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Using Computational Techniques for the Optimal Design of Evolving Habitats

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  • Space Architecture

Abstract and Figures

The goal of this paper is to demonstrate the possibility of using existing solutions and concepts developed and used for earth applications as a design architecture for outer-space habitats. The future habitats/cities will need to evolve constantly, fixing a form, a system or a program is not the solution to adapt to an environment that we will learn a lot from when we get there. The design for a habitat and its systems will require constant modifications to adapt to changes in the environment, our knowledge of it and/or our reaction to it. Interior and exterior organizations will certainly change rapidly depending on new requirements. To produce an optimal design at a fast pace and correctly we need to use computational techniques such as parametric design or topology optimization. The new design solution should be the best according to a chosen set of conditions. For example: well-being, comfort, ease of operation and construction. With the help of software such as Rhino/Grasshopper and SIMOC we can demonstrate the practicality and the necessity of this approach for future human settlements in any extreme environment.
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IAC-21-B3.7.10
Using Computational Techniques for the Optimal Design of Evolving Habitats
Thomas J.-R. Lagardea*
a Space Architecture Technical Committee, American Institute of Aeronautics and Astronautics, 12700 Sunrise
Valley Dr #200, Reston, VA 20191, United States, info@spacearchitect.org, thomas.lagarde@outlook.com
* Corresponding Author
Abstract
The goal of this paper is to demonstrate the possibility of using existing solutions and concepts developed and used
for earth applications as a design architecture for outer-space habitats. The future habitats/cities will need to evolve
constantly, fixing a form, a system or a program is not the solution to adapt to an environment that we will learn a lot
from when we get there. The design for a habitat and its systems will require constant modifications to adapt to
changes in the environment, our knowledge of it and/or our reaction to it. Interior and exterior organizations will
certainly change rapidly depending on new requirements. To produce an optimal design at a fast pace and correctly
we need to use computational techniques such as parametric design or topology optimization. The new design
solution should be the best according to a chosen set of conditions. For example: well-being, comfort, ease of
operation and construction. With the help of software such as Rhino/Grasshopper and SIMOC we can demonstrate
the practicality and the necessity of this approach for future human settlements in any extreme environment.
Keywords: Computational design, SIMOC, Space Architecture, Mars, Human Exploration, Site Planning
Acronyms/Abbreviations
SIMOC = Scalable Interactive Model of an
Off-world Community
ECLS = Environmental Control Life Support
System
RECLS = Regenerative Environmental
Control Life Support System
CAD = Computer Aided Design
Rhino = Rhinoceros 3D
Grasshopper = Rhino visual scripting objects
CO2 = Carbon dioxide
O2 = Oxygen
H2O = Water
PV = Photovoltaics
3D = Three spatial dimensions
4D = Three spatial dimensions + Time
dimension
.json = JavaScript Object Notation Format
.epw = EnergyPlus Weather Data Format
1. Introduction to computational design and
Scalable Interactive Model of an Off-world
Community (SIMOC) software
1.1 Introduction to computational design
Computational design is a field of computer aided
design (CAD) that includes many different concepts:
designing with data, processing power, parameter
setting, generative design, 3D modelling and
visualization tools [1]. The 3D modelling and
visualization tools have been used extensively in the last
40 years and the use of CAD software for creative tasks
is now a standard across many industries such as
architectural design or mechanical design.
The design process with CAD software
requires external data in order to create correct and code
compliant drawings. Different tools such as Excel sheets,
standard tables or pre-configured blocks are used by the
designers to correctly size their shapes and volumes.
Over the years, a library of external standards has been
created and the designers currently need to rely on
literature to properly draw their parts or spaces. An
example of that external set of standards is the
international building code [6].
This paper will explain the reasons and the
necessary steps for creating a direct link between
external data sets and open-source weather, energy and
forming tools. After such a link is established, it
becomes possible to draw lines, shapes, volumes and
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dimensions directly and automatically into the design
software. The designer doesn’t need to translate and
import external raw data. The code should also be
totally free and modifiable so as to be improved and
scaled up.
We need to first explain the different
components that will help us in this automatic process.
The first one is parametric design, it starts with a set of
data. This data set is linked to parameter settings, which
can scale up or down a design and influence the result
of scripts that will modify in turn the shapes created.
