The Forge (Architecture Masters Thesis) by ARCHIFUNCT

THE FORGE MCMY_THESIS

THESIS STATEMENT Located on the asteroid belt, an asteroid mining station set in the near future is home to 2000 asteroid miners. Our design a building stations, solution to

takes a systematic approach to settlement design at and urban scale. We critique existing modular space as well as futuristic designs to develop a design what is typically tackled as an engineering problem.

We propose an adaptive aggregated system based on complex rules to determine the arrangement of typologies in four dimensional space. This challenges the notion of the planar city configuration, and leverages the weightlessness of our alternative context to demonstrate applications of complex non-planar systems.

Architectural Team Michael Oâ&#x20AC;&#x2122;Reilly Carol Sun Maxine Zhou Yaseen Bhatti

Advisors Solon Solomou Filippos Filippidis Ulysses Sengupta Robert Hyde Samuel Bland

Critics David Connor Kathryn Timmins

Document Info File Name: Creation Date: Modification Date: Publication Date:

MCMY_THESIS 05/02/20 1 June 2020 6:49 pm 01/06/20

MCMY PRODUCTION

MCMY is the architecture supergroup formed by Michael Oâ&#x20AC;&#x2122;Reilly, Carol Sun, Maxine Zhou, and Yaseen Bhatti during their time at the Manchester School of Architecture.

IN ASSOCIATION WITH:

STAGE 01 SPACE SYNTAX & GENERATIVE METHOD

INTRODUCTION Located on the asteroid belt, an asteroid mining station set in the near future is home to 2000 asteroid miners. Asteroid mining has been conjectured as being an enabler of transferring industrial activities into space, bringing vast environmental benefits to Earth. The space industry is rapidly growing and as it transitions from the exploration phase of the 20th century to the settling phase of the near future, it is more important than ever that architects be involved in space. That is why we have engaged with an architectural competition brief to design an asteroid mining station, set in the near future using current technology. Our design uses an aggregated modular system that enables flexible growth and adaptation in 4 dimensions to create more connected spaces by leveraging the absence of gravity. The configuration for this aggregation has been computationally designed using an algorithm we created for this specific purpose. Our project is different to engineering solutions that tackle the same problem because we consider the working and living environments, and the connectivity between them.

STAGE 02 AGGREGATION SYSTEM & RESEARCH

STAGE 03 COMPONENT DETAIL DESIGN & AGGREGATION DEVELOPMENT

FINAL AGGREGATION GEOMETRY

GENERATIVE DESIGN

ATELIER CPU BRIEF The atelier CPU brief centres around Generative Design, a framework for combining digital computation with human creativity to achieve results that would not otherwise be possible. Generative design is a flexible and scalable framework; it can be applied to a wide range of design problems and scales, from industrial components, to a pavilion, to a city. Therefore, the brief is flexible in that it allows us to choose between occupancy, building and urban scale. The particularity of the asteroid mining station is that it sits somewhere between a small mining town and a space station, giving it both urban and building scale characteristics, so both approaches need to be considered. In Stage 3 will design at the occupancy scale too.

OCCUPANCY SCALE

BUILDING SCALE

URBAN SCALE

ARRANGEMENT AND PERFORMANCE

MINING TOWN

DESIGNER SETS AND CAN MODIFY PARAMETERS

START THE PRINCIPLE OF LEARNING IS KEY TO THE GENERATIVE DESIGN PROCESS. THIS INVOLVES FEEDBACK AND EVOLUTION THROUGH ITERATIVE AND EVOLUTIONARY OPTIMISATION.

DEFINE PARAMETERS

GENERATE GEOMETRY

DESIGNER

EVOLVE

EVALUATION

DESIGNER SETS AND CAN MODIFY EVALUATION CRITERIA

DESIGNER CAN MANUALLY SELECT AND REFINE OUTPUTS FROM A SET OF ITERATIONS

OUTPUT

THE OUTPUT CAN BE CONTINUOUSLY OPTIMISED AGAINST THE SET CRITERIA. THE GENERATIVE DEIGN PROCESS CAN PRODUCE MANY ITERATIVE SOLUTIONS, SOME MORE SUCCESSFUL THAN OTHERS. THE FITTEST OF THESE CAN THEN BE SELECTED BY THE DESIGNER AS A FINAL OUTPUT, OR AS PARENTS FOR A FURTHER ITERATIVE STUDY.

01. Instead of designing objects, we will learn to design systems which encode the full range of possibilities of a particular design concept 02. We will then learn methods for measuring and quantifying the performance of these systems so that each design can be evaluated automatically by the computer 03. Finally, given our designers intuition and measurable outputs we will be selecting the most appropriate design for our design problem

DESIGN OUTLINE

MISSION DESIGN The how The the

brief discusses some of the possible approaches for the mission can be carried out based on factual data. participants must take cues from the data and curate mission story for their respective projects.

CREW COMPOSITION The users for this space station would constitute of a group of space scientists, Medical specialists, astrophysicists, space engineers, maintenance engineers and workers in the asteroid mining factory, space station maintenance staff, etc. A part of the space station can be dedicated for research and space exploration comprised of scientists and space explorers. The participants can modify the ratio and add to the occupation/roles of the user already mentioned group depending on their design story.

FUNCTION The primary functions of this space station will be:

COMPETITION BRIEF Combined with the CPU brief, we have entered a space architecture competition which provided us with additional requirements. The challenge is to design a space habitat for 2000 “Astro Miners”. The ﬁrst self sustaining industrial space settlement will be commissioned by planet earth collectively in the year 2032. The habitat module would be replicable in nature that can be expanded based on the requirement. This space station would be a base for material extraction and processing, and also the home to a new off world settlement of miners and their supporting community. Competition info: https://uni.xyz/competitions/ leap/info/about?utm_ medium=website&utm_ source=archdaily.com

01. Metal/Mineral Extraction 02. Research - Training 03. Processing 04. Service / Maintenance 05. Accommodation 06. Production Unit 07. Recreation 08. Powering unit 09. Physical Exercise 10. Food production 11. Communication Satellite

HUMAN FACTORS AND ERGONOMICS The design teams must consider and accommodate human factors such as circadian rhythm, medical support facility, emergency unit etc. in their principles in design. Both the medical support facility and the recreation facility should be approached comprehensively to enable physical recreation and monitored exercise for the crew.

BASE DESIGN The base provides the living and working spaces and all other accommodations to sustain the crew and effectively support them in accomplishing mission objectives. The basic functioning strategy of the living and working environment of the station must be designed using plausible hypothesis and using the given facts.

SAFETY STRATEGY In case of an emergency an immediate evacuation must be strategized for the people on board. It does not require technical facts and accuracy.

DELIVERABLES Recommended number of boards/sheets - 6 boards/ sheets in portrait digital no limit. Site plan (Compulsory) Key conceptual sections x 1 (Minimum) 3D views x 4 Cover image/Thumbnail of size 2000 x 1000 px or larger in aspect ratio 2:1. Floor plans, images, sketches can be added to support the entry

JUDGING CRITERIA Presentation: The fundamental to a good entry is a good presentation. Concept/idea: Quality of thought and intent in pre-design phase. Spaces/program:How the spaces are calculated and ordered. Design Output: The ﬁnal architectural outcome of the solution.

CERES IN 2032 CERES TRAJECTORY ORBITAL LOCATION IN 2032: CLOSEST PATH TO EARTH

ASTE

ROID

BELT

CERES IN 2020

CERES

THE STATION WILL BE LAUNCHED A FEW WEEKS PRIOR TO CERES PASSING BY THE EARTH. IT WILL SYNCHRONISE WITH THE ORBIT OF THE DWARF PLANET AND USE THAT AS AN ANCHOR FOR MINING SURROUNDING ASTEROIDS.

1.5 AU

SITE LOCATION

MAR

LOCATION For this challenge, the region near asteroid (dwarf planet) Ceres located in the asteroid belt between Mars and Jupiter is chosen. It is also one of the ďŹ rst and largest asteroids known to mankind. Ceres itself is a dwarf planet and its mass is considered to be one-third of the total mass of the belt. It would be closest to the earth on Feb 2032 according to the predictions based on its trajectory data. A few weeks prior to this, the station would be launched for the deep space mission.

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CONTENTS TY

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click on interactive contents tab to switch between chapters

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Context

Chapter 01

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Why Space? CONTEXT AND NARRATIVE

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1973 NASA’s space station Skylab

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1998 International Space Station (ISS) launched

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CONTEXT & SCENARIO TRAJECTORY

50 Years ago, when Neil Armstrong set his foot on the surface of the Moon, it inspired a moment of awe for humanity. The New York Times newspaper argued that:

2012 First cargo sent by private company (SpaceX) to ISS

“man, from this day on, can go where so ever in the universe, his mind wills and his ingenuity contrives…to the planets, sooner rather than later, man is now certain to go.” We are living at pivotal moment in human history; our breakout into space is underway. Space Exploration, travel, tourism and asteroid mining are ongoing ventures of private as well as government aided space ﬁrms. This inevitable future will challenge our entire notion of humanity as an earth-bound planetary species.

2023 First asteroid mining operation

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PROGRESS

PRESENT DAY

TIME BRACKET FOR OUR PROJECT 2250 Population in space equals earth population

2200 First interstellar mission

2150 Multiple cities in space

2100 First University in space

1750 The notion of ‘planet’

2055 Well established industries and supply chain supplying industries in space

2060 Regular asteroid mining

2050 SpaceX plans to develop city on Mars 2023 First Asteroid mining colony First asteroid 2012 mining operation First cargo sent by private company (SpaceX) to ISS 2020 (Now) 3 people long term in space, 20,000 objects in Earth orbit

TIME

01 9

1973 NASA’s space station Skylab

1998 International Space Station (ISS) launched

2075 First base on Europa

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Earth is predicted to reach maximum capacity in 2050

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THE ARCHITECTS ROLE

The space industry is rapidly growing and as it transitions from the exploration phase of the 20th century to the settling phase of the near future, it is more important than ever that architects be involved in space. This is evidenced by the recent collaboration between space agencies like NASA and leading architecture firms such as SOM, BIG, Foster + Partners, and ourselves of course. Our project references the idea of a universal architecture. That is, terrestrial architecture comprises only a part of design criteria in possible futures. In this sense we are expanding our curriculum and strengthening our already multidisciplinary education.

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These disciplines are already well integrated into the space industry. If space architecture is to serve as a holistic coordination medium for these fields, it must assume basic knowledge of them into itâ&#x20AC;&#x2122;s process.

HEALTH & MEDICINE TRANSPORTATION DESIGN AEROSPACE ENGINEERING Space architecture serves as the vessel for human centered design to be a priority of the space industry.

BUSINESS & MANAGEMENT INDUSTRIAL DESIGN

LIFE SCIENCE [HUMAN FACTORS]

SPACE LAW & POLICY

ARCHITECTURE

SPACE ARCHITECTURE

SPACE INDUSTRY

SCIENCE FICTION

Space architecture can translate the creative ambition and vision of science fiction into the space industry.

Relationship diagram between Space Architecture and other disciplines, Adapted from figure 1, page 2 (wong, 2003)

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SPACE ARCHITECTURE RELATIONSHIP DIAGRAM

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ART

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HIGH VALUE ASTEROIDS

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AS OF SEPTEMBER 2016, THERE ARE 711 KNOWN ASTEROIDS WITH A VALUE EXCEEDING

US$100 TRILLION

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THE FORGE

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ASTEROID MINING INDUSTRY

Space Economy is the commercial utilization of technology in space, and its potential is currently limited to satellite navigation, satellite television and commercial satellite imagery. Private companies like Blue Origin, SpaceX, Rocket Lab, Virgin Galactic - Virgin, Bigelow Aerospace, Planetary Resource are some of the few that are already preparing to make the space business thrive by exploring options. Regularizing space travel for all, for better communication, mass tourism, and space exploration for minerals - asteroid mining etc are some of the proposals in pipeline. Since the potential value of minerals in these asteroids is staggeringly large, space organizations have already begun their quest in developing the technology required to make it happen. Our solar system is ﬁlled with millions of asteroids, ranging from the size of a feet to 975 kms. The amount of iron found in asteroids 16 Psyche alone is worth an estimated $10 quintillion. Such possibilities could see the annual revenues of the space industry go from $340 billion to $926 billion by the year 2040—creating a potential opportunity for venturing into creating space habitats for speciﬁc needs as well as research.

ASTEROID MINERS TRAVEL TO THE FORGE TO BEGIN THEIR TOUR

RE-SUPPLY TECHNICAL COMPONENTS

TRADE WINDOW ONCE EVERY

EXPORT MATERIALS TO EARTH

ASTEROID MINERS RETURN TO EARTH UPON TOUR COMPLETION

MONTHS

THE EARTH

ASTEROID MINING PROVIDES A SUSTAINABLE SUPPLY OF NATURAL RESOURCES

DEMOCRATISE TECHNOLOGY BY REDUCING COST OF ELECTRONICS

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CREATE A GREENER EARTH BY TRANSFERRING INDUSTRIAL ACTIVITIES INTO SPACE

SUSTAINABLY ELECTRIFY TRANSPORTATION

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75X

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OCCURRENCE RATE

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ELEMENT SYMBOL ELEMENT NAME

Element

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ASTEROID MINING PROCESS

The asteroid belt is composed of high value materials. Volatiles and water are considered vital to sustaining the space station. Not only is water alone vital, but it can also be broken down into oxygen and hydrogen for life support and fuel. Furthermore, metallic asteroids need to be mined for in situ materials to construct and expand the station. Some are highly valuable due to being considered rare on Earth, like gold and platinum, yet are abundant in the belt.

