ground water resource will concentrate. The main current ground water recharging is from the rain fall in Ai Lin area. In addition, because the aquifer in Liwa is about 80 meter higher than the Sea level, the ground water in Liwa will recharge to nearby area as well.
Thickness of Upper Aquifer 50
0 Water Flow Direction
Wells
26
Rainfall Amount
[ABa]
[AAj] [ZMC] [SW]
[ADh]
[ASh] [Msf]
[AW ] [AKh] [AR] [AM] [Rm]
[Gh] [AA]
[AGh]
[AQ]
[Kay] [Trq]
[Aly]
[Sbk]
[Lw] [Akh] [Tel Moreeb]
[AQ]
The History of Liwa Oasis Built by Nomadic People and the Relationship with Shallow Ground Water Resource
In the territory of Liwa, there are some specific locations which are easily accessible to the aquifer bellow. In the 16th Century, nomadic people were scanning this territory to find the appropriate locations to dig water wells. These wells provide stable water supplying, which was applied to support the nearby emerging villages, and also developed agricultural industry, such as date farm and fish farm. These villages were keep expanding and gradually merging together, consequently formed the city network as todayâ&#x20AC;&#x2122;s Liwa oasis.
Reference: Liwa Crescent Story https://www.environmentalatlas.ae/ resourceOfLife/ecologyOfWater
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other natural sources of fresh water.
Liwa Dunes
Oasis
Groundwater Flow Path
Sand Dunes
Arabian Gulf
Groundwater Aquifer System in Western Region
Reference: Liwa Crescent Story https://www.environmentalatlas.ae/ resourceOfLife/ecologyOfWater
29
Date & Fish Farm in Liwa Oasis between the sand dunes.
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31
Water Well Network
In history, Liwa is firstly supported by the traditional water wells network. The irrigation system which connected to the water well network helped Liwa to grow as a date farm. In the desert, the water well also became a guidepost system for the nomadic people travelling in the desert. The water wells network is an important components in the symbiotic network system in Liwa region. The settlement area of Liwa can only emerge when they are in a certain range of the Water well network. In addition, the water well network in the desert will allow the people to across the desert for travelling and trading purpose.
Water wells
Water wells Fields
Water well network
Settlement Fields
Farmland
Building
Reference: Traditional Wells in Liwa Territory https://www.environmentalatlas.ae/ resourceOfLife/waterThenAndNow
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0
2.5
10km
Human Activity Network
The human activity in Rub Al Khali has a long tradition.In the Neolithic age, peoples have started to move with the changing seasons, spending part of the year on the coast and the rest either at the higher elevations of the Hajar Mountains or the desert interior where they frequented its many lakes or oases. Although the reasons may be different, this seasonal migration away from the summer heat and humidity of the coast is mirrored today as residents of Abu Dhabi. For example, in the late 18 century, before the oil industry booming, people would stay in the Liwa oasis in the summer for date farm, and immigrated to the coastal area for pearl diving in winter time. After the booming of oil industry, people have more ability to discover the desert, then more resources have been found here. Not only for oil field, also include some mineral resources, and even sustainable energy, such as wind power and solar power. In addition, increasing people come to desert with sightseeing purpose. All these activities of human form a network in Rub Al Khali desert.
Picture Reference: http://edu.environmentalatlas. ae//images/imglib/ Pathways/_1566116498.jpg
Human Acitivities Fields
Thickness of Upper Aquifer
Highway 50
0
Road Tourist Spots Main Towns
Rainfall Amount 0 34
5
20km
[ABa]
[AAj] [ZMC] [SW]
[ADh]
[ASh]
[Msf]
[AW ] [AKh]
[AR] [AM]
[Rm]
[Gh]
[AA]
[AGh]
[AQ]
[Kay] [Trq]
[Aly]
[Sbk]
[Lw] [Akh] [Tel Moreeb]
[AQ]
Water Consumption Condition
Unfortunately, the majority of the water creating this impressive underground reservoir is ancient, fossil water and today there are very few ‘deposits’ being made into this bank, due to lack of rainfall or other natural sources of fresh water. Until about fifty years ago, Abu Dhabi’s water requirements were met solely from groundwater using traditional extraction methods. These often involved shallow, hand-dug wells or the falaj system (man-made wells and channels that collect ground, spring and surface water and distribute it by gravity to where it is most needed). The construction of the first desalination plant in 1960 marked a milestone in Abu Dhabi’s development allowing plentiful salt water to be converted into fresh water, thereby diminishing dependence on limited natural fresh water sources. This capacity not only improved the quality of life of citizens but effectively opened the previously water-deficient islands and surrounding coastline to rapid urban development and population growth, which continues today. Recently, the desalination water occupy 2/3 of the total water consumption, when the traditional groundwater only supply around 1/3 in total. Even with abundant water resource in history, Liwa’s present water supply also mores depend on the desalination water network, which connects to the costal area. After the booming of oil industry of UAE, energy became cheaper and more accessible here. This transformation make the application of water desalination in large scale become possible. Today, about two third of the water consummated in UAE are supplying by the water desalination plant.
Reference: Water Crisis https://www.environmentalatlas.ae/ resourceOfLife/waterCrisis 65.0 %
Groundwater
29.0 %
Desalinatied Water
6.0 %
Recycled Water
Total Water Supply = 3.355Mm² Year
Total Water Supply In Liwa Territory By Source in 2009
Desalination Water Supply Network Water Desalination Plant
Road
Water Pump Productivity Of Desalination Network Of Desalination
36
0
5
20km
[ABa]
[AAj]
AJBAN PS [ZMC]
UNIT 3
[SW] SWFIHAN PS
[ADh] ALPS POP UNIT & MAN 1 POP 145MGD
[ASh]
[Msf] UNIT 4 MUSSAFAH PS UNIT 5 MIRFA POP 39MGD
[ADh]
[AW ]
REMAH PS
AAROS PS
[AKh]
39MGD
SHUWEIHAT PS [AR]
SHOBAISHI PS
AL MAQUAM PS
[AM] SHUWEIHUT POP 101MGD
SHUWEIHAT IPS
[Rm]
IPS1
[Gh]
[AA]
UNAMED POP 25MGD
IPS2
[AGh]
MADIMAT ZAYEDPS
UNAMED POP 31MGD
SUMMIT RES
[AQ]
MUZAIRA PS
[Kay]
[Aly]
[Trq]
[Sbk]
[Lw] [Akh] [TM]
UNAMED POP 69MGD
Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis
Biological Self-organized Network Algorithm - Physarum polycephalum •
Physarum Polycephalum’s Selforganize and Network Generating Intelligence
•
Site Information input into Physa Biological Network Algorithm
•
Information output and Process from Physa Biological Network Algorithm
Autonomous Network Builder Swarm Robot
Computational Self-organized Network Algorithm Particles simulation
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
Biological Self-organized Network Algorithm - Physarum polycephalum Physarum Polycephalumâ&#x20AC;&#x2122;s Self-organize and Network Generating Intelligence 40-63 Site Information input into Physa Biological Network Algorithm
64-85 Information output and Process from Physa Biological Network Algorithm
86-109
Physa Biological Mechanism
The project tries to make a reasonable distribution of infrastructure and develop a
network system for cities and towns. As the cities and towns are always generated
through a resilient bottom up way, this has motivated us to understand of the resilient infrastructural systems and obtained more information of the natural systems. Therefore, we want to find an algorithm which can help us to deal with various input and output information. It is a way of bottom to up thinking which can help our design project. One special organism attracts us, and the name of the creature is slime mold, which is able to creat the â&#x20AC;&#x2DC;biological algorithmâ&#x20AC;&#x2122;. Slime mold, Physarum polycephalum, is a mold, having an identity crisis as it has not been defined as a kind of mold. It belongs to the kingdom of the amoeba and a singlecelled organism that joins with other cells ,being formed a mass super-cell to produce maximized resources by itselfe. The Physarum polycephalum is biologically fascinating and computationally interesting, which can be a mathematical model of creating networks in urban areas as a biological algorithm. In the growing process, oats would be an element to attract the Physarum polycephalum and the formed branches can be efficient networks which is applicable to new design networks for the communication and transportation under a set of constraints of environment problem. Until now all the experiments about slime mold were conducted by scientists. They have observed slime mold activities but these have not aimed at simulation for design cities. From the urban designer perspective, we want to use the slime mold algorithm into the urban design. What we are interested in is to use the intelligence self-organizing algorithm created by slime mold distribution into urban design making a shift for the future cities. Since the slime mold will response to attractive things (food). We have done some testing experiments to figure out its behaviours when it faces obstacles and a calibration experiment based on existing city network using trasfortation information.
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Biological Algorithm
The main mechanism of the slime mold is its two part tube structure. On the one hand there are the spontaneously assembling/disassembling actin-myosin fibers, i.e. the tubes, creating an oscillatory pressure. On the other there is the passive plasmodium which transports chemicals and food particles in waves through the tubes. These two parts intertwine closely. The flow of the plasmodium steers the direction of the fibers, while the direction of the fibers limits the flow of the plasmodium. Most of its mass lies at the food sources (hitherto referred to as FS) to maximize the food intake. Food particles are extracted periodically and the FSs serve as oscillators triggering bidirectional streaming (also referred to as shuttle streaming). The remaining part of the organism develops dependent on the environmental conditions. If conditions are favorable it moves Omni-directionally with a huge number of fine tubes between the outer membrane . If conditions are less pleasant it advances with thick tubes in the most favorable directions. The influence of the environment seems to be primarily on the membrane (weakening or strengthening it) and secondarily on the strength of contraction. 2.1 Research of Biological Algorithm 2.1.1 Life cycle of slime mold a) Biological properties of slime mold; b) Factors of effecting slime mold growth. 2.1.2 Scientific research of slime mold a) Case study in scientific area; 2.1.3 Process of cultivating slime mold a) research of cultivating environment; b) Observing the cultivation petri dishes from the normal sight. 2.1.4 Observation machine set up 2.1.5 Connection with urban perspective; a) Comparison between Physarum network and theories from â&#x20AC;&#x2DC;Occupying and Connectingâ&#x20AC;&#x2122; 2.1.6 The Calibration experiment
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Slime molds begin life as amoebalike cells. These unicellular amoebae are commonly haploid and multiply if they encounter their favorite food, bacteria. These amoebae can mate if they encounter the correct mating type and form zygotes that then grow into plasmodia.
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Scientific research of slime mold
Study 1 : Network formation in Physarum polycephalum. (A) At t =0, a small plasmodium of Physarum was placed at the location of Tokyo in an experimental arena bounded by the Pacific coastline (white border) and supplemented with additional food sources at each of the major cities in the region (white dots). (B to F) The plasmodium grew out from the initial food source with a contiguous margin and progressively colonized each of the food sources. Behind the growing margin, the spreading mycelium resolved into a network of tubes interconnecting the food sources. Credit: Image courtesy of Science/AAAS
Study 2 : The Feature of Maze-Solving Chopped up a single polycephalum and scattered the pieces throughout a plastic maze. The smidgens of slime mold began to grow and find one another, burgeoning to fill the entire labyrinth. Nakagaki and his teammates placed blocks of agar packed with nutrients at the start and end of the maze. Four hours later the slime mold had retracted its branches from dead-end corridors, growing exclusively along the shortest path possible between the two pieces of food. Toshiyuki Nakagaki, Hokkaido University in Japan (in the early 2000s)
Study 3 : Slime mold controlled robots The six-legged configuration with a hexagonal body shape is pictured in Fig. 3. The hexagonal platform is equipped with sensors equally in all six directions, resulting in a pole-free robot that can move and sense omnidirectional. This experiments show that light signals can be used to dynamically alter the coupling among oscillators and thus point to a path for interfacing the sensors of the robot with the plasmodium. Clearly, this work is at an early stage and further studies are needed to explore the properties, capabilities and limitations of such a system. Klaus-Peter Zauner ,at the University of Southampton, UK.