This process is still based on the designer input. It gives
more flexibility to designing objects since parameter
settings can allow a project to be adapted to changing
conditions (for example more crew, less energy, etc)
The second component, generative design is
based on certain constraints in which the scripts will
create multiple propositions, analyse them and find out
which ones responds best to the limits set by the
designer. It is an intensive computing process. This
process allows the creation of a shape or object that is
the optimal solution based on the input from the user.
The increase in processing power in the last
decade has allowed those processes to be more efficient,
even if Moore’s law is slowing down because of
physical limitations, a threshold has been reached that
allows scripts to help the design decisions in an efficient
way, many designers have used those tools to achieve
good results especially with weather analysis [2].
1.2 Introduction to SIMOC
SIMOC is a research and educational platform
for the simulation of a hybrid mechanical and biological
regenerative life support system or RECLSS, in a Mars
habitat. It is hosted by the National Geographic
organization and was created by an international team
headed by Kai Staats. SIMOC is freely available online
[7] and can be used by anybody, including specialists
and non-specialists. The data collected through SIMOC
will help to balance the required elements to ensure the
survival and well-being of a human crew on Mars. It
also aims to determine the minimum amount of cargo to
be shipped and the minimum energy consumption for
the duration of the mission.
The team behind SIMOC has developed a
comprehensive system based on published research [3].
SIMOC is an agent-based model, which is characterized
by the simulation of the actions and interactions of
autonomous agents. These independent agents exchange
currencies such as Oxygen (O2), Carbon Dioxide (CO2)
or Water (H2O). There are nine general categories for
these agents, sub-divided into smaller individual
categories. These categories are Inhabitants, ECLSS,
Agriculture, ISRU, Structure, Fabrication, Power,
Mobility, and Communication. More information can be
obtained by reading the free and downloadable guide
[3].
The SIMOC interface is web-based and can be
accessed from anywhere on any device [7]. An example
of the interface and the first page can be seen in Figure
1.
Figure 1. The first SIMOC page where a mission can be
configured.
1.3 The integration of the results in a 4D design
The main interface of SIMOC, after
configuring the mission, shows numerical and graphical
representations of the raw data being created, see Figure
2 for reference. Those numbers and graphs can be used
by a designer to build a 3D representation, for example
the amount of CO2 stored can be used to size the storage
areas.
Figure 2. A full SIMOC interface. New panels can be
added to the interface to have a good view of the
ongoing results of the simulation.
After a simulation is complete, the data file can
be exported. The data is saved as a .json file type. It is a
standard text format of storage for data storage. This
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result file is the most important data set for this project,
we will use it as the foundation to build the different
elements of the base.
The initial parameters of the simulation are the
dimension of the greenhouse, the dimension of the
habitat, the number of PV arrays, the number of battery
packs, the type and number of ECLSS modules, the size
of the storage tanks and the type and quantity of edible
plants.
We can import data from the SIMOC website
into numbers and letters that a computational design
software can understand. In this instance, we will
Grasshopper from McNeel but others such as Dynamo
from Autodesk could be used.
To accomplish that task, we are using elements from a
plugin for Grasshopper called Jswan, as shown in
Figure 3. Those components can extract and store the
data needed from the main .json file into the software.
Figure. 3. The SIMOC .json extraction and utilization
inside Grasshopper with the JSWAN plugin.
2. Data-driven design with SIMOC and
Grasshopper
The goal of this research project is to make
available to current and future architects, system
engineers, designers and others, the tools to correctly
size their buildings and ECLSS components.
This project is open-source, modifiable and
capable of integrating new environments and parameters.
It is possible therefore to simulate other extreme
environments such as the Moon, Europa or others with
any amount of crew or storage for any amount of time.
The first step in any design project is to know your
environment. For Mars, the temperature, wind speed,
cloud cover and pressure data are extracted from a .json
file created by Amber Thomas [13]. The .json file was
built using a daily minimum and maximum value inside
Gale crater.
We will need to create an .epw file with that data, it
will be the second most impotant component of this
project.