17X

Hydrogen

75X

74X

Carbon

Nitrogen

27X

1X VOLATILES & H2O

TO SUSTAIN THE STATION WITH WATER, FUEL, REFRIGERANT, AGRICULTURE, AND METALLURGY.

Oxygen

155X INDUSTRIAL METALS

Fe Iron

810X

Ru Ruthenium

440X

Os Osmium

Co Cobalt

180X

Rh Rhodium

540X

Ir Indium

Ni Nickel

TO CONSTRUCT NEW MODULES AND SUSTAINABLY GROW THE STATION WITH IN SITU RESOURCES.

44X

Pd Palladium

196X

Pt Platinum

PLATINUM GROUP METALS

TO SUPPORT DEMAND GROWTH ON EARTH, THESE METALS ARE HEAVILY USED IN AUTOMOBILES, ELECTRONIC DEVICES, ADVANCED MATERIALS, AND CANCER TREATMENTS. DESPITE DESIRE TO REDUCE DEPENDENCY, ONE-INFOUR MANUFACTURED GOODS REQUIRE THESE METALS.

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Hydrogen

Oxygen

WATER-RICH ASTEROID

$5 TRILLION VALUE IT CURRENTLY COSTS $20,000 TO SEND A LITER OF WATER FROM EARTH TO SPACE. WATER MINED IN SPACE CAN SUSTAIN HUMANS, BUT CAN ALSO BE CONVERTED INTO ROCKET FUEL!

Fe Iron

IRON-RICH ASTEROID

$1 BILLION VALUE THIS SMALL 10M S-TYPE ASTEROID CONTAINS 650,000KG OF IRON, AT $0.09027 PER KG ON EARTH ITâ&#x20AC;&#x2122;S NOT WORTH BRINGING BACK, BUT SINCE IT COSTS $20,000 TO SEND 1 KG UP TO SPACE, IT IS WORTHWHILE TO MINE IRON IN SITU FOR SUSTAINABLE LOCAL CONSTRUCTION OF STRUCTURES IN SPACE.

Pt Platinum

PLATINUM-RICH ASTEROID

$2.9 TRILLION VALUE THIS 500M ASTEROID COULD FILL THE DEMAND FOR PLATINUM ON EARTH, REDUCING OPERATIONS THAT DAMAGE OUR PLANET. ONE OUNCE OF PLATINUM IS VALUED OVER $1,500 ON EARTH.

MATERIAL RETRIEVAL

THE ASTEROID BELT

1 2

Carbon

Nitrogen

Oxygen

Fe Iron

Co Cobalt

Ni Nickel

THE FORGE

3 RE-S

Nitrogen

Oxygen

Fe Iron

Co Cobalt

Ni Nickel

THE WIDER SPACE INDUSTRY

WIDER SPACE INDUSTRY RESUPPLIED AND ENABLED BY ASTEROID MINING DUE TO REFUELLING AND CONSTRUCTION CAPABILITIES

Os Osmium

Ir Indium

Pt Platinum

Ruthenium

Rh Rhodium

MARS

Carbon

THE EARTH

DIMINISHING RESOURCES AND IMPOSSIBLE LONG TERM SUSTAINABILITY COULD BE RESCUED BY ASTEROID MINING INDUSTRY

TRADE NETWORK WITH NEAR-FUTURE SPACE INDUSTRY

Palladium

EARTH

SPACE COLONIES

Hydrogen

VOLATILES, WATER, AND INDUSTRIAL METALS ARE RE-USED DIRECTLY AT THE SPACE STATION AND OTHER NEARBY COLONIES

ORT

EXP

UPPL

ASTEROID MINING STATION LOCATED ON THE ASTEROID BELT, HOME TO 2000 ASTEROID MINERS

PLATINUM GROUP METALS ARE EXPORTED BACK TO EARTH EVERY 15 MONTHS DURING THE RESUPPLY WINDOW

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Hydrogen

THE AUTONOMOUS SHIPS DOCK AT THE FORGE, WHICH PROCESSES AND REFINES THE ASTEROID MATERIAL INTO HIGH VALUE ELEMENTS

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SMALL, AUTONOMOUS SHIPS RETRIEVE ASTEROID MATERIAL FROM KEY LOCATIONS IN THE BELT

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Population 136

Popul

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Humans are shuttled to the station from earth to fulfil the ever-increasiblng job demand of the growing station. A self sustaining, liveable community that is comfortable to live in long term are created. This system provide material for Mars and as a hub to the outer reaches of our solar system In the long term this new space fairing race builds more mining facilities from the same template as the original.

The first trillion dollar company is looking to generate money from mining materials from space,sending precious metals back to earth for retail, and using them to start the foundation of space infrastructure.

STAGE 01 MODULES ASSEMBLY

Modules Assembly

EXTRACTION

PROJECT TRAJECTORY

Pre M

Extraction

Living Essen

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Population 811

lation 402

PROPELL TO ASTEROID BELT

ASTEROID MINING

SUPPLY LIVING ESSENTIALS

ssentials g entials sentials Living Essentials Essentials

Mining Mining Mining Mining Mining

Expansion Growing Expansion Expansion Expansion Expansion Growing Growing Growing Growing

Livability Livable Livability Livability Livability Livability Livable Livable Livable Livable

Supply Supply Supply Supply Supply

STAGE 04

Asteroid Belt Asteroid Asteroid Asteroid Belt Belt Asteroid Belt Belt

Population 2722

Precious recious Precious Precious Precious Metals Metals Metals Metals Metals

Propell Propell Propell Propell Propell

Pt Fe

Population 1351

Export Export Export Export Export

STAGE 03

STAGE 02

Pt Pt FePt FePt FeFe

Population 1081

EXPORT PRECIOUS METALS

STAGE 05 Population Population Population Population Population Growth Growth Growth Growth Growth

STAGE 06 Hub Hub Hub Hub

Hub

Prototype Prototype Prototype Prototype Prototype

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HUB

ni Me ng ch 8 He an 00 al ic Sc th s 1 ie ca 28 Fa nt re rm is 48 Go in ts ve g& 40 Ot rm Foo he en d 4 r t Su 1 8 pp or t Se

EXPANSION

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LIVABILITY

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PROTOTYPE

POPULATION GROWTH

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Precedents OUR CRITIQUE OF EXISTING AND FUTURIST DESIGNS

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SPACE HABITAT TYPOLOGIES

Space habitats can be broken down into 2 categories: zero gravity space stations (like the international space station) and artificial gravity stations that use the centrifugal force of a spinning environment to simulate the effects of gravity.

Modular

These categories can be broken down into individual strategies that solve the same problem in various ways.

ZERO - G

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dumbell

torus

multiple dumbell

sphere

multiple beaded torus

banded torus

beaded torus

cylinder

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03 1

ARTIFICIAL GRAVITY

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MODULAR SPACE STATION (ISS & MIR)

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SPACE HABITAT PRECEDENTS

Most of the proposals for space settlements in micro-G tries to tackle the design issue by creating an earth-like environment under gravity, and under that environment create a city. This is the most obvious engineering solution but causes a lot of problems in both an urban scale as well as the actual feasibility. PROJECT:

INTERNATIONAL SPACE STATION

TYPOLOGY: STATUS: DESIGNER: DATE: DESCRIPTION: TARGET POPULATION:

MODULAR IN OPERATION 2000 MICRO-G STATION 1-15

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BERNAL SPHERE

TYPOLOGY: SPHERE STATUS: CONCEPT DESIGNER: JOHN D. BERNAL DATE: 1929 DESCRIPTION: ARTIFICIAL GRAVITY SETTLEMENT TARGET POPULATION: 20’000 SPHERE DIAMETER: 1.6 KM

PROJECT:

ISLAND ONE

O’NEILL CYLINDER

PROJECT:

TYPOLOGY: TORUS STATUS: CONCEPT DESIGNER: STANFORD UNIVERSITY, NASA-AMES RESEARCH CENTRE DATE: 1975 DESCRIPTION: ARTIFICIAL GRAVITY SETTLEMENT TARGET POPULATION: 10’000 RING DIAMETER: 1.6 KM, SECTION DIAMETER: 490M

ISLAND THREE

TYPOLOGY: CYLINDER STATUS: CONCEPT DESIGNER: AMERICAN PHYSICIST GERARD K. O’NEILL DATE: 1976 DESCRIPTION: ARTIFICIAL GRAVITY SETTLEMENT TARGET POPULATION: RING DIAMETER: 8 KM

ROTATIONAL EXPLORER (NAUTILUS-X & HERMES)

PROJECT:

NAUTILUS X

TYPOLOGY: dumbell & Modular STATUS: CONCEPT DESIGNER: NASAAMES RESEARCH CENTRE DATE: 2011 DESCRIPTION: ARTIFICIAL GRAVITY SETTLEMENT TARGET POPULATION: 6

ARTIFICIAL GRAVITY

03 3

PROJECT:

STANFORD TORUS

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BERNAL SPHERE

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A linear form of human settlement, on Earth, is a spontaneous response to local environment context which is a road or a river, along which habitation, manufacture, storage and trade are located. Ideally, it would expand along this main axis.

ARTURO SORIA Y MATA CIUDAD LINEAL, MADRID, SPAIN, 1894

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STANFORD TORUS & LINEAR CITY

ARTURO SORIA Y MATA CIUDAD LINEAL, MADRID, SPAIN, 1894

NASA & STANFORD UNIVERSITY SUMMER STUDY, 1975

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LINEAR CITY PROS:

CONS:

/efficient transportation as formed along a road or river

/minimise the possibility of shortcuts and urban complex. /Travel distance from extremities to central points are long /A city expansion is along a line without taking up more space

PROS:

CONS:

/ideal geometry for Artificial gravity generation

/Stanford Torus applied the form of linear city in its way of being closed, which further minimise the urban complexity and stop urban expansion in another dimension.

STANFORD TORUS

LIMIT BOUNDARY

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URBAN EXPANSION

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WEIGHT LIMIT

SPATIAL LIMIT

CONS:

/The Forge cannot be configured like the ISS, as the scale is far more complex

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/Cargo with too many

constraints regarding weight and space, sacrificing human comfort and health /Modules are shipped from earth as a complete package, rather than being constructed in space could have saved 50.4 Billion on launch costs.

PROS: /Modularity

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/3 Dimentional Connectivity

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Mined resources

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Resources

Recycle

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CLOSED SYSTEM ISOLATED TO EARTH //

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Eco-system

RETAIN CONNECTIVITY AND OPTIMISE ARRANGEMENT

SPATIALLY LEVERAGING ZERO GRAVITY

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NOn-linear, highly connected, but also liveable system Based on our precedent critiques, we concluded that the stanford torus proposes an engineering solution that compromises connectivity as well as flexibility. The ISS offers a flexible modular system, but it does not consider human centered design to a high degree, and the arrangement of the modules is generally a simple, linear configuration that does not make full use of the hierarchical possibilities to create more connected spaces in an aggregaed form such as this.

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We wish to address this gap by proposing a complex aggregated system that doesnâ&#x20AC;&#x2122;t compromise mobility or connectivity.

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Strategy

Chapter 02

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Urban Morphology FOUR DIMENSIONAL SPACE SYNTAX & COMPLEX ADAPTIVE SYSTEMS

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2D/3D WALKABILITY

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URBAN MORPHOLOGY - PROPOSED

2D URBAN MOBILITY RANGE

The biggest difference between an urban fabric on earth and in space is that in space, circulation could be arranged such that we are unbound from the notion of the planar surface, and connectivity could be achieved 3 -dimensionally because of zero gravity. Thus, it has a different way of arrangement, growth and adaptability, forming a completely different urban pattern.

3D SPATIAL MOBILITY RANGE

0 C:

PATTERN OF EXPANSION

2-D EXPANSION ON PLANE

CONNECTIVITY FOLLOWING URBAN BLOCK

EXPANSION ALONG INFRASTRUCTURE (ROAD/ RAILWAY)

EXPANDABLE IN ALL DIRECTIONS

LESS LIMITED 3-D CONNECTIVITY

CLUSTER AROUND INFRASTRUCTURE (GRAVITY RING/ENTERTAINMENT HUB ETC.)

04 5

CONNECTIVITY

// C:

URBAN GROWTH

G C

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The ability to learn and grow more resilient to its environment according to feedback.

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ADAPTABILITY

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COMPLEX ADAPTIVE SYSTEM

Cities and the ecosystem are both complex adaptive systems. Similarities in operating theories are as well shared on exhibiting both emergence and self organisation. Our habitat is a self-sustained complex adaptive system during mission, during which it will need to sustain the crews and adapt to certain scenarios and the change of demands.

COLLECTIVE BEHAVIOUR Behaviour emerged from the repetition and interaction of simple rules from single local responses.

SELF-ORGANISATION Able to organize and grow without external direction, manipulation, or control. Displays the ability to adapt to changes and respond to the surrounding environment.

EMERGENCE

0 C:

The generation of higher level sophistication is created through complex aggregate interactions between lower level agents obeying local rules.