Study 4 : Slime mould tactile sensor Two principle types of responses are identified: an immediate response in a form of a highamplitude impulse, and, a prolonged response, in a form of changes in oscillation frequency and amplitude. A correlation between weight of an object applied and amplitude of the slime mouldâ&#x20AC;&#x2122;s response impulse or amplitude and frequency of oscil- lations is rather vague and required further extensive studies. However, the sensor definitely acts as an ON-OFF sensors which is already a huge breakthrough in slime mould based electronics. Adamatzky, A. (2013). Slime mould tactile sensor. Sensors & Actuators: B. Chemical, 188, 38-44.
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1
2 fig1. Study1 Nakagaki, et al Rules for biologically-inspired adaptive network design fig2. Study2. (Left) Nakagaki, Yamada, Toth Path finding by tube morphogenesis in an amoeboid arganism
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Biological Test
The mass plasmodium which transports chemicals and food particles in waves through the tubes is the main mechanism of the slime mold. By observing the growth mechanism, the slime mold can suggest a self- organizing material distribution system. If conditions are favorable it moves Omni-directionally with a huge number of fine tubes between the outer membrane. If conditions are less pleasant it advances with thick tubes in the most favorable directions. The influence of the environment seems to be primarily on the membrane (weakening or strengthening it) and secondarily on the strength of contraction. Inputďź&#x161; - oat flakes - clean dish with moisture 2% agar - Physarum polycephalum Sclerotium - pleasant area 70% humidity and 22-25â&#x201E;&#x192; room temperature Rule: a) Cultivate the slime mold from Sclerotium. b) Cover the bottom of the petri dish with agar (3mm thick) c) Place a piece of dried slime mold on the center of the dish and then feed oat pieces near slime mold d)Once the cultures are set up, seal the edge of the dish with its lid and warp the dishes with aluminium foil to keep out light. e) Incubate the cultures at room temperature. f) wait about 24 hours for the mass plasmodium. g) Spray water 1 or 2 a day to keep the moisture level. Outputďź&#x161; a) Mass plasmodium dish b) Growth speed: 1-2mm/h; c) Period of aliving: approximately10 days
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Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. 04.Nov.2014 12:30a.m After 14hours in cultivation process
47
Filter Paper
Moistened Put into petri dish
.Cut a piece of sclerotial stage Physarum Avoid strong direct light
Wrap it Keep in around 25째 Wait around 24 hours
sclerotial stage Physarum
Growing Plasmodia
48
Solid Agar
Heating Agar to be liquid (With microwave) Pull into petri dish Wait for harden
Subculture Physarum Weekly
Put into the petri dish with agar, wait 5â&#x20AC;&#x201C;8 hours
Plasmodials Stage Physarum
The process of cultivating slime mold
Disposal (Gneral waste)
Observation (Once get the stock) 49
Preparation for experiment Tools: forcep, sprinkling can, electronic scale, heater, injection syringe, pot, petri dish, oats powder, spoon, knife, agar, slime mold
50
Cultivation process of Physarum
51
T - 2h 1st Stage : Network Expansion The territory path network makes searching for food within a territory easier
T - 6h 2nd Stage : Network Concentration The settlement path network connects individual resting places and houses.
T - 12h 3rd Stage : Network Optimisation The long-distance path network connects places of habitation.
The growth of slime mold is smilar to the theory proposed by Frei Otto in the book â&#x20AC;&#x2DC;Occupying and Connectingâ&#x20AC;&#x2122;.
52
Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. 07.Dec.2014 11:18a.m The 3rd stage of the experiment
53
The connection with urban perspective - residence development
We tried to demonstrate how plasmodium branches resemble the process of occupying human settlement. In the book, ‘Occupying and Connecting’, Frei Otto said that typical self - formation processes of change have become so rapid today that current urban-planning theories have been overtaken. But high effectiveness of self-created, in other achievable today in ‘natural’ town transport planning and leads to ecologically meaningful solutions that are also full of beauty. In the result of the experiment, the growing stages and form of slime mold are similar to natural human sattlement networks. Input： [ model part ] - three points of oat flakes and slime mold in pleasant area 70% humidity and 22-25℃ room temperature [ theory part ] There are three stages in nature of residence development: - the territory path network makes searching for food within a territory easier - the settlement path network connects individual resting places and houses. the central point is the water resource - the long-distance path network connects places of habitation and is used for inerregional migrations Output： The movement of slime mold is similar to the algorithm in nature, which is first it expands, then concentrate on one nodes, at last it becomes an optimized network.
Diagram reference: Occupying and Connecting, by Frei Otto
54
The emergent plasmodium branches, feeding the oat flaks. Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. 26.Jan.2015
55
The connection with urban perspective - hexagonal grid
In the cultivating process of slime mold, it can be seen that Hexagonal grid is commonly formed when it grows. According to Otto in his book â&#x20AC;&#x2DC;Occupying and Connectingâ&#x20AC;&#x2122;, territories of the same size form best in a hexagonal frame. The key points of the surfaces form a clear triangular grid. Often, many generations can be seen, with an increasing number of pentagonal surfaces.
Input: [ model part ] - random points of oat flakes and slime mold in pleasant area 70% humidity and 22-25 room temperature [ theory part ] A hexagonal grid can be created from a triangular grid if points are removed in territories of the same size are recorded. The advantages of the hexagonal grid resulting from the triangular grid have a broad spectrum of application. Output: Slime mold built an analog hexagonal grid of the main tube.
Diagram reference: Occupying and Connecting, by Frei Otto
56
The rich plasmodium physarum Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. 26.Jan.2015
57
Network Calibration Model
Slime mold is a single-celled organism with an identity crisis that joins together with other cells to form a mass super-cell to maximize its resources. The arganism does not have a central nerve system. When we take a closer look , there is a rhythmic pulsing flow, a vein-like structure carrying cellular material, nutrients and chemical information through its branches, streaming first in one direction and then back in to another direction with continuous oscillation. We have done several calibration experiments that would demonstrate how the biological algoritm applicable to urban design as a method of creating new network system. First, we made paths by oats to figure out whether slime mold follows the paths which could attract slime mold and tested with tree different matrials to gauge the growing speed of slime mold and which matrials can affect its speed. Second, we choose London as an existing city and then extracted tube stations as points of placing attractor(food source) to demonstrate the similarity between the trasprotation network and slim mold branches.
Biological Algorithm Calibration a-1) Demonstration about tracing food source a-2) Growing speed on three different matrials b) an existing city with its own transportation system
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T= 12 Hours
T= 0 Hours
T= 8 Hours
T= 48 Hours
T= 48 Hours
T= 48 Hours
Different Kinds of Food Resource Input Information: different kinds of attractive point Output Information: most effective one (honey with oats) Time lapse: 48 Hours
Different Types of One Food Resource Input Information: different kinds of attractive point Output Information: most effective one (oat powder) Time lapse: 48 Hours
Types of Food Resource Input Information: probable kind of attractive point Output Information: not effective Time lapse: 48 Hours`
Types of Input Information Research on the most effective types of input information and then use for the biological printing.
59
T= 0 Hours
T= 8 Hours
T= 0 Hours
T= 96 Hours
T= 96 Hours
T= 48 Hours
Geometric Grids of Food Resource Distribution Input Information: regular grids Output Information: Slime Mold algorithm (sweeping food resource surface) Time lapse: 96 Hours
Mesh of Food Resource Distribution Input Information: mesh shape Output Information: Slime Mold algorithm (clockwise direction growth and connect from one to the nearest and most effective points) Time lapse: 96 Hours
Lines of Food Resource Distribution Input Information: lines of food Output Information: Slime Mold algorithm (follow the attractive resources) Time lapse: 48 Hours
Geometric Input Information Create information from the site to the slime mold in different ways.
60
T= 0 Hours
T= 8 Hours
T= 15 Hours
T= 96 Hours
vvbvvc
T= 96 Hours
Contrasts of nodes and fields Food Resource Input Information: nodes and fields of food resource Output Information: clean and absorb the surface to constitute itself network structures Time lapse: 96 Hours
simulation of residence in nature Input Information: three nodes as residence in nature Output Information: it expands, then concentrate on one nodes, at last it becomes an optimized network. Time lapse: 96 Hours
Add more information/ Food Resource Input Information: random attractive point Output Information: an optimized network Time lapse: 96 Hours
Density Input Information The surface contains numbers of points in the specific area. Then we went to add more points and lead to biological printing.
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Physa Biological Network Model of Liwa Oasis Wells Network Analogical Model
In the next step, the biological Physarum algorithm will be applied in the Liwa region. This new systemic design methodology will bring the potential to fill the gap between the existing symbiotic network system and the urban development methodology, and give intelligent and optimal design suggestion to the ecological conflicts inside the ecological system in the same time. Choosing the exsiting wells in Rub Al Khali to simulate the network of infrastructure.
Input： oat flakes clean dish with moisture 2% agar Physarum polycephalum Sclerotium pleasant area 70% humidity and 22-25℃ room temperature Rule： a) Cultivate the slime mold from Sclerotium. Use sterile forceps to transfer a piece of filter paper containing this resting stage to a dish with oatmeal flakes. Wet the filter paper with drop of sterile water. Or transplant fresh plasmodia pieces on dishes with oatmeal flakes. b) Once the cultures are set up, seal the edge of each petri dish with Parafile, plastic or electrical tape, and warp the dishes in aluminium foil to keep out light. Incubate cultures at room temperature. c) For the starting points of slime mold, we choose the centre location of residence in Liwa. d) At the same time, put the oats on the postion of existing wells in Rub Al Khali. e) waitting about 24 hours for the mass plasmodium. Keep it at huminity environment. f) Get the final photos of the growth of slime mold. Use Grasshopper to analysis the result. Output： a) mass plasmodium dish; b) simulation result of exsiting wells in Liwa with slime mold.
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The Site for the experiment White dot : Existing wells White Circle: Experiment extract area
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T-0
T-7
T - 14
T - 42
T - 49
T - 56
T - 84
T - 91
T - 98
T - 21
T - 28
T - 35
T - 63
T - 70
T - 77
T - 105
T - 112
Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. Time interval : 7hourse 02.Nov.2014 - 04.Nov.2014
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T-0
T-7
T - 42
T - 49
T - 84
T - 91
T - 98
T - 21
T - 28
T - 35
T - 63
T - 70
T - 14
T - 57
T - 77
Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. every 7 hours 02.Nov.2014 - 04.Nov.2014 Experiment dish D150mm T - 105
T - 112
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Camera: Canon D600 with macro lens Photo took at Urban design studio in UCL. 09.Nov.2014 04:30p.m Experiment dish D15mm
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Water wells Water well network Densely Residential Area
42
0
84
Vector Conversion of the biological experiment result from site area 1
Time Lapse Scale Bar (Hours)
0
2.5
10km
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Camera: Canon D600 with macro lens Photo took at Urban design studio in UCL. 09.Nov.2014 04:30p.m Experiment dish D150mm
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Photos of 2nd experiment
Water wells Water well network Densely Residential Area
42
0
84
Vector Conversion of the biological experiment result from site area 2
Time Lapse Scale Bar (Hours)
0
2.5
10km
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Physa Biological Network Model of Liwa
Compared with the biological model networks and digital shortest path water wells networks, the shortest path is usually ignored the lines of slime mold. The network is similar to the direct and shortest path between existing wells. The network has some detour and also leave a hexagon grid, which gives us more potential to research on this kind of intelligence network. We want to explore this difference. Slime mold goes to the points but it is not go straight from one point to another point. We are more interesting if adding more points with the nodes. We can cooperate with these operation next in the project.