In order for that data to be correctly interpreted by the
other tools, it is necessary to convert all the data points
into a continuous list of hourly data. The EnergyPlus
Weather Data Format (EPW) [10] that we will use
requires 8,760 data points, which corresponds to the
number of hours in a year. This operation is
accomplished by using the different values mentioned
before as upper and lower limits and populating the rest
with a set of linear values that gradually progress from
one limit to the other.
An .epw file requires other entries to be used,
such as solar radiation or humidity. We can get those
data points from different published papers or websites
such as Seasonal and interannual variability of solar
radiation at Spirit, Opportunity and Curiosity landing
sites [4]. The wind direction and speed are from a paper
called Gale surface wind characterization based on the
Mars Science Laboratory REMS dataset [5] and
humidity levels can be extracted from a NASA website
[14].
All the data sets are combined and configured
to create the required .epw file, this action is
accomplished in Grasshopper by using components such
as the ones showed in Figure 4
Figure.4 Creation of an .epw file that is as close as
possible to the Martian environment.
With both .epw and .json files loaded in the software
and correctly integrated in the code, a topography file
can be imported. The project uses a topographic file that
represents Gale Crater because the Curiosity rover from
NASA is currently collecting weather data there. In
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addition, this site has many different layers of
interesting sediments. A perfect environment for a
Human exploration mission.
We need to import a CAD file of the terrain in the
code, this file can be built of meshes or breps. After
importing a terrain file we can start to run a terrain
analysis process using a specific part of this code, it will
show the user many important visual information about
the area selected for the base.
Water flow analysis can show where to find the most
interesting samples that were close to water for a long
period, see Figure 5.
Figure. 5. Water flow analysis of the terrain
An elevation analysis as shown in Figure 6,
should be used in combination with pressure and travel
time requirements.
Figure. 6. Elevation analysis of Gale crater.
Finally, a slope analysis is useful when
selecting a terrain for the location of the base. A flat
terrain will be less difficult to build on than a sloped
terrain. In Figure 7, the areas where the slope is too
strong are represented in red and should be avoided for
the construction of a base, except for radiation shielding
measures.
Figure. 7. Slope analysis of the terrain.
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After processing and examining all that visual
information, the designer can select a site and start
laying out the base. The user needs to select CAD
geometries (points, lines, volumes, or surfaces) already
present in the model space and move them around the
topography. The code will automatically place the
different structures on the topography and adapt the
modules to the terrain where the geometries are located.
An example of this automatic layout function is shown
in Figure 8.
Numerous other components of the base are
also automatically placed on the topography such as
electrical cables or the tube connecting the habitat and
the greenhouse. The electric cables are a good example
of the benefits of using this type of code for site
planning. It will give the designer the shortest path
between two points and therefore optimize the length of
the cables. This planning process can help optimize the
storage and transportation problems for setting up a base.
We will demonstrate the advantages of using
this code for general planning purposes by discussing
the case study of the greenhouse.
3. Case study: Greenhouse design
Due to the low amount of solar radiation that
reaches Mars, having a transparent Greenhouse is not
sufficient to properly grow earth-based plants. The
solution requires supplemental artificial lighting.
The code created can be used to optimally orient
the greenhouse, it will use the solar radiation data and
the sun vectors coming from the .epw file (Figure 9).
Figure. 9. A 3D representation of the annual solar
radiation intensity at Gale Crater.
With the .epw data set, the code will orient the
building at different angles and will calculate which
orientation will provide the maximum solar exposure
with the least amount of heat collected.
Figure. 8. Automatic placement of the different parts of the
base according to the slope of the terrain.
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Once the correct orientation is determined and the
location is chosen. The greenhouse is placed in the
model window is size being determined by the .json file/
After being placed, it can be populated with the plant
trays that were used in the SIMOC simulation file.
The SIMOC uses plant trays [3] that measures one
meter by one meter. In the SIMOC simulation used for
this paper, it was determined that the crew needed 20
trays of wheat, 30 of cabbage, 10 of strawberries, 50 of
radish, 50 of red beet and 50 of onions.
Those trays are directly imported into the
greenhouse with manual location selection, see Figure
10. Future research will aim to automate the process and
provide an optimal placement of the different plant
trays.
Figure. 10. Manual layout of the greenhouse with
automated amount of plant trays.