ENERGY RESOURCE PEOPLE

DETECTORS

EFFECTORS

Attention Filtering Identification Compression

Locomotion Communication Manipulation Expulsion

CAS: AGGREGATION RULE SYSTEM

ADAPTIVE LEARNING LOOP FEEDBACK EXTERNAL ENVIRONMENT

EXTERNAL ENVIRONMENT

// C:

04 7

The settlement as a complex adaptive system adapts more towards human need

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System description

CO NT

STAGE 2: SYSTEM SETTING AND AGGREG

EX T

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000

001

TE n ba Ur

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De compo of pop and r addi st

Responding to brief: 2000 asteroid minersâ&#x20AC;&#x2122; mining town

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006

005

Defin nu of typo ne for aggre

Define logic of arranging typologies by direct adjacency

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ME // //

SYSTEM BUILD-UP

STAGE 3: DETAILED DESIGN AND REFIN

000

001

Define nu proje be u each mo

Design of individual functional modules

006

005

Evaluation through fitness criteria

After comp module be re adde redu accor de

0 D:

GATION EXPERIMENTS 002

003

Define functions needed within the settlement to achieve an isolated mining society

efine osition pulation required itional taff

004

Define spatial requirement of different functions

Create massing typologies based on requirements

007

ne the umber each ologies eeded r the egation

Define evaluation criteria: evaluate successful aggregation

NED AGGREGATION RULES

e exact umber ected to used of type of odule

002

003

Facing different scenarios, demand might change

004

Flexibility in algorithm allowing system to adapt to change

Refine aggregation

// D:

05 1

r system pleted, es could eplaced, ed, or undant rding to emand

CO NT EX T

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2722

TOTAL POPULATION:

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The toal population is comprised of all of the support industries required for a thriving asteroid mining town. Starting from the number of asteroid miners, formulas were cdonstructed using global open-source data to find ratios required for the support industries. The sum of these ratios outputs the total of 2722, this is the population we will initially consider the spatial requirements for.

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POPULATION FORMULA: MATERIAL PROCESSORS

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mp = Material Processors

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m = Total Miners

mp = [ 0.218*m ]

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ME // //

In order to formulate rules that dictate the configuration of our urban grain, we must first understand who we are designing and providing for. As the project is based around commercial asteroid mining, we can extrapolate the demographics necessary to support such a society. Fundamental support industries include Healthcare, Mechanics, Food, Water, etc.

0 D:

POPULATION COMPOSITION 1: MATERIAL PROCESSORS

POPULATION

Total Pop

mp = 436

436

Material Processors are a Sub-Group within the asteroid Miner demographic. Their role is to deconstruct the mined material inbound on the station in order to harvest the desired material for trade or construction.

DEMOGRAPHIC SPATIAL REQUIREMENTS • • • • •

Residential 3D Printers / deconstruction Exercise Training Processing Machines

REFERENCES https://datausa.io/profile/naics/iron-steel-mills-steel-product-manufacturing

KEY:

POPULATION SIZE

POPULATION RATIO RELATIVITY

POPULATION DATA BUTTON (MORE INFO)

GA BO RD TA NE NI RS ST / S

MA PR TER OC IA ES L SO RS

S EM S ST ER SY INE G

/ ICE POL RITY U SEC

TIO

MA RE NUF SE AC AR TU CH RI

E ST WA ING L YC EC

MENTAL THERAPIS TS

A SE

E CH

NB: HOVER OVER BUTTONS FOR DEMOGRAPHIC DATA BREAKDOWN

05 3

RE // D:

NU RS

PH THE YSICAL RAP IST S

TO R DO C

ID RO CH TE AS SEAR RE

Y VIT GRA ARCH E RES

HCA

ICS

ALT

PARAMED

RE HE SP CH MO AR AT ESE R

NEM

VER

STA T DIP ION LOM AT

R AI CE N NA

ENT

AGRICULTUR E PRODUCTION

WATER ANCE E MAINT N

FOOD

MEAT N PRODUCTIO

AMENITIES MANUFACTUR ING

MECHANICS

ICA

MUN

I AN

ICE

COM

TE M & NA NC E E

CON WOR STRUC KER TIO N S

L MATERIA S STOCKER

AL ERI MAT VERS MO

MINING OPERATOR S

ASTEROID MINERS

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1.4M2

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ME // //

Moving on from the formulated demographics and their subroles derived, the next stage for establishing our design criterea is to translate this into spatial typologies. Each established demographic will contain sub-groups with particular sub-roles, and these roles will require different working environments. Using the sub-roles of each demographic we can establish the type of ‘building’ or ‘function’ that is required for certain tasks shared amongst industries. The ideal spatial needs of these will be employed through a generative process of urban design.

0 D:

SPATIAL REQUIREMENTS 1: RESIDENTIAL & RESEARCH

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POPULATION TO TYPOLOGIES

RESEARCH UNIT SPATIAL REQUIREMENT The toal population is comprised of all of the support industries required for a thriving asteroid mining town. Starting from the number of asteroid miners, formulas were cdonstructed using global open-source data to find ratios required for the support industries. The sum of these ratios outputs the total of 6616, this is the population we will initially consider the spatial requirements for.

RESIDENTIAL UNIT SPATIAL REQUIREMENT

2.4M2

REFERENCES https://www.gov.uk/government/publications/food-statistics-pocketbook/food-statistics-in-your-

TYPOLOGIES

SUB-ROLES

DEMOGRAPHICS

MATERIAL PROCESSORS CONSTRUCTION WORKERS MINING OPERATORS MATERIAL STOCKERS MATERIAL MOVERS MANAGEMENT

SYSTEMS ENGINEERS M&E MAINTENANCE COMMUNICATIONS MANUFACTURE AMENITIES WATER MAINTENANCE AIR MAINTENANCE WASTE RECYCLING

MAIN DOCKS ESCAPE PODS AIR & WATER WASTE

DOCTORS NURSES PHYSICAL THERAPISTS

MEAT

MENTAL THERAPISTS

AGRICULTURE

MINERS MECHANICS

PARAMEDICS

SECURITY

HEALTHCARE

LEISURE

RESEARCHERS

HEALTHCARE

GRAVITY RESEARCH ASTEROID RESEARCH

MANUFACTURE

ATMOSPHERE RESEARCH MANUFACTURE RESEARCH

STORAGE

GOVERNMENT FARMING SUPPORT

POWER MINING RESEARCH

HEAD OF STATE

RESIDENTIAL

AGRICULTURE MEAT CULTIVATION

POLICE/SECURITY JANITORS

SECONDARY REQUIREMENTS

TERTIARY REQUIREMENTS

// D:

PRIMARY REQUIREMENTS

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GARDENERS

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FUNCTION INDEX： INDEX： MEAT CULTIVATION / PRINTING FOOD PROCESSING

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ESCAPE POD

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TYPOLOGY MASSING MATRIX

MAIN DOCKS

After deriving the population of the station and filtered that population into typologies This matrix shows the massing of the designed typologies which allows easier visualisation and aggregation experiments in the following aggregations.

POWER

FUNCTION INDEX： INDEX： POWER GENERATION

0 D:

STORAGE

FUNCTION INDEX： INDEX： VEGETABLE PRODUCTION & HARVESTING FOOD PROCESSING STORAGE

FUNCTION INDEX： INDEX： RECYCLING SYSTEM AIR, WASTE & WATER MANAGEMENT STALE EXTRACTION / DESTRUCTION

FUNCTION INDEX： INDEX： RECYCLING SYSTEM INPUT NEW ELEMENTS STALE EXTRACTION / DESTRUCTION

HEALTH CARE

OPEN SPACE

BAR

FUNCTION INDEX： INDEX： PHYSICAL THERAPY SURGERY G.P OFFICES/ROOMS WARDS LABS MORTUARY

FUNCTION INDEX： INDEX： GREEN SPACE, COMMUNAL SPACES, ACTIVITY SPACES

FUNCTION INDEX： INDEX： DRINKING AND GATHERING

MANUFACTURE

MINING FACILITY

FUNCTION INDEX： INDEX： 3D PRINTING PROCESSING/FABRICATION DISTRIBUTION STORAGE

FUNCTION INDEX： INDEX： PROCESSING CONSTRUCTION DOCKING COMMUNICATION MINING MACHINE PORTS STORAGE.

RESEARCH

RESIDENTIAL

FUNCTION INDEX： INDEX： LAB EXPERIMENT STUDY

FUNCTION INDEX： INDEX： BEDROOM COMMUNAL FOOD & SOCIAL EXERCISE

05 7

AIR & WATER RECYCLING

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AGRICULTURE

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AGGREGATION NUMBER 5

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MANUFACTURE+ STORAGE+ POWER+ MAIN DOCKS+ AGRICULTURE

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TESTING MODULE CONNECTIONS

Before creating a system to aggregate, we experimented with different massings and connections to explore four dimensionality of space arrangement.

AGGREGATION NUMBER 5

AGGREGATION NUMBER 4

MAIN DOCK+ POWER+ ESCAPE POD+ AIR & WATER RECYCLING HEALTHCARE

RESIDENTIAL+ POWER+ HEALTHCARE+ LEISURE

0 D:

STORAGE+ AGRICULTURE+ WASTE RECYCLING+ AIR & WATER RECYCLING+ MANUFACTURE

WASTE RECYCLING+ STORAGE+ 3D PRINTED MEAT+ ESCAPE PODS+ MANUFACTURE

RESEARCH+ HEALTHCARE+ STORAGE+ MANUFACTURE+ STORAGE

AGGREGATION NUMBER 5

AGGREGATION NUMBER 6

ESCAPE PODS+ LEISURE+ RESEARCH+ RESIDENTIAL+ STORAGE+ HEALTHCARE

AGGREGATION NUMBER 6

3D PRINTED MEAT+ HEALTHCARE+ POWER+ LEISURE+ RESEARCH+ WASTE RECYCLING

AGGREGATION NUMBER 7

AGRICULTURE+ STORAGE+ MINING FACILITY+ 3D PRINTED MEAT+ RESEARCH+ AIR & WATER RECYCLING

AGGREGATION NUMBER 6

ESCAPE PODS+ MAIN DOCK+ MINING FACILITY+ 3D PRINTED MEAT+ POWER+ STORAGE

AGGREGATION NUMBER 6

MAIN DOCK+ STORAGE+ RESEARCH+ MAIN DOCK+ POWER+ 3D PRINTED MEAT

AGGREGATION NUMBER 6

MINING FACILITY+ ESCAPE POD+ STORAGE+ ESCAPE POD+ MINING FACILITY+ MANUFACTURE+

// D:

AGGREGATION NUMBER 5

05 9

AGGREGATION NUMBER 5

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SCENARIO RESILIENCE

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SCENARIO 01: DEMAND SHIFT FOR SPECIFIC FUNCTION

Physical recreation and monitored exercise for the crew is one of the key components of human centered design for space. This is because the more time one spends in micro-G environments, the more muscle and bone degredation they experience. Although we have real data for exercise regiments on the International Space Station, it does not account for extended time periods the population will spend on our habitat compared to the ISS. In the event that we underestimated the requirments for physical therapy and exercise facilities, this scenario will test the ability of the system to adapt to providing a 1.5x boost in availability of these functions.

ADAPTATION BY ADDITION

0 E:

ORIGINAL STATE

06 3

ADAPTATION BY RELOACTION

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ADAPTATION BY INVASION

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SCENARIO 02: EMERGENCY EVACUATION

In case of an emergency an immediate evacuation must be strategized for the people on board. How can the spatial arrangement be optimised to facilitate this evacuation and maximise the efficiency of evacuation points or escape pods? This would be comparable to planning fire escape routes for buildings on earth, except the ciculation is now three dimensional.

ESCAPE POD

MINIMUM STEPS TO ESCAPE = 5 TARGET = MINIMISE TOTAL NUMBER OF ESCAPE PODS (MAXIMISE EFFICIENCY). THE CATCH IS THAT YOU ALSO WANT TO INCREASE SURFACE AREA IN CERTAIN CASES (FOR WINDOWS, AND LEAVINGG VACANT PLOTS FOR ADAPTABILITY)

0 E:

EVACUATION STRATEGY FOR STATE A [25 MODULES, 3 ESCAPE PODS]

ESCAPE POD

EVACUATION STRATEGY FOR STATE B [25 MODULES, 1 ESCAPE POD]

// E:

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SCENARIO 03: ECOLOGICAL SHIFT FROM ASTEROID MINING

Asteroid mining was incredibly successful in the early years, but inevitably led to a crash of the market and asteroid mining economy. It is no longer viable to have a deep space settlement solely focused on asteroid mining. The system must diversify itâ&#x20AC;&#x2122;s industrial and economical modules.

ORIGINAL STATE INDUSTRY: 100% ASTEROID MINING

0 E:

ADAPTIVITY BY REPLACEMENT: REPLACE REDUNDANT MODULES WITH ALTERNATIVE INDUSTRIES DIRECTLY TO DIVERSIFY INDUSTRY AND IMPROVE ECONOMIC RESILIENCE

SCENARIO SHIFT: ASTEROID MINING 60% REDUNDANT

// E:

06 7

ADAPTIVITY BY ADDITION: ADD NEW SPECIALISED MODULES FOR ALTERNATIVE SUPPORT INDUSTRIES. RETAIN AND ‘STORE’ REDUNDANT MODULES IN CASE THE STATE REVERSES BACK.

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SCENARIO 04: ASTEROID MINING AUTOMATION

ORIGINAL STATE: THE STARTING RATIO OF HUMAN MINERS TO ASTEROID MINING UNITS. SOME AUTOMATION IS CERTAIN BUT HUMAN OVERSIGHT, RESEARCH AND MAINTENANCE IS VITAL.

After some years of successful manned asteroid mining, technology is developed that means that asteroid mining requires half as many operators for the same production output. Does the system adapt to a reduced population of asteroid miners, or does it increase mechanics and use the same number of original operators to double output?