Water wells Water well network Densely Residential Area
Conclusion from the analysis 1. it tends to oscillate and moves very quickly when it meets food source. 2. If slime mold found more food sources in one direction, it slows down the speed of movement toward other directions. 3. Slime mold always surrounds as a shape of circle when it meets food source. 72
42
0
Time Lapse Scale Bar (Hours)
0
5
20km
84
74
The Sabkha Condition in Liwa Oasis The Inland Sabkhas Groups in Liwa Territory
Sabkha, which meaning is salt plate, will be generated in the area where has brackish water supply but the evaporation capacity beyond the water recharging amount. The forms of sabkha will also be affected by the related elements, like ground water supply and wind affect. Inland sabkhas will be formed where the water table lies very close to the surface. Near surface evaporation concentrates salts until a salt crust develops. Lateral growth of this crust causes cracking and uplifting to form halite polygons with uplifted margins. Inland sabkhas can be found throughout much of Abu Dhabi and may be up to 5.5 square kilometres in area. South of Liwa, a shallow water table has promoted sabkha formation at about 80metres above sea level, and also in between a series of large barchans, which become a significant landscape here. This landscape have become the most important sightseeing resource for the local travel industry. In addition, the abundant salt in these sabkha also have other potential application, for instance, structural material and power generation resource.
1.Inland Sabkha in Liwa Region
2.Morphology of Inland Sabha
Reference: Unique Landscapes - Dunes & Sabkha https://www.environmentalatlas. ae/geographicInheritance/ dunesAndSabkha 75
The natural salt powders in the inland sabkhas to the south of Liwa region
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The salt crystal mixed with plaster under natrual condition in the inland sabkhas to the south of Liwa region
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Brine
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Strongly Saline
Strongly Saline
Medium Saline
Medium Saline
Slightly Saline
Strongly Brackish
Medium Brackish
Slightly Brackish
Fresh (Extended Range)
1,000
1,500
Fresh
(Extended Range)
1,500
4,000
Fresh
4,000
7,000
Slightly Brackish
7,000
10,000
Medium Brackish
10,000
25,000
Strongly Brackish
25,000
50,000
Slightly Saline
50,000
100,000
100,000
1,237 258 ppm 1,509 ppm 3,430 ppm 7,410 ppm 298,030 ppm 10,835 ppm
Huwaila
U
S
Map based on: 1,237 complete Hydrochemical Analyses & 4,439 TDS-Values calculated from EC-Measurements
B
H
A
0km
B
S
H
A
Bateen
ai b a r A i d
Brine
The salinity mapping of ground water in Liwa territory.
ua S f o m o d g i n K
A
B
A
H
25km
S
A
Fresh
B
Mashhur
A
Agrab
Qusahwira
75km
IL
100km
Bu Hamrah
Ramlat Ar Rabbad
Mender
Haleiba
Al Faiha
Al Ain
Zaroub
Sultanate of Oman
As Sad
Consultancy Services for Artificial Recharge and Utilisation of the Groundwater Resource in the Liwa Area
50km
Zarrarah
Al Qafa
A
Az Zafrah
S
H
Al Khazna
H a f it ebe l
INLAND SABKHA GROUPS
1,000
0
5
20km
2003
2015
The satellite photos of Liwa territory in 2005 and 2015. These photos represents the existing morphology in Liwa oasis is that the city is developing by occupy the sabkhs.
79
Sabhka Physa Biological Network Model
Connecting with site, sabkha has a number of salt and water, which should be used for human activities. The network between residence and sabkha can be the analog model object to research. The slime mold, as biological model, can help with the algorithm in the sabkha network. Choosing the sabhka in Rub Al Khali to simulate the network between sabhka and residence. Input： oat powder - sabhka in desert slime mold - residence in Liwa Rule： a) we get the location of sakbha in Abu Dhabi desert and choose one part of them. Put the oats powder as the food resource. Use 3D printer make the location accurate and also can control the quantity of oats. b) we get the location of residence in Liwa as the location of slime mold. 3D print the slime mold which with agar to the location of residence. c) we put a light source and a picture of contour map of Liwa as the light control part. Light is the elements to influence slime mold growth. Process: First it became four branches to the food and once it finished, slime mold went back to build another new network. And it may went through the paths where it had been to. Output： a) mass plasmodium dish; b) simulation result of sabhka in Liwa with slime mold. c) The network is similar to the direct and shortest path between each points. But it has some detour and also leave a hexgonal grid.
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Farmland Areas
Sabkha Areas
Topography Condition
Farmland Sabkha 0
1
4km
Materials: Agar, Physarum polycephalum Input approach: Bio-3D print
Materials: Oak powder, bee honey, food colouring Input approach: Bio-3D print
Materials: Photopolymer Input approach: SLA 3D print
The approach of interpretating the site information into biological algorithm
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2.4 Biological Printing &Robotics Input
In order to build a large size of network, the model should be added more ponits. This leads to biological printing and also relate to the digital simulation. The aim of this part is try to make every drop of slime mold and food with the limited quantity and put them in the accurate location. Different locations and different shapes of nodes should be accurate and mechanized, which are in order to build network in site. We create the biological printing part, using 3D printer with converted units to put accurat information of site. As for this part, we made some components to push the materials to the petri dish with the injector. Process a) food resource We tested different kinds of materials into the injection and make the food resources to be controled and be out fluent. Finally, we found honey and oats powder are the most functional ones. b) design the convered units The structure contains bowden tube, the connector and the fixed fame. c) tesing process - Honey is the most obvious one in virsual. But it dissolves in water and for a period of time, the honey is dissolved in water and can not see it clearly. - Oats powder is the one which is obvious and also can atrractive slime mold. Next step, we need try to print the information of site.
82
The Bio-3D print machine
83
[ Fixed Frame â&#x2026; ]
[ Bowden Tube ] Used for guiding the syringe with pushing and pulling the connector between tube and syringe
Used for fixing the positon of bowden tube, making the force direclty to the connector between tube and syinge.
[ Fixed Connector ] Used for connecting the syinge and bowden tube and as well as fixing the position of syinge.
[ Syinge (10ml) ] Used for squeezing food resource to the petri dish. It needs test mant times of different material which can be the food resouces for the slime mold and as well as can be squeeded from syinges. Finally, honey and dissolved oats can be the food resources.
Decomposition diagram of the modified nozzles of Bio-printing machine
84
[ Fixed Frame â&#x2026;Ą ] Used for fixing the positon of syinge, making syinge to be vertical direction.
T-0
T-7
T - 14
T - 42
T - 49
T - 56
T - 84
T - 91
T - 98
T - 21
T - 28
T - 35
The process of application of Bio-printing technology to print the slime mold and oats base on the information of farmland and sabkhas in Liwa.
85
Physa Biological Machines Observation Machine Set Up
Design the experiment apparatus which can observe the process of growing plasmodium branches with fixed time interval. The light is only on when camera takes photos and blocked out by putting the apparatus into a black box.
a) Laser cut the board of four layers and use the screw to fix them. b)On the fisrt layer, place a petri dish of experiment, huminity and temperature monitor beside the dish in order to maintain the suitable environment for its growing c) On the second layer, place the 10x manifying lens which has a swich for light and connect a time controler to turn the light on every 5 minutes in order to obtain clear photos. d) On the third layer, place ipad to record video of slime moldâ&#x20AC;&#x2122;s growth. e) On the fourth layer, take real-time pictures by using digital camera, updating the video every 5 minutes. f)Put The apparatus into a black box to avoid any light. g) Add water to the petri dish one or twice a day in order to maintain the suitable huminity level.
86
[
]
Shading box shelter from the light
[
]
Canon 600D Taking high pixel photos every 5 minutes
[
[
10x magnifying tool with white light
]
[ [
light box with four lamps
]
petri dish with slime mold
]
light control with site information
] The observation device of the biological experiment
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88
89
T-0
T-7
T - 14
T - 42
T - 49
T - 56
T - 84
T - 91
T - 98
T - 21
T - 28
T - 35
The interface of interpretating the experiment result from biological algorithm
90
0
5
20km
The interface of interpretating the experiment result from biological algorithm
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Information Process from Biological Network Algorithm
Because the main output from the biological network algorithm are pictures. In order to have accurate data to do further research and build up design proposal, we need to convert the picture to related vector data. In addition, these data show represent the property of the network of slime mold, and also can fit back into the site. Therefore, in this part, we apply algorithm to convert the picture to density points, and then these points can use to generate the meatball to show the to show the thickness of the slime mold in the picture. Finally, by input these points into shortest path algorithm, we can regenerate the network of slime mold in the biological experiment.
92
Farmland Sabkha Slime Mold Physa Network
0
1
4km
Footprint Area: 1201.945 Km²
Experiment result conversion Step1:
Quantity of Moved Salt 1201.945 km² 25962.02 K*ton
Overlap the experiment picture back to the map of the site
Physa Road Network Length: 797.947 km
93
Experiment result conversion Step2:
Footprint Area: 1201.945 Km²
Convert the picture to density vector points, and apply these points to generate the vector region.
Quantity of Moved Salt: 25962.02 K*ton
Farmland Sabkha Slime Mold Physa Network
Slime Mode Road Network Length: 797.947 km Representative Vector Points: 3780 High Density Areas: 595.069 Km²
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0
1
4km
Farmland Sabkha Slime Mold Physa Network
Footprint Area: 1201.945 Km²
Experiment result conversion Step 3:
Quantity of Moved Salt: 25962.02 K*ton
Apply shortest path algorithm to reconstruct the network of slime mold.
Slime Mode Road Network Length: 797.947 km 0
1
4km
Representative Vector Points: 3780 High Density Areas: 595.069 Km²
95
Ecologocial Relational Model
From above steps, we already can convert the biological experiment results from pictures to vector date and fit to the site. However, in order to provide a convincing urban design proposal, we need to combine the interpretating process with more complex site condition. For example, In the 2030 master plan of Liwa oasis, the population will become 3 times as in present, from around 20,000 to 60,000, and the main industry in Liwa will shift from farming to tourism. To fulfil the requirements for the increasing population and development of industry, we need different amount of resource and infrastructures. And these resources also has relations in between, which means normally to fulfil the single requirement of one resource we also need to produce extra related resource.
Each People Icon equal around 1000 people of Population
Population of Liwa Oasis in 2015: 20196
96
Population of Liwa Oasis in 2015: 51500 - 60000
0.1561
Salt
Salt Energy
Energy
0.1389
Household
0.0500
0.3636
Household
Land
0.0926
0.0121
Land
Energy
0.0005
0.0005
Energy
Household
10.000
0.1
Household
0.010
Land
Salt
Salt
Land
The input-out relation between different resources.