The number of PV arrays and the number of
batteries required is calculated by the SIMOC
simulation for the needs of the greenhouse and for
general use. The code can optimally orient and place the
PV arrays and the batteries on the topography and can
calculate the amount of power that will hit each
structure, see Figure 11.
This information on a building is useful for
cooling and heating purposes, for the PV array it will
tell the designer how much power each panel will
produce a year. Future evolution of the code will
calculate cooling and heating loads for the module.
The footprint of the greenhouse is also an
important information, the code will calculate how
much material needs to be removed for the greenhouse
to lay flat on the topography. This gives the designer
helpful information and can improve his decisions
regarding the layout of the base.
The greenhouse is a good example for the
utility of the code in the planning phase. The designer is
given the tools he/she needs to correctly size a base and
to choose the best location for its different parts(best
implies, the most economical in terms of setup time,
storage transported, heating loads and other parameters)
4. Lessons Learned and future evolution
The existing tools for earth-based energy simulation
can and should be used for Martian or other extreme
environments. The creation of a practical system to link
the SIMOC project and different 3D design software is
Figure 11. Amount of solar radiation that is
reaching each structure in a year
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a necessary step in exploring new pathways of
simulation and planification.
The possibility of using existing open-source
software and data sets allows future designers to start
their project with a strong basis, validated by previous
research and successful simulations.
The current code is the result of the involvement of
just one individual, see Figure12, but it is hoped that by
releasing the code to the public, it will be improved
upon and will benefit future designers and planners.
Efforts have already been made to adapt the
tools to extreme environments. The communities behind
those projects have been contacted and are willing to
collaborate on new versions of their projects [8, 9, 10].
The process of modifying and validating the existing
tools will require the involvement of multiple
communities working together [11, 12]. The author
welcomes the involvement of everybody and is
dedicated to offer his work and his results to the people
that are interested in improving the design process for
habitats in extreme environments.
Please refer to the websites that the author uses for
communication. Type Thomas Lagarde in Researchgate,
Academia, Linkedin or ISSUU. You will have access to
the updated material, tutorials and a list of people and
communities (including the author) you can contact to
help improve this project.
The author hope that this project and this code will
help to connect the world of computational design and
extreme environment habitats.
The free flow of information is essential to the future
of space exploration.
Figure. 12. Latest iteration of the code as of the date of
publication of this paper.
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Acknowledgements
The SIMOC project team (Kai Staats, Project Lead,
Iurii Milovanov, Lead Server Developer, Ezio Melotti,
Lead Front-end Developer, Sheri Klug Boonstra,
Associate lead, Don Boonstra, Educational Developer,
Bryan Versteeg, Space Habitat Architect & 3D Artist
The Grasshopper community
The Ladybug community
The Openstudio community
The Energy+ community
All the people that dedicated their time and energy
to provide the community with new tools to improve
their workflow and their design process
My family (Isabelle that helped me to go forward)
and my colleagues in the industry
Appendix
Correct amount of PV panels, battery packs and volume
of pressurized environments for four crews and
RECLSS according to one of the SIMOC simulation
Possible Mars base, presented by the SIMOC website.
This base was designed by Bryan Versteeg Copyright ©
SIMOC
Integration of the code within Revit using
Rhino.Inside.Revit
The list of the agents in the SIMOC project
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Computational Design Sustainability A Conceptual Framework for
  • Sahar Soltani
  • Gabriela Dias Guimaraes
  • Pan Liao
  • Victor Calixto
  • Ning Gu
Sahar Soltani, Gabriela Dias Guimaraes, Pan Liao, Victor Calixto, Ning Gu, Computational Design Sustainability A Conceptual Framework for Built Environment Research, Conference: The 38th eCAADe Conference. eCAADe -Education and Research in Computer Aided Architectural Design in Europe
Lead Server Developer, Don Boonstra, Educational Developer, A SIMOC Technical Document
  • Kai Staats
  • Project Lead
  • Iurii Milovanov
Kai Staats, Project Lead, Iurii Milovanov, Lead Server Developer, Don Boonstra, Educational Developer, A SIMOC Technical Document, ASU, School of Earth & Space Exploration, Interplanetary Initiative Pilot Project