SCENARIO FUTURE STATE: THE RATIO OF HUMAN MINERS TO ASTEROID MINING UNITS HAS CHANGED DUE TO INCREASED AUTOMATION AND UNDERSTANDING OF THE INDUSTRY. HOW WILL THE SYSTEM ADAPT TO THIS SHIFT THAT WILL AFFECT INDUSTRY AND DEMOGRAPHICS?

0 E:

ORIGINAL STATE:

ADAPTIVITY BY HYPERPRODUCTION: IF THE DEMAND FOR MINING ALLOWS FOR IT, THE ORIGINAL AMOUNT OF WORKERS CAN CONTINUE TO OPERATE DOUBLE THE AMOUNT OF MINING UNITS, DOUBLING THE PRODUCTIVITY OF THE STATION. THE HABITIATION UNITS WOULD REMAIN STABLE AND THE INDUSTRIAL UNITS WOULD POTENTIALLY DOUBLE. HOW DOES THIS AFFECT THE REST OF THE SYSTEM?

// E:

ADAPTIVITY BY POPULATION REDUNDANCY: IF THE RATIO OF WORKERS REQUIRED FOR EACH MINING UNIT DECREASES, AND MINING DEMAND DOES NOT GO UP, THE WORKFORCE WILL BECOME REDUNDANT, AFFECTING THE RATIO OF HABITATION MODULES.

06 9

THE STARTING RATIO OF HUMAN MINERS TO ASTEROID MINING UNITS

B: //

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y og

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y og ol

gy lo po Ty

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s ie

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ts en im er

n io at om

T T A E ol ri si gy ni ng y ol Cr ? s ph sc Re n on on ES polo d Mi turi tial ture olog Typ E ce nt or De o io ti ti at ga ga T spa cede GY an M tem nari GI k Ty eroi ufac iden icul Typ port RG lu re re EX Why Pre TE Urb Sys Sce LO Doc Ast Man Res Agr Bar Sup OD Eva Agg Agg FO PO //F: //G: //H: //I: //J: //K: //L: ETH //M: //N: //O: HE TY M T A: //

typologies

Chapter 03

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172cm

// // // //

Because of the lack of gravity, human posture and movement behaviour would be completely different from what it is and what weâ&#x20AC;&#x2122;ve been designing on earth. Thus, to facilitate this particularity, ergonomics in space have been studied and considered within all the typologies design (NASA, 2014)

72cm

71cm

zero-gravity no activity voloume= 0.88 cubic meter

total habitable module volume cubic meter

ERGONOMICS IN SPACE

neutral body postures in Zero-gravity

25 20

optimal performance

performance limit

tolerable limit

5 0

mission duration

0 E:

07 3

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Dock Typology

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ELEVATION

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DOCK

The primary function of the ‘dock’ is a depot for the asteroid mining shuttles we have designed and call ‘astro-tugs’. The dock also seves secondary functions as a mechanic repair station for the 1-man space shuttles. Each dock is crewed by 3 individuals. one pilot, one mechanic, and one control. The dock is an ‘end’ rather than a ‘node’ for obvious reasons: it has no social functions, and as a dock it must interface with space in order to maintain clear access. This led us to designing a single airlock style dock. However, docks can also self aggregate, as shown overleaf.

PLAN

Capacity: 1 Calculated numbers of module: 450 Achieved numbers of module: 64*12 Function: Docking asteroid mining ships Prioritised Connecting Modules: Mining > Residential Number of connections: 1

0 F:

design proFile

N IO AT UL RC CI

A V CC A IA ESS DO CCES PRIM TO CKS S ARY TH VIA TO F AIR E FO SE URT LOC RGE CO HE K. ND R ARY AGG AIR REG LO ATE CK D . 8M

CK DO

R AN EPAIR D L S, OA MA DIN INT G E

Y BA

AST R

O-T

AST R

D-W

O-T

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DOCK AGGEGATION RULES

CORE NODE DOCK MODULE THE CORE NODE IS THE CENTRALISED DOCK MODULE THAT IS THE GATEWAY TO THE REST OF THE AGGREGATION. IT HAS ONE STANDARD AIRLOCK CONNECTION THAT CAN DIRECTLY CONNECT TO ANOTHER FUNCTION, AS WELL AS FOUR SPECIALISED DOCK AIRLOCKS TO CONNECT TO UP TO FOUR STANDARD SOCK MODULES.

The dock module exists in two forms so that it can self aggregate. This allows the formation of dock clusters which creates more centralised versions of the function.

SECONDARY DOCK MODULE THE SECONDARY DOCK MODULE HAS NO STANDARD AIRLOCK AND CAN ONLY CONNECT TO OTHER DOCKS. TO ACCESS OTHER MODULES, ONE MUST NAVIGATE TO A CORE NODE DOCK MODULE AND TRANSIT THE AIRLOCK BRIDGE.

0 F:

DOCK CLUSTER EXAMPLE BECAUSE OF THE AGGREGATION RULES OF THE TWO TYPES OF DOCK MODULE, DOCKS CLUSTER INTO A MICRO AGGREGATION. THIS HELPS STREAMLINE THE DOCK PROCESSES INTO ONE CENTER. HOWEVER, THIS SUB-AGGRGATION IS LIMITED TO 12 SECONDARY DOCK MODULES TO AVOID DISCONNECTING THE FURTHEST SECONDARY DOCKS FROM THE REST OF THE FORGE. THIS MEANS THAT THE DOCK CLUSTER OPERATES AS ONE TYPOLOGY, AND THE DOCK DISPERSION THROUOUT THE SYSTEM REAHCES A STONG BALANCE BETWEEN CONNECTIVITY AND CLUSTERING.

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07 9

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DOCK DESIGN

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STANDARD SYSTEM AIRLOCK CONNECTS TO THE REST OF THE FORGE

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SECONDARY DOCK RACK SYSTEM WITH DOCK SPECIFIC SECONDARY AIRLOCKS //

DOCK BAY AIRLOCK

INTERIOR DOCK BAY 01

DOCK DESIGN F: //

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AIRLOCK BRIDGE

EXTERIOR DOCK BAY 02

BOARDWALK

ASTRO-TUG

82 0 F: //

LANDING LIGHTS

PIER

GRAB RAIL

USER ACCESS TERMINAL

MECHANICAL ARM FOR REMOTE REPAIRS AND INSTALLATIONS

SHUTTLE CHARGE POINT

REPAIR COMPONENTS

BAY 02 ELEVATION

08 3

PROTOTYPE COMPONENTS

DIAGNOSTICS DISPLAY CONSOLE

ACCESS AIRLOCK

// F:

GRAB RAIL

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COCKPIT //

GRAPPLE MOTOR

ASTRO-TUG F: //

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ROCKET

SCANNER

ASTEROID CAPTURE GRAPPLE

TARGETED ASTEROID MATERIAL

84 0 F: //

AIRLOCK HATCH

AIRLOCK ARM

‘WIND’-SCREEN

THRUSTER

HEADLAMP GRAPPLE MOTOR

GRAPPLE

GRAPPLE ANCHOR

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SCANNER

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ASTRO-TUG

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AIRLOCK ACCESS CORRIDOR CHASSIS SKELETON FRAME COCKPIT

08 7

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Asteroid mining typology

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ASTEROID PROCESSING SYSTEM

0 G:

Atesciurem rae porrores illaute peribus enit dolupie nditias ea vene volore pre volentiost vendae. Itas duci temporitiis dipsus eatae int, sit aut estrumet vid molorerfera sit is magnia nisitatis ut offici ipicitatur, odi ut que endelest que venem voloris itatur, nam, quae moditatiur sitatias acilicta plaboratusam

design proFile Capacity: 15 Calculated numbers of module: 245 Achieved numbers of module: 245 Function: Crushing asteroid and material separation Prioritised Connecting Modules: Manufacturing > Dock > Storage Number of connections: 5

D SE ES IAL C R O PR ATE UT M O

LS ETA

ATE ILIC

CAR

D HY

U SO

N BO

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Asteroid processing

09 1

OID ER IN AST ERIAL MAT

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Manufacturing Typology

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MANUFACTURING MODULE

The manufacturing module fabricates and produces all the other modules in the settlement by 3D printing with material and resources extracted from the asteroid mining process. Other than repair, reproduction and expansion of the settlement, the manufacturing module as well processes material into commodities that is valuable and difficult to produce on earth, such as conductors.

COLLECTED BY ASTRO-TUG

PROCESSED AND SCANNED BY MINING MODULE

MANUFACTURING MODULE

Apart from producing the parts for the FORGE, the manufacturing module could also be rented by other companies or countries to produce other space infrastructure.

0 H:

Capacity: 25 Calculated numbers of module: 21 Achieved numbers of module: 34 Function: Manufacturing & assembly modules for the settlement Manufacturing Prioritised Connecting Modules: Mining > Storage > Power> Research lab > Dock Number of connections: 6

design proFile

EMERGENCY STOP ZONE

REPAIRMENT & REPLACEMENT ROBOT

MODULE TRANSPORTING DOCK

OFFICE & 3D PRINTING SHED: 3d printing for micro structures & commodities

ACCESSORY PRODUCTION & ASSEMBLY ZONE

3D PRINTING PLATFORM

09 5

MODULE ASSEMBLY ZONE

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MODULE ASSEMBLY ZONE

SPREAD HEADING

// //

EMERGENCY STOP ZONE

Accessory 3D printing

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ASSEMBLY RACK

6 REPAIRMENT & REPLACEMENT ROBOT

3D PRINTING PLATFORM

Raw material refill connections

ACCESSORY PRODUCTION & ASSEMBLY ZONE

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OFFICE & 3D PRINTING SHED: 3d printing for micro structures & commodities

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Series 3 functions as a transporting and repair robot in the system. They will periodically go through the whole settlement for repairing. The scanner allows problems to be identified, and minor problems such as small part replacement could be done in place.

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SERIES 3REPAIR ROBOT

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BACK - ENGINE

LEFT - ARM

RIGHT - WELDER

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FRONT - SCANNER

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Connection arm for module transport

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MODULE MANUFACTURING SPREAD HEADING PROCESS

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ASSEMBLY ARM

BAR MODULE UNDER ASSEMBLY

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MATERIAL TANK

SERIES-3 IN POSITION FOR MODULE TRANSPORT

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Residential Typology

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CONNECTION CORRIDOR

RESIDENTIAL

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The residential module utilises central-fugal force to create artificial gravity. According to research, in order for astronauts to obtain health in space for long term missions, gravity will be required for at least 2-3 hours a day. Thus, in the design we combined the gravity therapy with the residential module for astronaut to be under gravity for the enough period of time.

COMMUNAL AREA

INDIVIDUAL RESIDENTIAL PODS

design proFile Capacity: 36 Calculated numbers of module: 73 Achieved numbers of module: 76 Function: Accommodation, dining & communal space Prioritised Connecting Modules: Agriculture > Mining > Dock Gravity: 0.38G Rotation speed: 4.5rpm Number of connections: 2

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Artificial Gravity in Stanley Kubrick’s 2001: A Space Odyssey

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A NASA engineer takes a walk in simulated zero gravity around a mockup of a full-scale, 7.3 m (24 ft) diameter space station in 1964.

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ARTIFICIAL GRAVITY

Weightlessness imposes some physiological changes on the human body including: bone loss, cardio-vascular perturbations and, muscles deterioration. It also affects the astronaut’s sense of orientation and consequently can generate a lot of stress. To avoid these issues, we can generate artificial gravity with centripetal force, with a rotating system. The Force felt by the astronaut’s body depends on the rotation rate of the system and the distance of the body from the rotation axis.

Artificial Gravity in Stanley Kubrick’s 2001: A Space Odyssey

Artificial gravity for spacecraft

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“

ARTIFICIAL GRAVITY IS AN IDEA WHOSE TIME HAS COME AROUND, … AND AROUND, … AND AROUND…

” - PR. LAURENCE R. YOUNG, DEPARTMENT OF AERONAUTICS AND ASTRONAUTICS,

0.5G

0.25G

a=w2R 1g = 9.8 Newtons/kg

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w 2 = kg(1/R)

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1000

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ONE T Z

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COM

The design of the residential module is based on the balance between centrifugal force and human comfort rotation speed (Globus, 2017). As the residential module will be the module that the workers spend most time in, we decided that gravity could be reduced to reduce the size of the residential module. Referencing to the gravity on Mars, 0.38G could allow both sufficient gravity to keep people healthy and at the same time reduce the scale and rotation speed of the module.

ROTATIONAL RADIUS (M)

RESIDENTIAL MODULE SECTION

4.5

ANGULAR VELOCITY (RPM)

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17000

6500

4000

3500

3000

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SPREAD HEADING

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CORRIDOR EXTERIOR

COMMUNAL AREA (EATING AND LOUNGING)

GRAVITY TRANSIT CORRIDO

S 6 EA , AR ON T TI EN TA ER RO IFF D

CORRIDOR EXTERIOR

GYMS

RESIDENTIAL PODS

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Agriculture Typologies

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AGRICULTURE

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The agriculture supports the settlement with 10 different types of plant-based food. The rotating structure provides vegetation with gravity in order to grow. Hydroponics could make food production up to 10 times more efficient The geometry of the module is determined by individual vegetationâ&#x20AC;&#x2122;s growth condition and required space

design proFile Capacity: Calculated numbers of module: 73 Achieved numbers of module: 76 Function: Vegetation growing, harvesting & processing Prioritised Connecting Modules: Residential > Synthetic Meat Gravity: 0.38G for internal structure Number of connections: 2

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FARMING AREA

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SERVICE PIPES

CIRCULATION

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gravitational Hydroponic farming

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HYDROPONIC SYSTEM

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design proFile

SYNTHETIC MEAT

Capacity: Calculated numbers of module: 35 Achieved numbers of module: 35 Function: Meat production & processing Prioritised Connecting Modules: Residential > Agriculture Number of connections: 5

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MEAT PRODUCTION PROCESS

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SPREAD HEADING

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Bar Typology

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BELTER BREWERY (BAR)

The bar module is a gathering place with social and recreational functions. The asteroid mining station is, among other things, a workplace. The belter brewery is a place to relax, celebrate, make toasts, and socialise with you colleagues. This is an important function within the system - employees can cross-pollinate ideas, communicate, and socialise in a non-working environment. This is important for the mental health and morale of the population. The system in place at the brewery involves a mostly automated serving environment, with manual supply and distribtion to the bars scattered throughout the system.