97
Input-output Model
Base on the input-output relation between different required resource, and the population increase target in master plan 2030, we can build up a mathematics ecological inputoutput model, we can predict the general requirements of each resource in each years. This dynamic mathematics model is potential to link to the information interpretating process of the biological experiment, which can help that to develop as reliable urban design proposal.
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42 31 21 10 83 73 62 52 125115104 94 0
2029
2030
2030
2016
2017
2018 2019
2028
2020
2027
2021
2026
2022
2025
2023
2024
2024
2023
2025
2022
2026
2021
2028
to
w
Sa
G
lt (
(1
K*
gy
2019
2027
er
n)
En
2020
*h ) 2029
2018
2030
2017
425
383
397
411
340
354
368
298
312
326
255
269
283
212
227
241
170
184
198
128
142
156
85
99
42
57
71
0
14
28
113
2030
2016
0
40
20
80
60
120
100
160
140
200
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240
280
260
320
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340
220
2016
2030
2017
2030
2018
2029
La nd
2028
m (k 2)
2027
u Ho
2026
se
h
d ol
(p
er
10
0m
2)
2019
2020
2021
2022
2025 2023
2024 2024
2023 2025
2022 2026
2021 2027
2020 2028
2019 2018
2017
2016
2030
2030
2029
0 20425 21500 18275 19350 1 6125 17200 13975 15050 11825 12900 9675 10750 7525 8600 5375 6450 3225 4300 1075 2150
The diagram show the predicted resources direct requirement and total requirement in each year before 2030.
99
Biological Experiment Information Interpretation
From the research of the territory, we understand the existing morphology of Liwa oasis is expanding by occupying sabkha. In the process of occupying, people need to clean the salt on the surface, to avoid the high salinity environment to damage the building structure. In addition, we also found energy has become the main force to drive the development, and salt is potential to a source of energy. In the other aspect, the behaviour of slime mold is actually re-distributing the resource on site to improve the transportation efficiency. To sum up, these condition give us the reasons to translate the process of the biological experiment as and analog model of people expanding the city by re-distributing the salt material and occupying sabkhas. The data from the input-output model is the prameter to control the quantity and progress of this interpretation.
100
0 1
Farmland
New Farmland
Sabkha
New Residential Area
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station
4km
Biological Experiment result Interpretation: Stage 1: Physa agent occupying part of the sabkhas as scanning the territory.
101
Biological Experiment result Interpretation:
Farmland
New Farmland
Sabkha
New Residential Area
Stage 2:
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station
The sakbhas which have been clean become the new farmland; the salt material is being to move to othe parts of sabkhas.
102
0 1
4km
0 1
Farmland
New Farmland
Sabkha
New Residential Area
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station
4km
Biological Experiment result Interpretation: Stage 3: The salt resource has been concentrate in the other part of sabkhs; a infrastructure network have been appear.
103
Biological Experiment result Interpretation:
Farmland
New Farmland
Sabkha
New Residential Area
Stage 4:
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station
Onece the salt material has been re-distribute, the salt power stations start to appear in the high salinity field; the power cable network also emerge to connect this area, and also expand to the farm land area to provide energy.
104
0 1
4km
0 1
Farmland
New Farmland
Sabkha
New Residential Area
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station
4km
Biological Experiment result Interpretation: Stage 5: The residential building have been developed near by the famrland area; another infrastructure network (cable, water pipe, road) emerged to connect both areas.
105
Physa Self-organized Network City
By applying physa self-organized biological algorithm, we can generate the urban design proposal contents different time stages, and provide suggestion on programs division, location, and material quantities. These system allow designers cope with the complex territory conditions and also fulfil the society develop requirement. In addition, instead-of traditional top-down city morphology, this methodology allow designers to develop new self-organized decentralized city.
Farmland
New Farmland
Sabkha
New Residential Area
Physa Footprint
New Salt Field
Infrastructure Network
New Salt Power Station 0 1
106
4km
The interface shows the development progress in different year from the biological algorithm
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The interface shows the development progress in different year from the biological algorithm
109
Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis
Biological Self-organized Network Algorithm - Physarum polycephalum
Autonomous Network Builder Swarm Robot
Computational Self-organized Network Algorithm Particles simulation
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
â&#x20AC;˘
The Collective Intelligence inside the Physarum polycephalum
â&#x20AC;˘
The Swarm Robot Gatherer and Builder Managed by Physa Algorithm
Autonomous Network Builder - Swarm Robot The Collective Intelligence inside the Physarum polycephalum
112-117 The Swarm Robot Gatherer and Builder Managed by Physa Algorithm
118-127
The Collective Intelligence of Physa Biological Algorithm The Swarm Movement of Physa Cells
The Physa Biological Algorithm could be operated and run under different scale. Under the higher than 100 time amplification microscope,, we can see the cells of slime mold moved in a very organize way. The swarm movement of cells resulted in an oscillation fluid, and transform the diameters of the tube frequently.
This organized swarm behavior was operated under a real time autonomous system. This property give us a new potential to extract detail real-time information from the Physa biological algorithm in a micro scale, rather than only observe the pattern of the macro network.
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T = 20s
µm
5µ m
18 5µ m
T = 15s
17
17 0
T = 25s
17 0
µm
T = 10s
µm
T = 5s
16 5µ m
T = 0s
20 0
19
19 0
5µ m
µm
T = 0s
T = 30s
T = 35s
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The Material Collecting and Converting Behaviour of Physa Cells
In the experiment about the slime mold growing into a surface made by oats powder. We can see slime mold gradually clean and absorb the surface to constitute itself network structures. It give us an inspiration about apply slime moldâ&#x20AC;&#x2122;s collective intelligent to organize swarm robots to collect salt or other resources. The pattern of its macro tube could be applied as the suggestion of the master plan about the developing strategy of series of sabkha. In the meantime, the mechanism of slime mold cells movement in micro scale, could be extract as the algorithm of organize swarm robot.
114
T = 163h
T = 31h
T = 32h
T = 33h
T = 34h
T = 35h
T = 36h
T = 37h
T = 38h
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Salt Harvesting from Sabkha
In some condition, salt is a precious resource in desert, and people may manually collecting with suffering of cruel climate condition. Although this scenario does not happen in Liwa now, but when salt is a potential resource of energy, it give us an argument to apply more coefficient way to harvesting salt from sabkhas. In the above research, we found the resource harversting behaviour of cells of slime mold have similarity of nomadic peopleâ&#x20AC;&#x2122;s collecting activity in desert.
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Picture reference: Salt harvest in Ethiopia http://www.ethiopiafirst.info/etnews/ index.php/component/k2/item/266coach-sewnet-bishaw-and-adanegirma-will-be-guest-of-honors-ataesa-one-dc2013.html
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Applying Swarm Robots as a Agent to represent the Resource Scanning and Collecting Behaviour of Physarum or Humman
What the physa biological algorithm and ecological model provide are the design proposal. To realize this proposal, especially in building scale, we need to find out the further develop about the material system on-site. Our above presentation demonstrates that slime mold has the potential to be a biological algorithm to provide design instructions. So to realize this design proposal, we introduce small robots as a construct method to interpret the behaviour of slime mold. Because the collective intelligence of the organization of the cells of slime mold has a lot of similarities with the organization mechanism of swarm robot.
Swarm robot conceptual model integrated with alive slime mold
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bio-algorithm (slimemold)
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Relational diagram to show the connection between swarm robot, slime mold and territory.
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Conceptual model of group of swarm robot moving on territory.
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2 1.Swarm Robot prototype 1 Testing the remote control and walking function. 2.Swarm Robot prototype 2 Installed with tank track and shovel, to test the capacity for moving on sand and collecting material on desert.
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Interface to control the swarm robots from the biological experiment output
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Interface to control the swarm robots from the biological experiment output
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Diagram represents how the biological algorithm output about redistribution of material will manage the swarm robot working in territory.
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Recording represents how the manage the swarm robot redistribute the salt material on-site based on the biological algorithm output.
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Physa Biological Algorithm
walking system mini camera
Arduino YUN Board
SG90 servo
Salt Battery
shovel
Swarm robot conceptual model integrated with alive slime mold
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Swarm robot conceptual model integrated with alive slime mold
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Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis
Biological Self-organized Network Algorithm - Physarum polycephalum
Autonomous Network Builder Swarm Robot
Computational Self-organized Network Algorithm Particles simulation â&#x20AC;˘
Digital Simulation from Physa Algorithm in Specific area of Liwa Territory
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
Computational Self-organized Network Algorithm - Particles simulation Digital Simulation from Physa Algorithm in Specific area of Liwa Territory
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The Particles - Site Interaction Behaviour of Physa Digital Algorithm
Because the biological simulation has certain limitations in the accuracy of the data processing and recording, we also manipulated the digital simulation as comparison and supplement. By setting up the agents as the swarm robot gatherer, and emit the collectable particles as the position of sabkha, we build up the simulation of swarm robot collection behaviour. In this simulation, we can clearly observe and analyse how the agents collecting materials and interact with the territory. These may provide us more suggestions about how to program the swarm robot and design the salt infrastructure network. Generate Robots Generate Robots
keep moving to other parts
Generate Robots Generate Robots Generate Resource
Generate Robots Generate Resource
Generate Resource keep moving to other parts
keep moving to other parts Generate Resource loopdistance Test
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Attract resource Leave the trails
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Attract resource Generate Robots
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Digital simulation of slft-organized network With Physa Biological Morphology and Liwa Salinity Information
Once we decided applied swarm robot as the builder of our urban design proposal, we found we need more detailed information inter of smaller scale and accurate time. The biological algorithm is capable in large scale, but has many technical limitation in small scale. In the other aspect, digital simulation has advantage in provide very accurate feedback. Therefore, we extract the mechanism from the behaviour of physarum to developed a related digital algorithm. These algorithm will run in a smaller scale of territory and mainly affected by the salinity information.
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Salinity mapping of Liwa territory and the zoom in area for digital simulation
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Digital Simulation Stage 1: The particles (colour lines) are scanning the territory to find the resource (red dots).
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Digital Simulation Stage 2: The particles (colour lines) were attracted by resource (red dots, and then changed their behaviour pattern.
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Digital Simulation Stage 3: The particles (colour lines) are redistributing the resource (red dots) in the sabkhas and leave the traces.
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Digital Simulation Stage 4: The digital simulation result can be interpreted as a more detail design proposal in a smaller scale.
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The process of the digital simulatio
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Ecological algorithm in Territory Scale
Nomadic Self-organized Network Oasis City - Liwa Oasis
Biological Self-organized Network Algorithm - Physarum polycephalum
Autonomous Network Builder Swarm Robot
Computational Self-organized Network Algorithm Particles simulation
Material System Composite Structure Ecological algorithm in Urban Cluster Scale
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The Resource Distribution in Liwa Territory
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The Potential Energy Producing Application of Salt Material
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The Structural Research of Palm Tree Fiber and Salt
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The Structural Research of Bioplastic
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The Composite Structure from Physical Experiment to Digital Simulation
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The Visualization of Physa Network City
Material System - Composite Structure The Resource Distribution in Liwa Territory
144-147 The Potential Energy Producing Application of Salt Material
148-149 The Structural Research of Palm Tree Fiber and Salt
150-155 The Structural Research of Bio-plastic
156-169 The Composite Structure from Physical Experiment to Digital Simulation
170-183 The Visualization of Physa Network City
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Resource on the site
In the Liwa area, some part of desert hills are covered with gypsum-cemented layer indicated of former sabkha conditions which result from the evaporation. Lightly cemented dune sand, whose bedding attitudes indicate sand transport towards the south area which is desert called Rub Al Khali, the site of this project, is exposed in the flanks of these mesas; similar dune sand can be seen in pits dug below the gypsum-cemented surface of the interdune between sabkhas, which are at an elevation of some 80 to 90m above sea level. (I.A.Abed & P.Hellyer, 2001) During the site visit, we could actually discover gypsum cement (fig.4) which looks like clod of dirt but is not strong enough to being a structure itself. Not only the gypsum-cement, there is gravel sheets which cover large areas and occur between linear sand dunes. They consist primarily of pebbles and gravels of quartz, limestone; pebble from the basement complex, and gypsum that are embedded in silt and sand. There are some older gravel sheets that consist of lacustrine deposits, loess flats, and alluvium. Mostly gravel sheets are characterized by lack of any vegetation (A.Kumar & M.M.Abdullah, 2011). The locations between gravel and gypsum are quite close compare with the location of other resources. In fact, the location of gravel is remote from the vegetation area where we found deposits of farmland such as date seeds, fiber and pieces of palm tree. Since farming is the main economic resource in Liwa oasis, most deposits are from the date farm which is on the Liwa crescent. The fiber and wood pieces from farmland have been dried because of high temperature and arid environment. The wood pieces are already has been using as frame of structure with the characteristic of flexibility itself (fig.3). Therefore, this deposits have potential to use as a frame of our structure system in the future. Additionally, the most important resource among these is salt from the sabkha. As the salinity in Liwa area was recorded 3,500~6,500 mg/l, dry salted crust is discovered many part of the desert by evaporating on the sabkha (fig.6). The salt is main resource of the material system combining date farm.
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with the deposits from
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Salt from the Sabkha as a material
As mentioned that salt is the main material resources can be easily found on the site, we firstly test growing salt crystals on sand to observe the shape and process of growing. Since the salinity water on the sabkha area comes from the sea water, we have decided to use sea salt (same as table salt) and Epsom salt which crystal structure makes its appearance similar to that of sea salt but have fast salt crystallisation and bigger crystals than other salt types. To compare with different crystals, we also test to use rock salt as comparative experiment. A)Salt crystallisation on sand - Salinity rate: 35% (approx.) - Amount of salinity water: 60ml (approx.)on each petri dish - Salt type: sea salt, Rock salt, Epsom salt Rule Keep the similar condition to the real sand dune and place the water on the sand. Observe the crystallisation until all the water has evaporated. Experiment period: 1week Result - Each different crystals appeared as the original crystal shapes. - Sea salt crystals looks the most similar to that on the site (salt crusts). - Growing rate: Epsom salt crystals > Sea salt > Rock salt B) Epsom Salt crystallisation - Salinity rate: 40% (approx.) ; Almost maximised salinity rate - Amount of salinity water: 60ml(approx.) Rule Same as experiment A Experiment period: 3weeks Result - After 2days: crystallisation happened on the edge of the petri dish. - After 2weeks: the petri dish almost filled with crystals. - After 3weeks: the colour of crystals turned from transference to white. Additionally, salt can be used as a resource of energy production. One of the typical case of this is molten salt storage which will introduce on the next page.
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30% Salinity water Photo taken after 3 weeks of starting date
Seasalt crystals on sand
Salinity : 35% Period : 1 week
Salinity : 40% Epsum salt
Seasalt
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Detail on the edge of petridish After 3 weeks
The salt crystal samples in natural conditon.
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Liwa Oasis
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The “ Saltygloo” p roject i s an i gloo m ade o f printed translucent m odular salt panels. I mage Courtesy of Matthew Millman. Emerging Objects specializes in using innovative materials for 3D p rinting. I n addition t o salt, t he company has experimented w ith alternative materials such as cement polymer, nylon, acrylic and wood.
ails in Details the site in the site 2
Details in the site Salt purifySalt purify
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1-3: The natural salt condition in sabkhas in the rub al khali desert.
Way to purify Waysalt to purify salt
ails in Details the site in the site 4-5: The traditional salt purification process.
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Salt site bricks by 3D-printing ails in Details the site in the
Commenting on t heir use o f salt, t he company shared, “The translucent qualities of the material, a product o f the fabrication process and t he natural properties of salt, allow for natural light to permeate the space and h ighlight t he assembly and s tructure and r eveal t he unique qualities of one of humankind’s most essential minerals.”
Way to purify Way tosalt purify salt
Way to purify salt
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3 1.Victoria solar park, Victoria, Australia. This solar park using salt as material to storage heating energy to provide electricity at night. Picture reference: http:// inventorspot.com/articles/10_ gigantic_solarpowered_ project_10559 2.Research model of salt water battery. 149
Fiber system from palm tree
As we discovered deposits from date farms, fiber can be the main structure to grow salt crystals on its surface. On previous part, we tested to grow salt crystals on sand and realised it requires some shape of structure for the purpose of this project which is to build infrastructure on site. Fiber existed on the site is not as much as strong than the pieces of wood but the best advantage of it is the thickness. Thanks to the very thin fiber like hair, the shaping of form is easy so it can be created various type of shapes. Firstly, fiber catalogue is created by consideration of density, direction, diversity and tension between each fibers. This will plays as basic foundation to build small units of structure and explore the growth of salt crystals on different patterns of fiber. (p.146) Secondly, one small experiment has been conducted to prove the relationship between density of fiber and rate of salinity water. As a result, it is proved that higher density of fiber and higher salinity water help the salt crystals appear more during short period. (p.147) Not only that, crystals on fibers is connection link between them clearly seen through micro view. Therefore, it can play a role as connection link on fiber and it make the structure stronger after exposing the real condition of the site which has hot and arid environment. (p.148) However, there are a few weaknesses about forming the structure only using the fiber and salt crystals. Above all things, the strength is not enough to being structure itself and shape would be changed after totally dried up. So, If there is frame which can support fiber and crystal material, the material of salt and fiber would covered the surface of frame with helping use deposits on the site. The material for frame will introduce on the following chapter.
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Branch of palm trees in Liwa oasis
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Patterns of fiber Density of fiber Gap between fibers Rate of tangle
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Catalog of Fiber Pattern
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Average distances between fiber Salinity Experiment operation time
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Catalogue of salt crystal with fibers
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Catalogue of fiber mixed with salt water under microscope
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Fiber mixed with salt water above sand
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Bio-Plastic From the Starch in the Date seed
In the terrain research and study, we found that the massive production of dates ,as the United Arab Emirates (UAE) and people love food, that its core is mainly composed of starch, and the starch is the main ingredient of bio-plastic. In the Liwa area, a large number of farms, people grow what need less water, so dates as a sugar high crop becomes one of the major crops. Liwa farms area produce a large number of dates, and process it for coastal city as a good provided commodity, and at the same time as a special dessert, became a symbol of Dubaiâ&#x20AC;&#x2122;s food. In our research, dates provided enough starch to become an important part of building materials for massive production. We can get not only starch, the material from the date-palm trees, but date palm leaves are rich with fiber, as a metabolic waste material in date palm farms. We combined the two material with producing methods of composite material as reinforced concrete.
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The date from palm tree in Liwa oasis
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Material
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Ingredient : Starch Glycerine Vinegar Water Process
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Plastic promotion from within the strength of the whole system, made in the shape of an organic structure, at the same time, there is a tension between the online and line and pull.
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Plastic and system of wool blend to enhance the strength of the yarn itself can shore up the metal net rail network
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Fiber and plastic system combine to form the more toughness and strength of the material system, but the control of fiber is very difficult.
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Fiber and plastic system combine to form the more toughness and strength of the material system, but the control of fiber is very difficult.
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Above is our experiment decomposition of video camera, we controlled experiment material through the layering and different steps in the experiment, when after the completion of the final step, we use the natural properties of gravity to control the final modelâ&#x20AC;&#x2122;s form.
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Fiber and plastic system combine to form the more toughness and strength of the material system, but the control of fiber is very difficult.
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Fiber and plastic system combine to form the more toughness and strength of the material system, but the control of fiber is very difficult.
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Physical Experiment integrated with Digital Simulation
Simulation is used as the further development of material test, at the end of a large number of experiments, the experimental results canâ&#x20AC;&#x2122;t express design process as a system and logic, so the simulation is refining the experimental steps and experimental stage results will be different in process. Simulation starts from the design of framework. As for different experiments to simulate the process, we use different algorithms in the digital way for different stages of the used materials have a clear understanding. After the whole simulation results, we adjust slightly at different stages, and it can be showed in the final results which we can discover the difference. We will have deeper understanding about both simulation and material by contrast. At the same time, simulation deepened our controlled stage for material testing process, simulation and physical experiment combination form a cycle of citations of each other. Simulation guides physics experiment in details and adds more control factors, physical experiment feedback simulation more data at the same time, in order to strengthen the relationship between simulation and reality.
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Divergent Growth of Monomer Mycelium
Catalogue of complex fiber system in digital simulation
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material simulation material simulation friction of material structure
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Catalogue of complex fiber system in digital simulation Scale: 10m Unit A Unit B Unit C Unit D
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Catalogue of composite system in digital simulation
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Composite system case study Rate: 200 Seed: 187 Coagulate force cutoff distance: 10 Attraction strength: 5v Coagulation size: 0.5 Coagulation drag: 0.5 Repellent strength: 0.1 177 Get neighboring particles cutoff distance: 21 Max number points: 30 Speed: 5
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Physical model of digital composite case study
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In this phase of the simulation, we will have material test, simulation data, maps and information together, which are derived from the information of a large area map with simulation data, and the field data is combined with the simulation of material test which can form a complete system of construction, for the design phase of the urban planning, construction and generated form are early derivate and projected
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Morphology from trace: Second layer of paths were emiited from the frame, and then the volume (bio-plastic) will grow and wrap all the structure.
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The case model of generated structure.
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23105 m2 350 People 420 kg Bio-plastic 731 kg Fiber
3040.5 m2 97 KW·h 671 kg Salt
50304 m2 657 People 1247 kg Bio-plastic 957 kg Fiber
6347 m2 198 KW·h 1756 kg Salt
1347 m2 75 KW·h 605 kg Salt
24570 m2 547 People 1075 kg Bio-plastic 875 kg Fiber
2340 m2 75 KW·h 573 kg Salt
8972 m2 750 KW·h 3047 kg Salt
7884 m2 752 People 648 kg Bio-plastic 1054 kg Fiber
6207.5 m2 60 People 201 kg Bio-plastic 303 kg FiberV
3540 m2 157 KW·h 861 kg Salt
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467 m2 60 KW·h 87 kg Salt
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The conceptual scenario of Physa city
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The construction and Salt crystallization process of the building prototype and the involver in each stage.
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Plan View of Urban Scale
In the Physa urban design proposal, the swarm robots, material distribution condition and bio-plastic composite structure will constitute a new dynamic ecological system. This system will build up the new urban morphology which link the requirement of society development to the complex local ecological condition.