Capacity: 25 Calculated numbers of module: 38 Achieved numbers of module: 38 Function: Recreational drinking & gathering place Prioritised Connecting Modules: Agriculture > Open Space > Residential Number of connections: 5

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design proFile

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BASE INGREDIENTS TANKS

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CENTRIFUGAL DISTILLERY

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BELTER BREWERY

Where people habit, so does craft and culture. Scotland has whiskey, Italy has coffee. We suppose that the asteroid belt would be no different... The design for the belter brewery imagines the establishment of a local beverage company with it’s roots and brand set in the emerging asteroid mining industry (The name “belter” comes from asteroid “belt”). BAR

The design of the brewery system revolves around the zero-gravity nature of the environment, so we designed a centrifugal distillery that doesn’t rely on gravity to separate varying density substances, as earth-based distilleries typically do.

SERVO-SERVING MECHANICAL ARMS MAGNETIC COUNTER

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BELTER BREWERY Y

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BELTER BREW

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A SIGNATURE ASTROBEVERAGE

Belter brew is the front-line beverage product of Belter Brewery ltd. Our key considerations when designing this aspect of the project were, how do people drink in zero-g, and is there a way we can improve the standard solution of a bag and a straw method? We managed to find a convenient solution to this by learning about the behaviour of surface tension liquids in weightlessness. This led us to the design of the Belter creased-edge beverage container. The design of the bar also acknowledges that astronauts probably wouldn’t be full-time bartenders in the near future, so we designed an automated system for serving drinks that is operated by “servo-serving” mechanical robots.

CREASED-EDGE BEVERAGE CONTAINER THIS CONTAINER DESIGN DRAWS UPON THE DESIGN OF FUEL TANKS IN SPACE ROCKETS THAT NEED TO REIGNITE IN A WEIGHTLESS ENVIRONMENT. WHAT HAPPENS IS THAT THE SURFACE TENSION OF THE LIQUID CAUSES IT TO TRAVEL ALONG THE CREASE OF THE SURFACE, MEANING THE LIQUID CAN BE MOVED IN A DIRECTION WITHOUT ANY EXTERNAL FORCE SUCH AS GRAVITY OR PRESSURE/SUCTION. THE CONTAINER’S PRINCIPALLY LOW-TECH DESIGN IS 3D-PRINTED RIGHT ON THE FORGE ITSELF.

REFRESHING BELTER BREW BELTER BREW IS THE REFRESHING BEVERAGE ASSOCIATED WITH THE GEOGRAPHICAL LOCALE OF THE ASTEROID BELT. THE EXACT RECIPE IS A SECRET, BUT THE UNIQUE PROCESS INVOLVING LABGROWN PLANTS AND THE CENTRIFUGAL DISTILLERY ARE THOUGHT TO BE KEY TO IT’S SIGNATURE TASTE.

MAGNETIC BASE

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THE SIGNATURE BELTER BREW CONTAINER HAS A MAGNETIC PLATE ON THE BASE. MAGNETS AND VELCRO ARE APPLIED TO ALMOST EVERY SMALL OBJECT IN WEIGHTLESS ENVIRONMENTS, IN FACT, VELCRO WAS FIRST INVENTED AS AN ENGINEERING PRODUCT FOR USE IN SPACE BEFORE IT WAS SPUNOFF AS A CONSUMER PRODUCT.

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Support Typologies

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External connection for cargo unloadďź&#x152; mainly for import from earth

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STORAGE PODS

CORRIDOR: collection and distribution of storage within the settlement

STORAGE PODS

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STORAGE The storage module contains 12 individual storage containers which allows external access and make it easier to install and replace.

design proFile

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Capacity: Calculated numbers of module: 11 Achieved numbers of module: 11 Function: Storage of backups supplied from earth and goods to be transported to earth Prioritised Connecting Modules: Manufacturing > Mining > Residential Number of connections: 6

CHEMICAL RESEARCH

ASTRONOMY AND MISSION CONTROL

ANTENNA

GRAVITY RESEARCH

RESEARCH LAB The research lab is made up of 4 parts and is operated and used by 4 groups of scientists.

MATERIAL & MANUFACTURING RESEARCH

design proFile Capacity: 20 Calculated numbers of module: 10 Achieved numbers of module: 10 Function: Research laboratory and mission control Prioritised Connecting Modules: Mining > Residential Number of connections: 2

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HUMAN PHYSICS RESEARCH

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LIFE SUPPORT Air: Oxygen (21%), Carbon Dioxide(<1%), Nitrogen (78%)

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Oxygen could be obtained from lunar rock. Nitrogen is most easily available from the Earth, but is also recycled nearly perfectly. Also, nitrogen in the form of ammonia (NH3) may be obtainable from comets and the moons of outer planets.

design proFile Capacity: Calculated numbers of module: 20 Achieved numbers of module: 20 Function: Water, air and waste management Prioritised Connecting Modules: Number of connections: 1

ZERO-G RECREATION AREA

0.38G RECREATION AREA

ZERO-G RECREATION AREA

20000

Capacity: 300 Calculated numbers of module: 10 Achieved numbers of module: 9 Function: Open green space with leisure and sports incorporated. Prioritised Connecting Modules: Residential > Bar > Mining Number of connections: 10

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The green space module provides a place for workers to relax. It is designed as a sphere so that the height a person feels could meet the definition of a public space. The module is designed with many connections so it could at the same time act as a node, and encourage more people to pass through, which is considered to benefit the workerâ&#x20AC;&#x2122;s mental health.

design proFile

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LEISURE & OPEN SPACE

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POWER Air: Oxygen (21%), Carbon Dioxide(<1%), Nitrogen (78%)

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design proFile Capacity: 5 Calculated numbers of module: 17 Achieved numbers of module: 18 Function: Power supply Prioritised Connecting Modules: Number of connections: 1

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HEALTHCARE The healthcare module consists 4 sections linked with a corridor. It provides workers with regular health check as well as emergency healthcare.

design profile Capacity: 3 (doctors) Calculated numbers of module: 10 Achieved numbers of module: 10 Function: Emergency/regular health treatment & enquiry Prioritised Connecting Modules: Residential > Mining Number of connections: 6

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MONITOR

OPERATING ROOM

ELEMENTS RESEARCH

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OPERATING EQUIPMENT

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HEALTHCARE EXPLODED DIAGRAM E

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CONSULTING ROOM EXAMINATION ROOM TREATMENT ROOM PHYSICAL THEARAPY OPERATING ROOM TOILET MALE TOILET FEMALE

SECTION B

ENTRANCE 120㎡ RECEPTION 60㎡ WAITING AREA 60㎡ FACILITY MANAGEMENT 60㎡ STAFF REFRESHMENT 60㎡ SUPPLY STORAGE 60㎡

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SECTION D MEDICAL RECORD MEDICATION STORAGE CLEAN ROOM UTILITY

60㎡ 20㎡ 60㎡ 60㎡

CORRIDOR

STAFF FLOW

SECTION C 50㎡

NURSE WORKING AREA

50㎡

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OPEN-PLAN ADMINISTRTION AREA

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PATIENT FLOW

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design proFile

EMERGENCY ESCAPE POD

ESCAPE POD

1 COMMUNAL DOCK

Capacity: 25 Calculated numbers of module: 76 Achieved numbers of module: 76 Function: Distatchable modules for emergency escape. Prioritised Connecting Modules: Number of connections: 1

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ATTITUDE CONTROL THRUSTERS

PHOTOVOLTAIC PANEL

EGRESS/INGRESS HATCH EQUALIZER

EMERGENCY BEACON

RETRO-ROCKET NOZZLES

ACCESS HATCH ACCESS PANEL

ESCAPE POD

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SECTIONï¼&#x161;ESCAPE POD AND DOCK // L:

ELEVATION

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gy lo po Ty

gy lo ng

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? s ph sc Re n on on ES polo d Mi turi tial ture olog Typ E ce nt or De o io ti ti at ga ga T spa cede GY an M tem nari GI k Ty eroi ufac iden icul Typ port RG lu re re EX Why Pre TE Urb Sys Sce LO Doc Ast Man Res Agr Bar Sup OD Eva Agg Agg FO TH //M: //N: //O: HE ME T A: //

Method

Chapter 04

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Evaluation Criteria

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SYSTEM GOALS

CONNECTED HIERARCHY OF SPACES (EACH FUNCTION HAS UP TO 3 CONNECTIONS, AND THERE ARE ONLY CONNECTIONS TO THE 3RD DEGREE = BETTER)

Our goal is to create a highly connected and accessible environment. This means prioritising high value connections, and maximising the occurence of them within our walkable distance. In other words, to maximise the number of high priority functions in the most connected positions, relevant to any single starting component. The distance to high priority connections should be minimised, based on our theory of walkability. This involves arranging the spaces in a complex hierarchy, where the highest priority functions are the most connected, while low priority or irrelevant functions are less conncected as a result. This hierarchy should exist from the perspective of any single component, creating a very complex hierarchical aggregation.

DISCONNECTED HIERARCHY OF SPACES (EACH FUNCTION HAS ONLY 2 CONNECTIONS, AND THERE ARE CONNECTIONS TO THE 5TH DEGREE = BAD)

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STARTING POINT

MOST CONNECTED

1ST DEGREE CONNECTIONS (HIGHEST PRIORITY)

2ND DEGREE CONNECTIONS

3RD DEGREE CONNECTIONS

MAXIMISE NUMBER OF HIGH PRORITY FUNCTIONS IN THE SHORTEST RANGE POSSIBLE. WE ACKNOWLEDGE THAT THIS GOAL COULD HAVE A SELECTIVE BIAS FOR MORE COMPACT AGGREGATIONS.

4TH DEGREE CONNECTIONS

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5TH DEGREE CONNECTIONS (LOWEST PRIORITY)

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LEAST CONNECTED

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CURRENT WALKABILITY INDEX MEASUREMENT ON EARTH

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DENSITY

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THE NEW ‘WALKABILITY’ SPREAD DEFINITION HEADING

Atesciurem ‘Walkability’ rae porrores has been illaute used peribus as an urban enit dolupie measurement nditias to define ea vene the volore success preof volentiost an urban vendae. environment. Itas duci It is temporitiis strongly related dipsus toeatae the well-being int, sit aut of an estrumet urban environment vid molorerfera and the sit residence is magnia or pedestrians nisitatis utwithin offici ipicitatur, the area. odi Walkability ut que endelest has been quequalified venem voloris and quantified itatur, nam, intoquae different moditatiur measurements. sitatias Walkability acilicta Index (WI) plaboratusam has been one of the most significant and widely used. However, the particularity of a zero-G environment in an isolated community makes the conventional definition of walkability inadequate to be measured in our project. Human movement and the environment of a ‘pedestrian’ will be completely different. Thus, we have adopted and redefined the measurement of walkability based on the WI on earth to suit our system, and will utilise it to evaluate the success of the urban aggregation.

DIVERSITY

DESIGN

ACCESSIBILITY

DISTANCE TO TRANSIT

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MOVEMENT BEHAVIOUR

WALKABLE AREA

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Need for floor plane

H = 2.10m L = 1.56m w = 1.23m Volume = 4.03m3 Movement Speed (Walking): 1.4m/s

15 min walk = 1250m

L z y H

H = 0.70m L = 2.96m w = 1.23m Volume = 2.55m3 Movement Speed (Weightless): 2m/s

15 min movement = 1800m

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According to research, Walkability index (WI) offers the best performance within a 1.6km radius, which is within 15-20min walk (Morency, 2018).

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SCALING WALKABLE DATA FOR POPULATION SIZE

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NYC POPULATION = 8.4 MILLION NYC SIZE = 784KM2 DENSITY OF NYC = 8,400,000/784 = 10,714.286 PEOPLE PER KM2

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WALKABLE RADIUS (EARTH) = 1.250KM WALKABLE AREA (EARTH)= ΠR2 = 4.91KM2 DENSITY OF WALKABLE AREA = 10,714.286 X 4.91 = 52,607.143 PEOPLE PER WALKABLE AREA (KM2) THEREFORE: 52,607.143 PEOPLE FOR 4.91KM2 IS WALKABLE AREA METRIC ON EARTH

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Because walkability is influenced by multiple factors, rather than linear radius walking distance, but also density and livability, we decided to reference walkable cities on earth, and according to density, scale the walkable distance on earth to form the walkable distance metric in the space settlement. New York city is one of the most walkable cities in the world according to walkable measurements. Thus, we chose New York as a reference to calculate the actual walkable area that will suit the designed settlement.