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Swarm Robots
Slat Power stations
New salt fields
Bio-plactic composite Household
New Farmland
Bio-plactic composite Tourist service center
2807 m2 1876 kg Salt
3772 m2 250 KW路h 3047 kg Salt
2772 m2 268 Kg Salt
1634 Robots Moving 23862 Kg Salt
1634 m2 876 kg Bio-plastic 25 Person
23772 m2 877 kg Bio-plastic 512 Person
1107 m2 106 KW路h 576 kg Salt
28707 m2 27876 kg Salt
1974 m2 25 People 877 Kg Bio-plastic
Design Report
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Design Report
Physa CITY: Sabkha Urbanism
1. Self-organized Network Oasis City Liwa Oasis Our site, Liwa oasis, is located on the north edge of Rub Al Khali desert of UAE. Unlike the other typical top-down developed big cities of UAE, such as Dubai and Abu Dhabi, the morphology of Liwa oasis is more related to the resource distribution and topography condition in the desert. In Liwaâ&#x20AC;&#x2122;s area, we connected the existing water wells in the desert. So we can find the Liwa oasis was emerging with response to the resource distribution network, as a symbiotic network system. In addition, the existing morphology of Liwa also has strong relationship with the salt resource distribution on site. Here are two satellite images gaped ten years, we can see Liwa oasis was developing by cleaning and then occupy the sabkha. Because the sabkha are basin areas, which are more suitable for settlement, even people need to clean the salt onsite to avoid the erosion to building structure. Because Liwa is a specific network city which is sensitive for resource distribution, it will allow us use one specific microorganism, Physarum polycephalum, which has the collective intelligence to autonomously generate optimized network system basing on the food resource from the environment condition, to simulate and develop a new physa network city morphology. In the morphology, we also need to introduce other elements to make it available. The swarm robot will be the autonomous builders to realize the design proposal. To interrelate the material on site, we developed the material system to figure out the composite structure. And then, the digital simulation will allow us the integrated the above research elements with sites. And finally all the elements will be feeded back to site again. With different kinds of testing, we begin to find out the specific aspect of site which should be input to the biological algorithm. So, we found the most significant Landscape and also resouce in Liwa area is the inter-dune sabkha groups.
Sabkha, which means the salt plate, generated by the over evaporation of salt water. The soil of Sabkha contain abundant salt and plaster. These material are potential to applied as energy producing and construction material. In fact, the existing morphology of Liwa also has strong relationship with Sabkha. From satellite images gaped ten years, we can see Liwa oasis was developing by cleaning and then occupy the Sabkha. Because the Sabkha are basin areas, which are more suitable for settlement, even people need to clean the salt onsite
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to avoid the erosion to building structure. In addition, in the master plan of 2030, the population of liwa will increase from 20,000 to 50,000. We suppose that is meaningful to apply physa biological algorithm to simulate this expanding.
2. Biological Network Algorithm Physarum Polycephalum In order to output information of network system, we want to find an algorithm which can help us to deal with various input and output. Therefore, we found the creature--Slime Mold to help us to continue the ‘biological algorithm’. It is a singlecelled organism, a cell, which joins together with other cells to form a mass supercell. Some scientists observed the slime mold activity like the Tokyo’s railway analog model, but they are not focusing on the city itself. From the urban designer perspective, what we are interested is to use the self-organizing algorithm of slime mold into urban design and make a shift for the future cities. Frei Otto proposed rules of the muscle memory in nature. It is similar to the growth of slime mold, which is first it expands, then concentrate on one nodes, at last it becomes an optimized network. It’s biologically fascinating, it’s computationally interesting. So until now we want to find the connection between slime mold and city focusing on the site. We cultivate the slime mold, and tried to get the basic features of slime mold like testing food resources, and how to control it. The reason we want to get the features of slime mold is to create information from the site to the slime mold in different ways. The idea of this is to analog the resources on the surface. But it is not simply surface. It contains numbers of points in the specific area. Then we went to add more points and lead to biological printing. We want to record the movement of slime mold, so we made physa bio algorithm apparatus. Because we want the location of each part to be accurate and mechanized. We create the biological printing part. We remould 3D printer and use bowden tube to guide the syringe. And the bowden tube pushes and pulls the connector between tube and syringe. Once we manage to understand different types of resources, we look up to calibrating existing network for example London network analog model. Choose about 30 tube stations in London. Compared with the lines and other calibration networks, different types of paths are slightly different. They usually ignore the lines of slime mold in the transport systems. We want to explore this difference. Slime mold goes to the points but it is not go straight from one point to another one. It seems to take some detour. We contrast seven aspects of the network efficiency.
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Finally, we found that the efficiency of the path of slime mold is between that of London road and underground. We are more interested if adding more points with the nodes. We can do these operation next in the project. Connecting with the site, we made the physa biological network model in Liwa. We collect the information from the site with the existing wells and residence in Liwa. We found that slime mold tend to the area with full of resource. When it finished the first loop it will also come back to the former area to find more food. The food here we can described as the water in existing wells. This gives us the potential that the network built by slime mold can guide to collect information in desert and find resources. We put food resource to the location of sakbha in desert and slime mold in the location of residence in Liwa. We put a picture of contour map as light source. As for this part, we made some components to push the materials to the petri dish with the injector. Here is how does it works. This time we use the injector to give food resource to the accurate location. The sabkha analog model build a network between residence and sabhka. The existing road system build a network between farmland and residence. The most obvious and effective network part is near the residence and the element of both networks is also residence. This also proves that we can insert new information to the existing network and create more complex and optimized network. What does slime mold do is to redistribute the resources from the residence part to the sabhka part. Optimizing networks is help to make effective use of resource and transfer information in desert.
3. Autonomous Network Builder Swarm Robot What the physa biological algorithm and ecological model provide are the design proposal. To realize this proposal, especially in building scale, we need to find out the further develop about the material system onsite. Our above presentation demonstrates that slime mold has the potential to be a biological algorithm to provide design instructions. So to realize this design proposal, we introduce small robots as a construct method to interpret the behavior of slime mold. First, the site condition of desert decided the walking mechanism of robot and it could execute several movements based on the features of slime mold. Then, we extract the swarm behavior rules of particles inside the tubes of slime mold. This algorithm will guide a collection of robots to construct on the side. It includes the six-legged walking system to adopt to the environment, and there is a shovel to
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push the sand and salt to a certain place. This video shows the basic movements of robot, like forward, backward, turn left, turn right, and also draw some patterns by leaving its trace on sand. Then we did some experiments on sand territory. This one shows how the robot find salts that is located in the same place as in computer. And another experiment is about digging salt on the site, and forming certain structures. And then we apply the algorithm of particles moving inside slime mold into swarm robot, which will emit signals when they arrive food or home areas. Then these signals will be transmitted among the robots until they form a direct connection between food and home. Here is a digital simulation we use these algorithm to run the swarm robots collection on site. We can see the robot go between the Sabkha region and the urban region, and constitute the complex network. These routes could be combined with material constructing function and then be converted from simulation to reality, to the movements of multiple robots, in architectural scale.
4. Computational Network Algorithm Particles simulation In order to develop the further potential of physical experiment, when need to convert that into the digital simulation to do deeper analysis and research. We conclude different components of physical model into 4 parts, frame, surface, volume, strands. We learned some properties from these 4 parts, and make the digital parts to simulate and represent them. We can change some parameters like speed, velocity, force and others in digital model, make more potentials which cannot be made in physical model. Also, we can analysis different results by controlling certain parameters, and we compare these feedback to physical model, and improve our physical model. Firstly we build skeleton structure to bear the detailed-material system as bioplastic bubbles or bioplastic fluid which may dry and harden and make the physical more complexity and hierarchy. Second one: Fiber strands This level simulates the fiber details, give the whole simulation more possibility and hierarchy, combine the separate parts together and give the simulation extensions on the edge of bottom. Third one: bio-plastic surface The surface comes out of frame provide a complex level of the simulation and simulate the bio-plastic flexible property. Fourth one: salt-crystal volume
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The simulation try to reproduce the salt crystallization on the surface and frame layer which makes muscles and skin on the skeleton structure filling the spaces of growing system. We tried using metal mesh as a frame of structure to know whether the materials follow the frame or not. In these photos, the salt crystals tend to follow the line of this mesh. This one shows that the comparison of fiber strands and salt-crystal volume. We design and control some parameters and get this morphology. Then we add polygonizer on strands to simulate salt crystal volume. In order to get different morphology and more differentiation, we change the speed of strands, then we get another kind of morphology .This is a perspective view from the top, you can find more details in the whole system. Actually, in the simulation process, the structure grows from the boundaries to the center. In this drawing, i use different colour to indicate different hierarchy in the same structure. The frame system is more controlled by the junction points as more regular form. However, the volume system develop from that represent more flexibility and complexity.
5. Material System Composite Structure and Multifunctional Network Landscape 5.1Material system â&#x20AC;&#x201C; Salt Crystallization In order to use the salt as a materials to build structure on the site, we firstly made a little sand dunes on the petri dishes and then poured three different kinds of salt. These photos are taken after 3days of the start date. The surface all firmed up and the salt water all evaporated. Compare with the sample from the real Sabkha, these look similar to the real samples. In this term, we have tried to find how salt crystals can be used as a part of the structure. These photos are materials which mixed with glue and gave colours on the surface. Although glue helps the salt crystals become hard forms like a brick, we have realised it is not suitable to make shape of structure itself. So we tried using metal mesh as a frame of structure to know whether the materials follow the frame or not. In these photos, the salt crystals tend to follow the line of this mesh. After that, we grew salt crystal on the different density of gauze. As a result, if the gaps between each lines are bigger, the crystals would grow bigger. We started to grow salt crystals on the very thin fiber with some gaps between each fibers, we considered it could be a structure, helping to connect the each fiber with crystals.
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5.2 Material system â&#x20AC;&#x201C; Bio-plastic According to the research and the field trip, we have a clear observation of the site and find useful information about how to develop a material system. As mentioned before, we find elements from date tree and palm forest, which provided us enough resources to build a material system based on local environment. With starch from the date and fiber from the palm tree leaf, we can build a complex structure with bio-plastic and fibers. Firstly, we find that with different proportion of bio-plastic, the basic material, will show different property. We use a dilute bio-plastic recipe mixed with foaming agent, and this turns out to be a transparent bubble structure which is lightsome. And the details inside has hierarchy and differentiation. Connect with result coming from the slime mold experiment, we find the similarity between the hexagon bubble network system and the slime mold optimal network. So we decide to make it a physical experiment solution to simulate the track of slime mold growth.
After that, we are thinking about a new system as prefabricated part in the oasis and build in the Liwa Sabkha. We started to use salt crystallization to be a binder between two pieces of prefabricated bio-plastic material part, which will be a whole system of constructing.
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Appendix A: Study of Sand Dune Morphology
200-205 B: Robotic Input of Physa Biological Algorithm
206-209 C: Phsya Biological Algorithm Research Case - Simulation of London Transportation Network
210-215 D: Swarm Robot Digital Organiziation Algorithm base on the Physa Biological Algorithm
216-219 E: Digital Simulation Research of Complex Strands System from the Site Information of Liwa
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Sand Dune Types Distribution in Desert The morphology of Sand dune will be mainly affected by wind powder and sand supply. Due to the different conditions of these elements, different types of sand dunes will be generated in desert.
Global wind 50 m height from MERRA 3.0M/S - 6.0M/S Sand Supply Value of Sand
Wind Spped of Sand Dune 0
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Sand Dune Morphology with Wind Speed and Sand Supply Influence
Sand dune is the most common physiognomy in the desert. The morphology of sand dune are mainly related to several important elements: 1.
Direction of winds.
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Speed of winds.
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Amount of sand supply.
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The property of ground surface.
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Rain fall amount.