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Walkability metric: Calculation process

WALKABILITY METRIC

MINING TOWN POPULATION = 2722 WALKABLE RADIUS (SPACE) = 1.8 KM2 WALKABLE AREA (SPACE) = 10.17876 KM2 WALKABLE AREA DENSITY = 2722/10.18 = 267.38 PEOPLE PER WALKABLE AREA WALKABLE VOLUME (AS SPHERE) = 24.43 KM3 DENSITY OF NYC APPLIED TO A WALKABLE AREA IN SPACE: DENSITY OF NYC * WALKABLE AREA (SPACE) = 10714.286 X 10.17876 KM2 = 109,058.14 PEOPLE PER WALKABLE AREA IN SPACE SCALE THE WALKABLE AREA IN SPACE ACCORDING TO POPULATION 2722 X (10.17876/109,058.14) = 0.254KM2 PER WALKABLE AREA METRIC IN SPACE ΠR2 = AREA: R = √ (0.254/ Π) = 0.284KM = 284M THUS, THE NEW WALKABLE DISTANCE METRIC IN SPACE COULD BE DEFINED AS 284M IN RADIUS.

Walkable distance metric = 1250m

15 min movement = 1800m

Walkable distance metric = 284m

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15 min walk = 1250m

THE FORGE

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Walkability metric: comparison scaling process

NEW YORK CITY

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Transformation rule

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SYSTEM 1: CELLULAR AUTOMATA

In a CA system we create a grid of agents (called cells) who change their state over time based on the changing states of their neighbours until reach a state of equilibrium, which doesnâ&#x20AC;&#x2122;t necessarily exist as the same according to the start status, or the complexity of the system.

The biggest difference between an urban fabric on earth and in space is that in space routs could be arranged 3-dimentionally, and connectivity could be achieved 3 -dimensionally because of zero gravity. Thus, at first we proposed a 3D CA system.

Cell State Transformation

Neighbour state Transformation

Transformation rule

CA Cell Space

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LAND USE PATTERN

BUILDING TYPOLOGY: MASSING, FACADE AND HEIGHT

SPAWN CENTRES

FUNCTIONAL POD 1

FUNCTIONAL POD 2

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LAND OCCUPANCY: VACANT/UNDER CONSTRUCTION/REDUNDANT

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2D/3D URBAN CA MODEL

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SYSTEM 2: DLA

DLA (Diffusion Limited Aggregation) describes the growth system in which the particles forming the structure wander around randomly before attaching themselves (“Aggregating”) to the structure. Structures as such could be seen in coral reef and snowflakes. Going forward, we have elected to take a DLA approach rather than CA because the CA model is restricted to a grid and therefore doesn’t offer the same flexibility as a DLA for a system that uses different scales of geometries.

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CONTROL OVER GEOMETRY

NO RULES

APPLIED CONNECTION RULES

please click on space above if video doesn’t play

H2 H2 H3

S1 S2 H1

All possible connections H3-S1 H2-S1 H1-S1 S2-H1 S2-H2 S2-H3 H3-S2 H2-S2 H1-S2 S1-H1 S1-H2 S1-H3 All other self-connections

Connection rules applied H3-S1 H2-S1 H1-S1 S2-H1 S2-H2 S2-H3 The biggest problem in a DLA algorithm is the randomness of aggregation. However, we managed to gain control by identifying connections between different elements.

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MINING MODULE

connect to

This page explains the possible connections between one module to another. The mining module with 5 connections and the docking module with 6 connections are used as examples. Connecting the dock to the mining module as a result has 5*7=35 possible outcomes. This could cause a potential problem in which the possibility of aggregation might be too large and hard to evaluate when there are more modules in the aggregation. Limiting and making the connections more specific thus appears more important in future exploration.

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Aggregation rules: connection faces

AGGREGATION RULES: DIRECT CONNECTION

D2 D6

D5 D3 D1 D0

DOCKING MODULE

M0-D2

M0-D3

M0-D4

M0-D5

M0-D6

M1-D0

M1-D1

M1-D2

M1-D3

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Aggregation rules: CONNECTING DOCKING MODULE TO MINING MODULE

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We managed to control the rotation of aggregated modules by defining the plane of the connection surface. By rotating this plane, the aggregated module could be rotated accordingly, giving us more possibilities and more control in terms of the overall geometry.

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AGGREGATION RULES: ROTATION

ASSIGNING DIRECT FACE CONNECTIONS

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Aggregation rules: CONNECTING DOCKING MODULE TO MINING MODULE

For the convenience of rotation, connections would function better when designed circular

CO NT

STAGE 1: CONSTRUCT AGGREGATION MODULES

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INPUT AXIS AND FACES

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START SET UP MODULE CONNECTIONS

RESIDENTIAL MODULES ARE AT THE CENTRE OF WALKABLE ZONES

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INPUT Module massing

INPUT AXIS AND FACES

Stages 1 - 3 of the aggregation methodology covers the process of module set-up to the production of an aggregation. The key part of these methodology stages is the rule setting, as this allows us to gain a higher degree of control over the aggregation output.

INPUT Module massing

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AGGREGATION METHODOLOGY

REMOVED DIR CONNECTIONS

NUMBER OF AGGREGATION MODULES:427

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// COMMUNAL

STAGE 2: SETTING CONNECTION RULES

STAGE 3: EMPLOY AGGREGATION SYSTEM

When setting up rules for the DLA algorithm aggregation, only direct connections can be suggested. Due to this constraint, we must use a process of elimination to remove unwanted connections, due to the vast amount of posiible combinations. This process is show below.

ULE COMBINATIONS = 4356

NATIONS EMPLOYED = 3963

Rule Removal:

As residential modules are the primary communal module, any connection between residential modules and industrial modules should be culled (with 2 exceptions)

RETAINED DIRECT CONNECTIONS

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FINAL RULES INPUT INTO AGGREGATION SYSTEM

OUTPUT AGGREGATION FOR STAGE 4

STAGE 4 SEMI-STOCHASTIC RULE BASED AGGREGATION GENERATION

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INDUSTRIAL

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Identical Industrial module typologies should not connect to One another, with the exception of mining / manufacturing. This is to prevent over representation of support modules within walkable zones.

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Rule Removal:

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STAGE 4: RETRIEVAL ALL MODULE CENTROID

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ALL OTHER MODULE CENTROIDS

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# OF MODULES = 2722/14 = 194

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ALL OTHER MODULE CENTROIDS

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Stages 4-6 of the aggregation methodology details the process of determining walkability, and extracting the metrics to do so. Through the examination of the post aggregated form, we can measure distances between module centres. We test these metrics from the residential module,as it is inhabited for the most time, and is the centre of communal activities in The Forge. This keeps all activities in the staion in closed loops, always originating and ending from the living quaters, giving a valid reflection of typical life.

AGGREGATION METHODOLOGY

c ear Res

INPUT POPULATION: 2722

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STAGE 5: MEASURE LONGEST PATH

STAGE 6: TEST FOR ‘WALKABILITY

GENERATE LINES FROM RESIDENTIAL CENTROIDS TO ALL CLOSEST TYPOLOGY CENTROIDS

IF A < B THEN THE AGGREGATION IS WALKABLE

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EXTRACT & MEASURE EACH RESIDENTIAL CLUSTERS LONGEST LINE

DISTANCE A LONGEST LINE LENGTH

A < B WALKABLE RADIUS LIMIT LONGEST LINE = CLUSTERS ‘WALKING’ RADIUS

DISTANCE B = 284M

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RESIDENTIAL NETWORK CLUSTERS

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Residential Network clusters contain 1 of each typology the closest typology to each residential module is represented in that residential modules ‘cluster’

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DEFINING AGGREGATION DATA

Before aggregation iteration commences, it is necessary to highlight what metric outputs translate to in terms of walkability in a data diagram. This allows for fast performance reviews in a universal language.

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HIGH PERFORMING

LOW PERFORMING

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AGGREGATION WALKABILITY The aggregation walkability metric gives us 2 key pieces of information. The number of items displayed in the list tells us the number of residential modules in one aggregated system. This suggests a capacity of a residential module in the aggregation, as well as a suggestion of unused support modules aggregated around the station. The numbers themselves represent the distance from each residential module to the closest every other typology in the system scaled between 0 & 1.

THEORETICAL-THRESHOLD WALKABILITY RADIUS DISTANCE BETWEEN MODULES (THE LOWER THE BETTER)

284 METRES

Number of residential Modules to measure from

1.0

180M

0 METRES

0.6

0.0 WALKABILITY RATING (THE LOWER THE BETTER)

The walkability rating assesses modules proximity by tending to 0 as modules are placed closer. If modules exceed the 284 metre radius a metric of 1 or over is output, this aids in identifying poor performing aggregations.

CLOSEST WALKABLE RADIUS The closest walkable radius gives the exact distance in metres of any residential module to the closest typology option available.

M THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

The farthest walkable radius gives the exact distance in metres of any residential module to the furthest typology option. This does not include modules across the station, only the closest representative of each typology

THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

AVERAGE WALKABLE RADIUS The average Walkable radius is simplly the arithmetic mean of all module connections from one residential typology.

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FARTHEST WALKABLE RADIUS

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The first iteration of aggregations were very time consuming to produce, as such only a small population size could be assessed for performance. This iteration produced a sample population which all performed within the desired criteria, suggesting the theoretical threshold for a walkable radius can be achieved in a 3 dimensional aggregation

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ITERATION 1: HIGH PERFORMERS CO

AGGREGATION ITERATION 1

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ITERATION 1: LOW PERFORMERS

ITERATION 1: AVERAGE PERFORMERS

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CONSTRAINT 1: MANUAL STOCHASTIC SYSTEM

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The stochastic nature of the system means we have little control over specific outcome variations. In addition to this, the reset button necessary to generate aggregations also causes the system to forget about any previous aggregations, meaning all aggregations must be documented after each generation. This process is time consuming and extremely inefficient, and therefore must be improved.

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1 AGGREGATION PER RESET

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DATA DELETED WHEN AGGREGATION RESETS

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After carrying out the first iteration of aggregation, it is apparent that the system has some imperfections which can be remedied. The combination of stochastic aggregations and rules setting to exercise a degree of control has outputted some unexpected results, in particular the number of residential modules in the aggregation. It is also a laborious script to run manually, which can also be amended through automation. This will lead into the next iteration of aggregations ,where these constraints will hopefully be bypassed.

SOLUTION 1: AUTOMATION Automating the aggregating system solves any issues regarding efficiency. Instead of pressing a button for each aggregation, thousands of iterations can be generated instead. Using a data recorder native to grasshopper, iteration data can be stored so data isnâ&#x20AC;&#x2122;t lost, as IT IS shown TO BE ABOVE. once a large enough population size of aggregations are generated, they can be ran through an evolutionary solver (Galapagos) to find the optimal performer of the population for our walkability metrics.

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RESOLVING SYSTEM CONSTRAINTS

AGGREGATION SYSTEM CONSTRAINTS

1000 AGGREGATIONS PER RESET

DATA RECORDED FOR EACH AGGREGATION

ITERATION OPTIMISATION FOR WALKABILITY (GALAPAGOS)

CONSTRAINT 2: POSSIBLE AGGREGATION OUTPUT OPTIONS

SOLUTION 2: POPULATION SAMPLING

NUMBER OF POSSIBLE AGGREGATION COMBINATIONS FOR 100 MODULES = 2.8 [X10158]

It is impossible to attain data on every person alive today or test something on every human. Instead, a population sample is taken to test the performance or validity of a study. This will also be necessary within these aggregation iterations. This will mean we cannot achieve an optimal configuration, We will however generate a configuration that satisfies our metrics to a high degree.

The number of possible aggregations for 100 modules is greater than the number of atoms in the universe squared. It is simply impossible to search through all possible configurations, this causes issues for an optimisation process.

KEY:

CONSTRAINTS

POPULATION SAMPLE SIZE PER ITERATION = 1000 AGGREGATIONS AGGREGATION SYSTEM

SOLUTIONS MODULE INPUT Module Inputs to aggregate

SOLUTION: INPUT NUMBER OF RESIDENTIAL MODULES

AGGREGATION ALGORITHM

CONSTRAINT 3 AGGREGATION RULES As explained previously, the input rules dictate which modules can connect directly

CONSTRAINT 2 SOLUTION: AUTOMATION SOLUTION: POPULATION SAMPLING RESET The reset button refreshes the stochastic aggregation algorithm.

CONSTRAINT 3: UNDER/OVER REPRESENTATION OF MODULES Residential:

Manufacturing:

Capacity: 454

Capacity: 6

6/194 modules

Low Representation

70/194 modules

Very high Representation

Iteration 1 showed us that some modules in the aggregation were over represented, whereas some where under represented. This would result in some modules being over capacity, and some redundant.

SOLUTION 3: SETTING PRE-DEFINED RESIDENTIAL CAPACITY as wALKABILITY IS DEFINED THROUGH CONNECTIVITY FROM RESIDENTIAL MODULES, DEFINING A NUMBER OF RESIDENTIAL MODULES WILL ALLOW FOR A MORE EVEN REPRESENTATION OF ALL MODULES THROUGHOUT THE STATION. Currently the max capacity of a residential unit in space is 40. This equates to a number of 68 residential modules.

PRE-DEFINED RESI NUMBER :

AMENDED MODULE NUMBER 68 + (2722/13) = 277

17 3

The number of modules in the aggregation is set as an input into the algorithm.

CONSTRAINT 1

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NUMBER OF MODULES IN AGGREGATION

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LOW PERFORMER

Following on from assessing the system constraints, we have responded by creating an automated aggregating system. The video shows the set up of key parts of the script, as well as the iterative assessment of hundreds of modules in mere seconds. From this we have extracted a range of high and low performative aggregations, based on the output metrics. From this automated process we have now passed a major hurdle imposed by the system, and can continue gaining further control and manipulation in Stage 3. the increased level of control is illustrated in the next page, where all iterations are high performing, and attained through a much more intuitive process outlines previously.

AVERAGE PERFORMER

AGGREGATION ANIMATION

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https://vimeo.com/419716420

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N.B. IF VIDEO DOES NOT PLAY PLEASE FOLLOW THIS LINK TO VIEW ON VIMEO:

CO NT

FINAL HIGHEST PERFORMING

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As stated previously, the amedned system is greatly advantageous, providing better results faster. However, it also allows us to view some of the system constraints in a new light. The contro over module number has not been enganed and therefore there is still an under / over representation of certain modules. This will now have to be resolved in Stage 3.

it is important to note, that even though this aggregation is the otimal performing iteration out of all there high perfroming strategies, there is an extremely high chance that if the population sample siize was increased, this iteration would be surpassed. Moving Forward INTO Stage 3, it may be worth taking multiple population samples per iteration, to improve our automated searcheâ&#x20AC;&#x2122; accuracy.

AGGREGATION 2 & 5: RESIDENTIAL MODULES

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REVIEWING SYSTEM CONSTRAINTS

ITERATION 2 BEST PERFORMERS

Number of residential modules far too small for an asteroid mining community

Since the attempted resolution of several system constraints, the only one that reamins is the residential typologies infrequent occurence in all aggregations, high or low performing. This can be clearly seen in Iterations 2 & 5 which contain only 7 and 8 residential modules respectivley. A solution to this would be to force the system into producing a certain number of modules for all typologies, so one rule does not detract from another, as the system compromises equally for all modules

POSSIBLE SOLUTION: SET ALL TYPOLOGY NUMBERS

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HIGH PERFORMING

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ITERATION 2 BEST PERFORMER ANALYSIS

For the final iteration of Stage 2, it is important to analyse the highest performing aggregation, so we can identify any biases that affect metrics. Module size is shown the be a definite bias, and this must be remedied in Stage 3.

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OVERALL DATA EVALUATION

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AGGREGATION WALKABILITY The walkability metrics starting from each residential module show an all around high performing score for every walkable ‘cluster.’ This shows the aggregation has clusters within close proximity to one another throughout the aggregation.. This suggests cluster distances are within a couple of modules of each other.

THEORETICAL-THRESHOLD WALKABILITY RADIUS DISTANCE BETWEEN MODULES (THE LOWER THE BETTER)

284 METRES

List Length = Number of residential Modules

1.0

83.7M

0 METRES

0.2

0.0

WALKABILITY RATING (THE LOWER THE BETTER) NB: As mentioned previously The walkability rating assesses modules proximity by tending to 0 as modules are placed closer. If modules exceed the 284 metre radius a metric of 1 or over is output, this aids in identifying poor performing aggregations.

CLOSEST WALKABLE RADIUS = 59.6M The closest radius of 59.6 metres corresponds with the walkablity rating of 0.209947 in the list of values displayed. This is due to the size of modules affecting proximity, so distance measured will always depend on the connecting module sizes. This shows us that the walkability metric must be normalised to account for this bias.

M THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

The farthest walkable radius of 121.3 metres is less affected by the bias of module sizes. This corresponds with the value 0.426971 in the list of values displayed. This shows the largest walkable cluster in the aggregation is still half the size of the theoretical threshold of 284 metres.

THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

AVERAGE WALKABLE RADIUS 83.8M The average walkable radius shows us that all modules are highly connected, only placed a few module separations apart from each other at most.

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FARTHEST WALKABLE RADIUS = 121.3M

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CO NT

GOAL: FORMULATING AN AUTOMATED SYSTEM

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We previously achieved a level of automation in our aggregation system. However we did not delve in to the system and understand how we can manipulate it to ensure we do get the best result possible. The algorithm thus far is just a simple search algorithm, that concludes after finding a high performing iteration.

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HIGH PERFORMER ENDS SEARCH

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TRUE OPTIMAL POTENTIALLY SKIPPED

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REVISED PRIMARY GOALS

Through a series of extensive iteration explorations in Stage 2, there are elements of the system we have created which are either inaccurate, or could be done better. This must be addressed prior to restarting the aggregation experiments.

SYSTEM SEARCH START

PROPOSED: STAGE 3: APPROPRIATE & EFFICIENT ALGORITHM

The aim in this aggregation section of the portfolio is to fidn the optimal performing aggregation iteration, that satisfies all of our output metric criteria. The first stage to being able to do this efficiently is to create a system which will guarentee the output of the highest performer of a sample population.

SYSTEM SHOULD STOP WHEN OPTIMAL IS FOUND

SYSTEM SEARCH START

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GOAL: MEASURING WALKABILITY PREVIOUS: STAGE 2 In Stage 2 the script has determined walkability and walkable clusters to be the closest direct point from the Residential module origin. This is not the most accurate method to finding the closest module to the point of origin as shown below

RESIDENTIAL MODULE ORIGIN

CLOSEST LITERAL POINT MEASURED BY SYSTEM The closes point judged by the system is literally the closest point but not the closest accessible point.

PATH BETWEEN MEASURED POINTS

CLOSEST ACCESSIBLE POINT IN SYSTEM

Residential Module

The closest point judged by the system is literally the closest point but not the closest accessible point. This would provide a better walkability

Connecting Module

PROPOSED: STAGE 3

PROPOSED CURVE NETWORK

we now hope to improve the accuracy of measuring walkability by creating a network of curves connecting all modules. This presents a classic ‘travelling salesman’ problem to solve to find the shortest path between the residential modules and all other modules.

RESIDENTIAL MODULE ORIGIN

PROPOSED SHORTEST PATH THROUGH ALGORITHMIC RESOLUTION

CLOSEST MODULE

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The Travelling Salesman Problem

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Aggregation Automation

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The traditional design process is typically manual, where the designer formulates id encouraged, however only confuses the designer when attempting to design larger i

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Generative Design is the process of iterating through several Design options generated through the construction of a system. This system contains input constraints set y the designer, and output metrics, for the designer to assess for performance. This is the methodology which we have employed through the aggregations so far and will continue to follow. It is the most logical methodology for generating spatial clusters of modules which follow certain rules, as doing this manually would take many many times longer to execute.

The generative design process is more automated than its traditional counterpart. T sometimes results in an unexpected variation which the designer could not have form

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Generative Design: The Fundamentals

GENERATIVE DESIGN

SET CONSTRAINTS START

GENER

TRADITIONAL DESIGN PROCESS

deas to explore through concept development, which are then evaluated for feasibility. This is a valid and proven method when tackling design projects in which artistic prescription is infrastructures in a logical and elegant way.

FEASIBILITY

CONCEPTS

END FINAL OUTPUT

EVALUATION

GENERATIVE DESIGN PROCESS

The power of generative design comes in the form of the ability to produce thousands of variations of an idea, and measure each performance to determine the best performing output. This mulated manually.

HIGHEST PERFORMERS

FINAL OUTPUT

RATE ITERATIONS

END

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RE-ITERATE IF NON-OPTIMAL

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EVALUATION

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A genetic algorithm takes inspiration from the evolutioanry process of nature. It re consists of breeding, mutating and sampling the genetic population, to craete a sele

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INITIAL POPULATION

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A genetic algorithm driven system tends to be more intuitive than a simple search algorithm. This is especially the case when there are a large number of genes and gene combinations. There are, however, circumstances where employing this system is not the most efficient. The current system we are employing is stochastic, meaning that other than the fixed rules,which cannot be manipulated generatively, there is no correlation between a gene and its performance. This means the system cannot recognise high performing genes effectively, and so runs many generations, which repeat each other. This adds to the search time as well as oppening the possibility of missing the optimal strategy. This points us in the direction of using a brute force algorithm, which would eliminate our primary system issues.

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Generative Design: Evolutionary Solver assessment

STRATEGY 1: GENETIC ALGORITHMS

POULATION SELECTION SAMPLE

SYSTEM VARIABLES: GENOTYPES

The system variables of a genetic algorithm are the parameters that control the d to how genes control the appearance of organisms

X Y Z GENERATIVE DESIGN GENES

GENES I NATURE

SYSTEM VARIABLES: PHENOTYPE

THE PHYSICAL MANIFESTATION OF THE SYSTEM PARAMETERS

THE PHYSICAEXP OF AN ORGANISMS

GENETIC ALGORITHM PROCESS

elies on changing a system which has variables that change the measurable output metrics, called genes. It tehn explores the possible genes to find the best performing value. This process ection of genes higher performing than the initial input population. This is an iterative process that could continue until the optimal result is reached

HIGH PERFORMING MUTATE SELECTION SAMPLE

TEST GENERATION PERFORMANCE

BREED HIGH PERFORMING GENES

GENETIC ALGORITHM SYSTEM ISSUES

GENES ARE STOCHASTIC AND CANNOT BE IMPROVED OR MUTATED

ISSUE 2: SELECTION SAMPLE IS IMPERFECT

TRUE OPTIMAL POTENTIALLY SKIPPED

HIGH PERFORMER FROM SAMPLE IS NOT OPTIMAL FROM POPULATION SYSTEM SEARCH START

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ISSUE 1: STOCHASTIC GENES & PERFORMANCE

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PRESSION S GENES.

END

PERFORMANCE

IN E

LOW PERFORMING

SIMILAR GENES

design. This is similar

NEW GENERATION

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BRUTE FORCE EXAMPLE: DEEP BLUE CHESS [1997]

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The most famous moment for all of AI was arguably when the Program ‘deep blue’ beat one of the greatest chess players ever in 1997. Although Machine Learning gets all the attention today. AI SYSTEMS ORIGINATED AS unintuitive but complicated exhaustive search algorithms. This kind of search is known as a ‘brute force attack’

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The generative design systems we will employ in the experiments will be based on brute force. This page shows how the system, we have named ‘deep space’ functions. Brute force is a classic method of computational problem solving with ‘Artificial Intelligence.’ Ultimately, brute force is an exhaustive search algorithm that searches all possible options and returns the highest performing input for said ‘goal.’ This is inferior to genetic algorithms when considering highly complex and large design systems, but with smaller, simpler systems, brute force is a faster and more accurate method to employ.

DEEP BLUE ‘BRUTE FORCE’ SYSTEM EXPLANATION CHESS MOVE 1

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Generative Design: Brute force attacks

STRATEGY 2: BRUTE FORCE ALGORITHMS

SEARCH LEVEL 1

SEARCH LEVEL 2

SEARCH LEVEL 3

SEARCH LEVEL 4

SEARCH LEVEL 5

Deep Blue’s brute force algorithm searched for every possible move several moves ahead, similar to how world class chess players plan several moves ahead. This method is very computationally heavy. However, If the system theoretically searched for every possible combination of moves, it could never lose a game. This system works very well when there are a limited amount of options to search through.

THE FORGE’S BRUTE FORCE ALGORITHM: ‘DEEP SPACE’ [2020] SYTEMS PROCESS DIAGRAM

1000 AGGREGATIONS PER RESET DEEP SPACE

1.0: GENERATE POPULATION OF AGGRGEGATIONS

DATA RECORDED FOR EACH AGGREGATION

2.0: ASSESS EVERY AGGRGEATION’S OUTPUT METRICS

3.0: OUTPUT OUTRIGHT HIGHEST PERFORMING OF WHOLE POPULATION

HIGHEST PERFORMING AGGREGATION

DEEP SAPCE’S BRUTE FORCE SYSTEM ADVANTAGES ADVANTAGE 1: FAST PROCESSING FOR SMALL POPULATION SAMPLES

ADVANTAGE 2: SYSTEM UNAFFECTED BY STOCHASTIC VARIABLES

THE DEEP SPACE BRUTE FORCE SYSTEM IS THE GENERATIVE METHOD THAT WILL BE EMPLOYED

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The system will search through all possibilities, so the nature of the aggregation has no effect on the highest performer found

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it is easy and fast to assess a small population’s out put metrics and determine the best permorming..

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Aggregation Experiments

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MEASURING WALKABILITY FROM CIRCULATION NETWORK

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GOAL 1: PRODUCE CIRCULATION NETWORK CURVE

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AGGREGATION EXPERIMENT GOALS

As stated previously several times, we aim to improve the accuracy of our experiments by developing a network of curves to measure from, instead of the old, inaccurate method of measuring direct node distances. This adds another goal onto our experiment on top of connectivity and walkability. Achieving this will allow us to more precisely and accurately assess aggregations with regards to our desired walkability goals. This will ultimately bring more legitimacy to the experiments conducted.

Clusters were previously defined as connections from the closest module of each typology to the residential modules measured as direct routes from each module centres rather than through the network of modules.

PREVIOUS WALKABILITY MEASUREMENT METHOD

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Improving walkabiltiy measurements

CONSTRUCT CURVE NETWORK TO REDEFINE ‘WALKABLE CLUSTERS’ DEFINED IN STAGE 2

THE LONGEST AND SHORTEST LINES DEFINED THE BOUNDS OF THE WALKABLE CLUSTERS, DRAWN AS DIRECT PATHS FROM MODULE CENTRES TO THE NEAREST RESIDENTIAL MODULE

REVISED WALKABILITY MEASUREMENT METHOD Newly defined clusters will follow the curve circulation path when the goal is achieved. This will give us a greater understanding of module arrangements, and what constitutes as a high performing aggregation.