6. Although most areas of desert are arid and blustery, but the details climate and geological condition are still very diverse. These diversities result in the various types of sand dune in Rub Al Khali Desert. However, the formation of sand dune is a long-term process. Therefore, even the climate condition some regions of the desert have changed, the form of sand dune still represent the condition in historical period. In addition, the morphology of sand dune is also a more complex process, which is not only affected by the changing climate, and represent the interaction of physical, chemical and biological processes.
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Barchan Dune
Transverse Dune
Linear Dune
Star Dune
Wind Direction
Sand Dune Morphology with Wind Affect
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Digital Simulation of Sand Dune Morphology with Wind Speed and Sand Supply Influence
A basic simulation of sand dune formation could be described by the Werner’s model. In this model, a dune field is represented by a grid of square cells, each containing a stack of slabs representing the height of sand at that cell. The main parameters in this model include initial sand dune height, wind speed, the property of the surface. In the beginning of simulation, the amount of each slab on each grid will be randomly setup under an average amount. After the simulation begin, the win direction will keep from left to right, then the “wind shadow” will be casted at 15 degrees by sand slabs. All the sand slabs not under the “wind shadow” will be moved from left to right with certain amount. After the movement, the sand slabs will be decided to deposit or continue to move, based on the situation of the current gird, about the surface property and if it is under the wind shadow. In addition, if deposition or erosion creates a slope greater than the angle of repose, normally is 1/3, the appropriate surrounding slabs will be moved downslope one cell (avalanched), expanding outward as necessary. After a period of running, the simulation will normally stay on a stable condition. Through applying the parameters related to certain kind of sand dunes, several typical sand dunes could be represent in this simulation.
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Wind Speed = 3 Sand Supply = 5
Barchan Dune Ticks = 1
Ticks = 15
Ticks = 170
Ticks = 320
Ticks = 500
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Wind Speed =10 Sand Supply = 20
Huge Linear Dune Ticks = 1
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Wind Speed =10 Sand Supply = 3
Small Linear Dune Ticks = 1
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Ticks = 1000
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Wind Speed = 6 Sand Supply = 20
Transverse Dune Ticks = 1
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Ticks = 720
Ticks = 1200
Digital Simulation of Sand Dune Morphology with Wind Speed and Sand Supply Influence
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2.4 Robotics input
Focus on the process of inputting data into the expeiment, we use robot to help this part instead of artificial way. The artificial way injecting slime mold and food is not accurate and has errors when the points more than 50. The aim of this part is try to make every drop of slime mold and food with the limited quantity and put them in the accurate location. Then we can design the experiment with different path with the robot. Process a) Design the device to inject liquid Use rotary movement of motor to push the nut in the device and then the downward force push
the syringe to drop the liquid. Before putting the oats, test the concentration
of oats to make sure every drop of them be the suitable quantity. b) Programme codes for the ardurino to control the device As for the ardurino code, edit the motor to move every 2 seconds and test the speed of movement. c) Use GH to edit the track of robot Make Rihno files to draw the track of experiment and programme the GH to control the robot. And give the true or false signal to connect with the ardurino. d) Combine all the process together to test and debug the robot.
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Universal Robots UR10
The M5 Screw and Nuts used to fix all the layers
The mechanical diagram of the end effector of the robot The end effector of the robot 208
12V Stepper Motor with Cable
M8 Screw and Nut Used to Push the Syringe
Plastic 100ml Syringe with Oats Liquid inside
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Physa Biological Network Model Advantages --The Calibration experiment with transportation network in London
All the experiments using Slime mold has been done by scientists. They observed slime mold activities in a scientifical way, not focusing on simulated obeject such as cities. From urban design perspective, we want to use the biological algorithm created by slime mold into the urban design. What we are interested in is to use the intelligence self-organizing algorithm of slime mold into urban design making a shift for the future city networks. The aim of the project is the connnection between urban design and biological algorithm. To calibrate this algorithm, we selected London as developed urban area to compare the biologically generated network by slime mold with the existing manmade network in the city. Input： - Fine oat powder with clean water, slime mold Process： a) Cultivate the slime mold from Sclerotium. Use sterile forceps to transfer a piece of filter paper containing this resting stage to a dish with oatmeal flakes. Wet the filter paper with drop of sterile water. Or transplant fresh plasmodia pieces on dishes with oatmeal flakes. b) Once the cultures are set up, seal the edge of each petri dish with Parafile, plastic or electrical tape, and warp the dishes in aluminium foil to keep out light. Incubate cultures at room temperature. c) Choosing about 30 stations in London, it contains the tube stations and train stations. For the starting points of slime mold, we choose the location of three stations, the King’s Cross Station, the Victoria Station and the Waterloo Station, which are the comprehensive station with the function of tube, train and bus stations. d)At the same time, the other position of points, we put the liquid of oat. e) waitting about 24 hours for the mass plasmodium. Keep it at huminity environment. Output： a) we contrast the network of slime mold, London road, London underground and the proximity of each stations. b) From the study of connecting and occupying, we calculate the connection amount, average distance, detour of different networks seven aspects and so on. c)we found that the efficiency of the path of slime mold is between that of london road and underground. Compared with the lines and other calibration networks, different types of path is slightly different. it is usually ignored the lines of slime mold in the transport systems. We want to explore this difference. slime mold goes to the points but it is not go straight from one point to another point. it seems to take some detour which seems to be kind of sticky. we are more interesting if adding more points with the nodes. we can cooperate with these operation next in the project.
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Kingâ&#x20AC;&#x2122;s cross station
Victoria station
Water station
London tube station map Red dot : Tube station Blue dot : National train station Yellow dot : Overground station We selected three main train stations as start points ; Kingâ&#x20AC;&#x2122;s cross, Victoria and Waterloo station.
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Camera: Canon EOS600D with macro lens Photo took at Urban design studio in UCL. 19.Jan.2015 - 23.Jan.2015
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Analysis result of the calibration experiment
Network of Slime Mold Network of London Road
The efficiency of the path of slime mold is between that of London road and underground. However, the slime mold physa has obviously higher complexity, which is potential to be further analysed.
Network of London Underground Network of Proximity
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Time Scale: Hour
NETWORK OF SLIME MOLD Conection Amount : 96 Total Journey (m) : 100324.93 Total Length (m) : 56058.55 Average Distances (m) : 1045.05 Detour (%) : 58.67 Occupy Areas (Km²) : 16.88 Path Per Km² (m) : 3320.20
NETWORK OF LONDON ROAD Conection Amount : 96 Total Journey (m) : 102747.00 Total Length (m) : 53238.71 Average Distances (m) : 1070.28 Detour (%) : 62.50 Occupy Areas (Km²) : 16.88 Path Per Km² (m) : 3191.56
NETWORK OF LONDON UNDERGROUND Conection Amount : 96 Total Journey (m) : 109471.55 Total Length (m) : 45397.08 Average Distances (m) : 1140.33 Detour (%) : 73.14 Occupy Areas (Km²) : 16.88 Path Per Km² (m) : 2592.94
NETWORK OF PROXIMITY Conection Amount : 96 Total Journey (m) : 63228.32 Total Length (m) : 63228.32 Average Distances (m) : 658.63 Detour (%) : 0 Occupy Areas (Km²) : 17.69 Path Per Km² (m) : 3574.18
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Slime Mold Swarm Behaviour Algorithm
If the food supply is sufficient
YES
NO
Expanding Behaviour
Concentartion Behaviour
Randomly Movement in Swarm Groups
the amoebas release their intercellular reserve of cAMP in a large pulse
The cells move toward higher cAMP concentration area
The cells release cAMP at food locations
Postive Loop The receptors were desensitized, and the amoebas fall into refractory state for some time
The cells are moving from this too dense area to the triggering cells or nearby areas
We extract three typical behaviour pattern of slime mold. Randomly swarm behaviour when food is sufficient; Leave signal chemistry at food location to attract more cells; move to nearby area when one place is full of slime mold.
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Transfer Behaviour
The cells were navigated to food concentrated locations
extracellular cAMP concentration exceeds a certain threshold
Dust Area Dust Area Dust Area
Loaded Robot Loaded Robot Loaded Robot
Empty Robot Empty Robot Empty Robot
void emptyRobot(){ if (Recieve lightSignal_ Dust){ void emptyRobot(){ void emptyRobot(){ walk iftoward signal if (Recieve lightSignal_ (Recieve Dust){ lightSignal_ Dust){ emit lightSignal_Dust; source; walk toward signal walk toward signal } emit lightSignal_Dust; emitsource; lightSignal_Dust; source; if (arrive dustArea){ } } emit iflightSignal_Dust; if (arrive dustArea){ (arrive dustArea){ Pick up particles; emit lightSignal_Dust; emit lightSignal_Dust; switch to loadRobot; Pick up particles; Pick up particles; } switch to loadRobot; switch to loadRobot; if(arrive } } dumpArea){ emit lightSignal_Dump; if(arrive dumpArea){if(arrive dumpArea){ continue randomly walk; emit lightSignal_Dump; emit lightSignal_Dump; } continue randomlycontinue walk; randomly walk; else { } } walk randomly; else { else { emit lightSignal_Posit; walk randomly; walk randomly; avoid collision; emit lightSignal_Posit; emit lightSignal_Posit; } avoid collision; avoid collision; } } } } }
void loadedRobot(){ if (Recieve void lightSignal_Dump){ void loadedRobot(){ loadedRobot(){ if (Recieve lightSignal_Dump){ if (Recieve lightSignal_Dump){ walk toward signal source; Empty Robot emit lightSignal_Dump; walk toward signal walk source; toward signal source; } Empty Robot Empty Robot emit lightSignal_Dump; emit lightSignal_Dump; Loaded Robot if (arrive dumpArea){ } } Loaded Robot Loaded Robot Empty Robot Emitting Dump Signal emit lightSignal_Dump; if (arrive dumpArea){ if (arrive dumpArea){ Empty Robot Emitting Empty Dump Robot Signal Emitting Dump Signal Loaded Robot Emitting Dust Signal drop off particles; emit lightSignal_Dump; emit lightSignal_Dump; switch to emptyRobot; Loaded Robot Loaded Dust Robot Signal Emitting EmptyEmitting Robot Emitting Dust Signal Dust Signaldrop off particles; drop off particles; switch }to emptyRobot; switch to emptyRobot; Empty Robot Emitting Empty Dust Robot Signal Emitting Dust Signal Loaded Robot Emitting Dump Signal if(arrive dustArea){ } } emit lightSignal_Dust; Loaded Robot Emitting Loaded Dump Robot Signal Emitting Dump Signal if(arrive dustArea){ if(arrive dustArea){ Dust Area continue randomly walk; emit lightSignal_Dust; emit lightSignal_Dust; } Dust Area Dust Area continue randomlycontinue walk; randomly walk; else { Dump Area } } walk randomly; else { else { Dump Area Dump Area emit lightSignal_Posit; walk randomly; walk randomly; avoid collision; emit lightSignal_Posit; emit lightSignal_Posit; } avoid} collision; avoid collision; } } } }
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Converting these typical behaviours pattern of slime mold, in order to organize the autonomous swarm robot system, to collect the salt material from the sabkha to the select region.