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CO NT

THE TRAVELLING SALESMAN

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THE TRAVELLING SALESMAN PROBLEM

The construction of the desired curve network was solved by applying simple Hamiltonian cycle methods to the aggregation geometry. This new curve network allowed us to tackle the travelling salesman problem head on. With the aid on the A* algorithm, we were able to solve the shortest paths we desired by reducing the search size necessary to find the shortest path.

The most common example of the travelling salesman problem asks the question: ‘In a list of cities, with known distances between them, what is the shortest possible route one can take starting and finishing from the same point?’ This is known as an NP-Hard problem, as the time required to calculate this problem, exponentially increases with the number of cities and connections possible in the problem.

CONTEXTUALISING THE PROBLEM

1 O:

Solving for walkability networks

THE CLASSIC TRAVELLING SALESMAN EXAMPLE

2D NETWORK

The classic travelling salesman example, when simplified presents a problem of points and lines. This problem is not dissimilar to the issue of finding the shortest path Between residential modules, and the closest members of its ‘cluster.’ The problem complex as these paths must be navigated through a dense network of possible connections, on a 3 dimensional plane

3D NETWORK

SOLVING THE TRAVELLING SALESMAN PROBLEM IN A 3D AGGREGATION CONSTRUCTING CIRCULATION NETWORK ACQUIRE MODULE CONNECTION PLANES

REMOVE CURVES LONGER THAN LONGEST MODULE

COMPLETE CURVE NETWORK: HAMILTONIAN CIRCUIT

ACQUIRE MODULE CENTRES

INTERCONNECT ALL POINTS & PLANES TEST IF INDIVIDUAL CURVES ALIGN WITH CENTRES AND CONNECTIONS

FINDING SHORTEST PATHS FROM CIRCULATION NETWORK Now the circulation network has been successfully constructed, we have a system in which we must solve the traveling salesman problem. The number of lines and destinations in this network exposes the nature of the NP-Hard problem. It would be impossible to compute all possible outcomes, as it would require hundreds, if not thousands of years. Instead, we can employ the a* Algorithm to find the shortest route through the generated network by inputting a desired connection between 2 points. This narrows the scope of searching, making it possible to find the shortest route once more.

SHORTEST WALK NETWORK DESIRED CONNECTION LINE ACQUIRE MODULE CENTRES

INPUT CURVE NETWORK [HAMILTONIAN CIRCUIT]

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SOLVE SHORTEST PATH WITH A* ALGORITHM

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These visualisations enlighten us to what is happening ‘behind the scenes’ so to speak when iterating the aggregations. Now we have accomplished solving the travelling salesman problem, we can explore what new measurable metrics could possibly be open to being introduced into our experiment goals. This visualisation helps us see that the measurement of module proximity can be assessed. This can be explored in the next iteration generation.

RESIDENTIAL CLUSTER CENTRE

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Module ‘Walkable’ Cluster Matrix

VISUALISING MODULE NETWORKS

RESIDENTIAL CLUSTER CENTRE

WALKABLE CLUSTER EXAMPLE 1: MODULE CONSTITUENTS Module Cluster 1 is shown as an example for how the clusters which we are measuring for walkability are roughly composed. This visualisation allows us to see how module proximity manifests within aggregations too. Cluster 1 shows a wide spanning, long path connections in general.

5 MODULE AWAY FROM RESI

6 MODULES AWAY FROM RESI

1 MODULE AWAY FROM RESI

WALKABLE CLUSTER EXAMPLE 2: MODULE CONSTITUENTS Module Cluster 2 is shown as an example for how the clusters which we are measuring for walkability are roughly composed. Cluster 2 shows a compact and close aggregation for this section, as all connections are all very close.

2 MODULES AWAY FROM RESI

WALKABLE CLUSTER EXAMPLE 3: MODULE CONSTITUENTS Module Cluster 3 is shown as an example for how the clusters which we are measuring for walkability are roughly composed. Cluster 3 shows a medium spanning walkable area, with no direct connections but very consistent close connections.

3 MODULES AWAY FROM RESI

4 MODULES AWAY FROM RESI

2 MODULES AWAY FROM RESI

WALKABLE CLUSTER EXAMPLE 4: MODULE CONSTITUENTS Module Cluster 4 is shown as an example for how the clusters which we are measuring for walkability are roughly composed. Cluster 34 shows very long connections, reducing its walkibililty. Despite this, the other 2 examples are show to be high performing. This is something to consider for the iterations, as i unwalkable cluster should affect the output metric of a whole aggregation.

7 MODULES AWAY FROM RESI

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4 MODULES AWAY FROM RESI

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DEFINING REVISED DATA

After reworking our walkability metric, not much has changed in the way we assess and display the data. The main changes since Stage 2 at this point are the walkable radius ranges. As the typologies have been defined, the methods of determining walkability have become more accurate and produce higher value metrics, accounting for turns and module circulation.

LOW PERFORMING

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THEORETICAL-THRESHOLD WALKABILITY RADIUS DISTANCE BETWEEN MODULES (THE LOWER THE BETTER)

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0.0 WALKABILITY RATING (THE LOWER THE BETTER)

CLOSEST WALKABLE RADIUS The closest walkable radius gives the exact Walkable distance in metres of any residential module to the closest typology option available. This included turns around corners etc.

M THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

The farthest walkable radius gives the exact walkable distance in metres of any residential module to the furthest typology option. This does not include modules across the station, only the closest representative of each typology.

THE LOWER THE DISTANCE THE HIGHER THE AGGREGATION PERFORMANCE

AVERAGE WALKABLE RADIUS The average Walkable radius is simplly the arithmetic mean of all module connections from one residential typology.

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FARTHEST WALKABLE RADIUS

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HIGH PERFORMER

Using the brute force solver ‘deep space’ we constructed, we have iterated a 3rd generation of aggregations. As the newly refined typologies have been input into the system, the distances measure between modules has been increased on average. This is due to the increase of module sizes. As a result of this, a high performing iteration is one that has a walkability below the 284m threshold, as most do not meet this criteria now.

HIGH PERFORMER

ITERATION 3: ANIMATION

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Based on building the system and resolving previous problems with iterations, we have identified new metrics in which we can control during our generative design process. Solving the shortest path problem computationally also opened the door to measure, and set constraints / thresholds on certain module proximities.

3.0 RESI TO MANUFACTURING & DOCKS: MINIMUM 5 MODULE SEPARATION

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Measurimg Module Proximity

GENERATING NEW OUTPUT METRICS

4.0 MINE TO MANUFACTURING: MINIMUM 2 MODULE

5.0 MINE TO DOCKS: MINIMUM 3 MODULE SEPARATION

MEASURING & ENFORCING PROXIMITIES 1.0: Resi to bar = 5 modules. This is derived from a proportion of relative modules, as well as their social / work relationships. If a residential module is greater than 5 modules away from a bar or green space, the cluster is considered as disconnected and therefore produces a poor output metric factored into the aggregation. 2.0: Resi to Mine = 4 modules. This is derived from the necessity to be within a close proximity to one of the stations primary workplace. If these 2 modules are too disconnected, it could compromise the entire systems walkability. 3.0: Resi to Manufacturing & docks = 3 modules. This is derived from the necessity to be within a close proximity to the stations primary workplace. If these module separations are too great, too high a population will have to migrate across the station daily. 4.0: Mine to manufacturing = 2 modules. These 2 typologies should always maintain a close proximity as their functions are closely tied. Large separations of these modules would result in the need for large scale transport systems, as opposed to being resolved spatially through design. 5.0: Mine to docks = 3 modules. These 2 typologies should always maintain a close proximity as their functions are closely tied. Miners are also likely to be processors and other mining related professions..

ADDITIONAL PROXIMITY REQUIREMENT: ESCAPE PODS TO ALL = 3 MODULES, SIMILAR TO A FIRE ESCAPE ON EARTH, THESE MODULES ARE NEEDED REGULARLY AND WITH EVEN DISTRIBUTION THROUGHOUT THE STATION IN THE EVEN OF ANY POSSIBLE FAILURE.

DETERMINING SYSTEMâ&#x20AC;&#x2122;S EXACT PROXIMITIES

POOR PERFORMANCE AGGREGATION IN THRESHOLD NOT MET

1 EDGE = 1 MODULE

COUNT CURVE EDGES TO OBTAIN MODULE SEPARATION

TEST IS MODULES ARE BELOW THRESHOLD

TAKE SHORTEST PATH CONNECTIONS

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IF MODULES LIE WITHIN THRESHOLD, THE METRIC IS SATISFIED

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The mining modules require access for the collection and processing of asteroids. Therefore there must be open space around a mining moduleâ&#x20AC;&#x2122;s entrance.

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In addition to connectivity, walkability and proximity, access is also an important factor to consider for modules. Ass we have elaborated on the processes that occur in the typologies, we now understand some typologies will require open adjacent areas to dock and input / output to the station. In a stochastic aggregation it is difficult to control exactly where modules go, so a solution is to make the system output a poor performance if the modules access is shown to be poor.

DOCK MODULES

A dock module is where the workers will depart onto their asteroid mining journeys. This cannot be done if the entrance / exit is obstructed. Therefore the docks must also have space available at its openings.

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Measuring Module Proximity

EXTERNAL ACCESS DEPENDANT MODULES

MANUFACTURING MODULES

The manufacturing module has a constant stream of small vessels and machines flying around its vicinity. It therefore requires a level of openness and access to open it up to these processes.

METRICISING ACCESS

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POOR ACCESS

2.0 ELIMINATE LINES WHEN THEY COLLIDE WITH GEOMETRY

MODERATE ACCESS

GOOD ACCESS

MORE LONG LINES = GOOD ACCESS = HIGH PERFORMER

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1.0 PROJECT LINES FROM MODULE FACES

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3.0 MEASURE AVERAGE LINE LENGTH PER MODULE

MORE SHORT LINES = BAD ACCESS = POOR PERFORMER

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DEFINING FINAL DATA

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AGGREGATION WALKABILITY OPTIMISE FOR 0 THEORETICAL-THRESHOLD WALKABILITY RADIUS: 284 METRES

OUTPUT TO OPTIMISE: AVERAGE WALKABLE RADIUS

MODULE PROXIMITY: OPTIMISE FOR 0

0.371 OUTPUT TO OPTIMISE: AVERAGE THRESHOLD SATISFACTION

OUTPUT TO OPTIMISE: AVERAGE LENGTH OF PROJECTED LINE

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FINAL ITERATION: ANIMATION

The culmination of all of our methods and theory, as well as 3 generations of iterations have brought us to this page of final iterations, where the optimal form will be derived, and fianlised from hereout. The output metric in these aggregations is much more legible, as it has been scaled and averaged to 1 metric optimising to 0.

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BIBLIOGRAPHY

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CHAPTER 1 CONTEXT SPACE TRAJECTORY 1973 NASA’s space station Skylab: https://www.columbian.com/news/2013/nov/10/satellite-hits-atlantic-but-what-about-nextone/ 1998 International Space Station (ISS) launchedhttps://en.wikipedia.org/wiki/International_Space_Station 2012 First cargo sent by private company (SpaceX) to ISS https://www.britannica.com/topic/Dragon-spacecraft 2023 First asteroid mining operation https://newatlas.com/asteroid-mining-future/44961/ SPACE HABITAT PRECEDENTS Bernal Sphere: https://space.nss.org/bernal-sphere-space-settlement/ stanford torus: https://en.wikipedia.org/wiki/Stanford_torus O’Neill CYlinder: https://en.wikipedia.org/wiki/O%27Neill_cylinder Hermes space station: https://www.arnoldrenderer.com/news/ares-juice/ STANFORD TORUS &LINEAR CITY city maps: https://madridusp.wordpress.com/history-of-planning/before-1985/ torus: https://www.pinterest.co.uk/pin/130182245455546858/

CHAPTER 2 STRATEGY COMPLEX ADAPTIVE SYSTEM Holland, J. H. (1992) ‘Complex Adaptive Systems’ Research Library. Daedalus; Winter 1992; 121(1); pp. 17.

CHAPTER 3 TYPOLOGY ERGONOMICS NASA. (2014), Human integration design handbook. [Online][Accessed on 15th May 2020][https://www.nasa.gov/sites/default/ files/atoms/files/human_integration_design_handbook_revision_1.pdf] RESIDENTIAL Globus, A. (2017), Space Settlement Population Rotation Tolerance. University of Michigan. [Online][Accessed on 15th May 2020][http://space.alglobus.net/papers/RotationPaper.pdf]

CHAPTER 4 METHOD THE NEW WALKABILITY DEFINITION Ropars, G.L. and Morency, C. (2018) @Walkability: Which Measure to Choose, Where to Measure It, and How?’ National Academy of Sciences: Transportation Research Board. 2672(35); pp.139–150. WALKABILITY METRIC NYC map: http://getdrawings.com/new-york-city-map-vector

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DLA SYSTEM Bourke, P. (2014), DLA - Diffusion Limited Aggregation. [Online][Accessed on 10th May 2020][http://paulbourke.net/fractals/ dla/]

THE FORGE Located on the asteroid belt, an asteroid mining station set in the near future is home to 2000 asteroid miners. Our design takes a systematic approach to settlement design at a building and urban scale. We critique existing modular space stations, as well as futuristic designs to develop a design solution to what is typically tackled as an engineering problem. We propose an adaptive aggregated system based on complex rules to determine the arrangement of typologies in four dimensional space. This challenges the notion of the planar city configuration, and leverages the weightlessness of our alternative context to demonstrate applications of complex non-planar systems.

MCMY_THESIS