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Swarm robot simulation From the algorithm extract from physa cells movement algorithm
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Process of swarm robot simulation
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Digital simulation of network With Physa Biological Morphology and Liwa Popultaion Information
In order to let the simulation system combining the terrain data-maps we use the data to introduc the environment information, the whole simulation system began to combine environmental elements in growing on the ground from here. Until the middle of the past century, population centres in the Emirate were restricted to seasonal settlements in and around Abu Dhabi, Dalma Island and inland at Ghayathi, Liwa and Al Ain. The population consisted of a number of different tribal groups with their strong ties of kinship and distinct traditions. The most prominent of these groups were the Bani Yas, Manasir and Baharinah. In 1904 the tribal population in the Emirate was approximately 18,000, with settled groups and nomads who would camel herd, hunt and fih. Nomadism declined, specifially among the Bedouin of the dominant Bani Yas tribe, as the economy in the region diversifid. Prior to 1970, Abu Dhabi Emirate was largely undeveloped with a population of 46,375 (1968 CensusData). The increased commercial production of oil over the past forty years spurred urban development and industrial growth, which led to a dramatic risein population. Census data from 2005 indicate the population of the Emirate was 1.4 million. By the end of 2008, Abu Dhabi had a total of 1.57 million people, and was the most heavily populated of the seven emirates that make up the UAE. Seventy-fie per cent of the population are expatriates, while the remaining twenty-fie per cent are Emirati. A majority of the population resides in the urban centres ofAbu Dhabi City and Al Ain City; other population centres are found in smaller towns and cities in the Western Region such as Madinat Zayed, Mirfa, and Ghayathi. The population is projected to more thantriple by 2030; these increases will occur mainly in Abu Dhabi City and Al Ain City, with projected growths of greater than 3 million and 1 million, respectively.
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Digital simulation of network With Physa Biological Morphology and Liwa Popultaion Information
We developed a simulation method, known as life-stage simulation analysis (LSA) to measure potential effects of uncertainty and variation in vital rates on population growth for purposes of species conservation planning. Under LSA, we specify plausible or hypothesized levels of uncertainty, variation, and covariation in vital rates for a given population. We use these data under resampling simulations to establish random combinations of vital rates for a large number of matrix replicates and finally summarize results from the matrix replicates to estimate potential effects of each vital rate on in a probabilitybased context. Estimates of potential effects are based on a variety of summary statistics, such as frequency of replicates having the same vital rate of highest elasticity, difference in elasticity values calculated under simulated conditions vs. elasticities calculated using mean invariant vital rates, percentage of replicates having positive population growth, and variation in explained by variation in each vital rate. To illustrate, we applied LSA to vital rates for two vertebrates: desert tortoise (Gopherus agassizii) and Greater Prairie Chicken (Tympanuchus cupido). Results for the prairie chicken indicated that a single vital rate consistently had greatest effect on population growth. Results for desert tortoise, however, suggested that a variety of life stages could have strong effects on population growth. Additional simulations for the Greater Prairie Chicken under a hypothetical conservation plan also demonstrated that a variety of vital rates could be manipulated to achieve desired population growth. To improve the reliability of inference, we recommend that potential effects of vital rates onbe evaluated using a probability-based approach like LSA. LSA is an important complement to other methods that evaluate vital-rate effects on , including classical elasticity analysis, retrospective methods of variance decomposition, and simulation of the effects of environmental stochasticity.
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Digital simulation of network With Physa Biological Morphology and Liwa Soil Information
The change of the composition of land plays a big role for the growth of simulation, different land composition and distribution will guide the simulation, and according to the different composition of different plots and position set difference algorithm to simulate system. Soil is the unconsolidated mineral or organic material on the immediate surface of the earth that serves as a natural medium for the growth of land plants. Soil formation is a result of the complex interaction of climate, parent material, biological activities and topography; biological processes play a minor part in the Emirate’s soil development due to low organic matter and scarce vegetation. The soils of the Emirate vary according to chemistry, mineralogy, physics, uses and other factors; generally, the Emirate’s soils are sandy and dominated by minerals such as quartz and carbonates. Along the coast, however, soils contain high sodium chloride salt concentrations, and tend to be poorly drained. The Environment Agency – Abu Dhabi implemented the Soil Survey of Abu Dhabi Emirate to identify and map the soils of the Emirate and determine their suitability for different uses to assist with land management planning. Nine soil great groups were recorded and are displayed on this map; other map units where the dominant component is not a natural soil (rock outcrops, miscellaneous units, etc.) are also displayed. Torripsamments are the most dominant and widespread soil great group, comprising eighty-one per cent of the Emirate. These soils occur as extensive dune systems and sand sheets and tend to have low water holding and run-off capacity, high infitration, and low fertility. The Emirate’s desert soils are highly prone to wind erosion and degradation due to the region’s climate, increased salinisation, waterlogging, human land use practices, and overgrazing. Soil degradation reduces the capacity of the soil to produce goods or services, impacting food security and the quality of the environment.
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Digital simulation of network With Physa Biological Morphology and Liwa Soil Information
The high variability of construction and agricultural machinery leads to high demands on the product development. In particular, the interaction of the tool with the ground during excavation or plowing but also the interaction of a harvesting machine with the crop, etc. are hardly predictable. Until now, such complex interactions are usually determined by measurement and are therefore difficult to integrate into the virtual product development.. The activities in this area are, therefore, focused on the integration of the complex behavior of such materials. This makes it possible to compare variants of a machine under the same conditions. Design changes may be analyzed for their impacts on the load on the machine or the flow of crop material through the machine. Methods of classical soil mechanics, Finite Element Method (FEM) with special material models for the nonlinear material behavior of the soil, fail in this application. A particular challenge is the separation of the material during excavation. New simulation approaches, such as particle simulation can better deal with these conditions. In particular, the Discrete Element Method (DEM) has been proven for simulations of this kind. Another important aspect of the interaction between a vehicle and the ground is the tire-soil interaction. The existing tire models developed at the ITWM have been updated to include the behavior of deformable soil. This allows to assess the vehicle dynamics, sinkage and loading in off-road applications.
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Digital simulation of network With Physa Biological Morphology and Liwa Vegetation Information System of the vegetation is very precious in the desert region, this data contains a lot of vegetation map locationâ&#x20AC;&#x2DC;s information. In simulation the data map simulates the growth of the system affected by the location of vegetation, the system not only affected by the growth algorithm, but also by the control of the data map at the same time. The vegetation cover in the Abu Dhabi Emirate is sparse due to its harsh climate and limiting soils. Subtle differences in climatic or soil conditions across the Emirate have a marked distribution on plant species; generally, the western part of the Emirate and the coastal and sabkha areas support little vegetation cover, while the north-east has greater foliage cover. The soil salinity of coastal and inland interdunal areas of the Emirate exerts a highly selective effect on plant growth. Though additional research is needed on the flra of the Emirate, it is estimated that the Abu Dhabi Emirate is home to 400 plant species. During the soil survey conducted by the Environment Agency â&#x20AC;&#x201C; Abu Dhabi, data was collected on vegetation communities. This map shows the distribution of eleven different vegetation communities in the Emirate. Vegetation communities are determined by the dominant perennial species observed during surveys. The Cyperetum-Zygophylletum community (dominated by Cyperus conglomeratus and Zygophyllum spp.) makes up the largest part of the mainland Emirate (31.3%) and is widespread on sand dunes and sand sheets throughout southern and eastern Abu Dhabi. CyperetumHaloxyletum-Zygophylletum is also widespread in the Emirate (23%) and found on sand sheets and sand dunes throughout central and northeastern Abu Dhabi. The areas of no vegetation are extensive on the map and indicate hypersaline and hypergypsic conditions that generally occur along the coast and in inland sabkha areas.
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Digital simulation of network With Physa Biological Morphology and Liwa Vegetation Information Floodplain vegetation and physical habitat are mutually interdependent; alterations to the Natural flow regime yield effects on the vegetation spatial, quantitative and qualitative distribution. Upon a certain extent, riparian vegetation is therefore a mirror of the ecosystem processes which have undergone in the floodplain. Such processes effects are often measurable over a long time scale which goes beyond the regular human observation possibilities. Nevertheless, it is often required, either for management or research purposes, to know what will be the consequences of habitat alterations. Liwa Vegetation is designed to meet this need since it incorporates the main key factors and behavior of riparian ecosystems. Key factors include the essential riparian ecosystem driving forces while behavior includes vegetation responses to such factors, which are generally found in temperate and Alpine climates. The model concept assumes that vegetation development depends by the functional relationship between hydrology, physical processes and vegetation communities. In the model conceptualization, physical processes are represented by height over mean water, shear stress (as indicator of morphodynamic disturbance) and flood duration. These factors allow the successful establishment and development of the vegetation or its retrogression to the initial stage. Liwa Vegetation main feature is the vegetation succession or retrogression in space and time and it is further divided in modules, namely: recruitment, morphodynamic disturbance and flood duration. The model is addressed as dynamic because it takes different inputs for each simulated year and because the outputs of each model run is fed again to the model as input for the next iteration. The simulations results are in form of maps, and tables to ease the visualization and processing of the simulated scenarios as well as the dissemination and reporting of the findings.
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Physa Digital Network Model of Liwa Oasis Digital simulation of network as a symbiotic and interactive system
Preliminary simulation system is based on the observation of the way of slime moldâ&#x20AC;&#x2122;s growth, the whole system is in accordance with the basic theory of biological algorithm by the evolution of the step by step. In the simulation, using the particle system to simulate the performance of multiple nuclei in slime molds. Using the dual system interactions to complicate structure, and produce more hierarchical structure system. Here we put four single units of the growth, each of different units are generated by different structure simulation, and then under the control of the different speed and force, we put in the algorithm based on the biological control. From the scale of the micro analysis, actually slime molds and mycelial system structure has many place worthy of learning. In four different units, we outspread from the angle of the micro to macro direction, by reference to the growth of the slime molds, the algorithm is extended to the overall structure of material system. Because of the different infrastructure, so the result of the growth also has the difference.
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Atlas of simulation
Divergent Growth of Monomer
type A
The simulation using the algorithm for the structure of deconstruction, and create a more direct ways with hierarchical structure.
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details from inner structure
details from inner structure Divergent Growth of Monomer Mycelium Scale: 10m Unit A Unit B Unit C Unit D
material simulation material simulation friction of material structure
details from inner structure
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changeable structure 1
changeable structure 2
changeable structure 3
changeable structure 4
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The whole system is divided into two different systems, the two system also follow different time, the rules and the development process.
Divergent Growth of Monomer Mycelium/Pysarum Scale: 10m Unit A Unit B Unit C Unit D
material simulation material simulation friction of material structure
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In different periods, the structure of the red to generate and guide the yellow derivative organ system and by the trigger new changes through time.
Atlas of Simulations Type C Physarum
Divergent Growth of Monomer Mycelium/Pysarum The growth of the system by two different system interaction, generate new structure, to imitate the formation of slime molds.
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Divergent Growth of Monomer Mycelium/Pysarum
In this system, the system of three different with each other, by time and the particleâ&#x20AC;&#x2122;s position control curve happened.
Scale: 10m Unit A Unit B Unit C Unit D
material simulation material simulation friction of material structure
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As a result, the whole system is full of change, on the space have administrative levels feeling. Due to the nature of the growth, leading to a vector of the system to have an upward.
Atlas of Simulations Type D Physarum
Divergent Growth of Monomer Mycelium/Pysarum The system use the particle system movement to generate a complete organism, through to the different speed control, to achieve a relatively complete system
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details from inner structure
details from inner structure
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details from inner structure
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Divergent Growth of Monomer Mycelium/Pysarum Scale: 10m Unit A simulation Unit B simulation Unit C Unit D
material material friction of material structure
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details from inner structure
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details from inner structure
details from inner structure
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