SIMOC paper presented at ICES 2019
The SIMOC team has presented one paper at the International Conference on Environmental Systems (ICES), July 7-11, 2019.
Visit the Publications page at SAM to learn more …
Beta product is running!
After more than two months focused effort on improving performance, stability, and the user interface, we have produced a launch beta product that installs on Linux, OSX, Windows, both personal computers and servers alike. The performance improvements are astounding, with 500 simulation time-steps running in just 50 seconds, or 5 hours per second. This means we can simulate roughly one full day on Mars in roughly 5 seconds. And this includes the front-end (web dashboard). If we run the server alone, it is even faster!
Stay tuned!
Dartmouth team takes 1st place in NASA BIG Idea Challenge; used SIMOC
Photos of 2019 BIG Idea Challenge Forum Awards Ceremony, plus winning teams. Marsboreal Greenhouse Design
NASA’s 2019 BIG Idea Challenge Winner Designs Best Planetary Greenhouse
Dartmouth was announced the winning team of the fourth annual Breakthrough, Innovative and Game-changing (BIG) Idea Challenge April 24 at NASA’s Langley Research Center in Hampton, Virginia. Massachusetts Institute of Technology University was awarded second place.
NASA’s BIG Idea Challenge engages universities in engineering design to develop space exploration concepts for the Moon to Mars. Earlier this year, five innovative designs for a human-scale Marsboreal greenhouse were selected to compete in the 2019 BIG Idea forum. Teams from Dartmouth College, Massachusetts Institute of Technology, University of California Davis, University of Colorado Boulder and the University of Michigan convened at Langley April 23 to present their greenhouse designs and prototypes. The ideas are derived from the Mars ice home designs, with potential aspects that could be demonstrated on the Moon.
Similar to the SIMOC research project at the Biosphere 2, it was determined that SIMOC could be used to generate non-linear functions for CO2 sequestration for each of the principal plants used in the Dartmouth team’s design, thereby enabling a data-driven model for the transpiration of the total plant ecology. The Dartmouth team worked tirelessly to conduct an extensive literature review and data extraction, from which SIMOC was programmed to generate a reciprocal dataset and function for each of the modeled plants.
Learn more at NASA.gov …
Update from the Development Team – April 15, 2019
Well into Phase III, the SIMOC development team is preparing for debut launches with partners National Geographic Society and the Arizona Science Center this summer.
We have completed the redesign and rebuild of the Configuration Wizard upon a highly flexible, scalable code foundation, with both a Novice and Advanced configuration. The dashboard is now being rebuilt, based upon the same, new code base that supports the Wizard.
The Agent-Based Model (ABM) is now highly programmable by means of the JSON file settings. Most important is introduction of non-linear (normal, log, sigmoid, exp) functions to describe plant growth and respiration cycles as close as possible to the real world.
With the close of May 2019 and the Phase III development cycle, SIMOC will enjoy a more robust back-end server, improved performance and stability.
Stay tuned!
SIMOC at Biosphere 2, research concluded
The research project is cleaned up, all equipment returned to boxes, shelves, and storage units. The barley has been dried for comparison to the original seeds, with an astounding result: 800g seeds, just 450g dry mass after fully mature plants are achieved.
SIMOC was configured to closely approximate the biomass accumulation, water retention and loss, and CO2 production for the duration of the live experiment. We consider this a success, as SIMOC is given its first non-linear functionality.
The draft paper is complete and submitted to ICES, the International Conference on Environmental Systems. Thank you University of Arizona for funding to make this possible, and the Biosphere 2 for hosting, supporting, and helping to guide this rapid-fire experiment.
SIMOC at Biosphere 2, experiment summary
Three weeks at the Biosphere 2, and the barley growth experiment is nearly complete. The biomass accumulation plateaued a few days prior with overall CO2 production beginning to fall.
Overall, the experiment went very well. The data harvested is solid, from start to finish. While at moments there were what felt like major hurdles to the desired steady-state environment surrounding the experiment, one comes to realize that with 15,000 to 70,000 data points (depending upon the instrument) over 12 days, a half hour of direct sunlight on a grow chamber, or a 15 minute power loss to the fans and subsequent CO2 build-up has no affect on an overall trend.
With three CO2 sensors, one at the outlet of each grow chamber and a third at the inlet to both chambers (as they are situated perpendicular to each other, sharing inlet air space, we found the daily fluctuations to match that of known CO2 correlations to temperature.
The data shows solid trends in all five of the parameters captured. While we came into the experiment principally interested in CO2, hoping to capture the photosynthetic draw-down of CO2 once the barley chlorophyll was activated, we learned that our seedbed was too thick, the underlying seeds remaining in a O2/CO2 respiration phase that kept the CO2 in the chambers higher than anticipated. Yet for the 30 minutes each day of direct sunlight (due to a gap in the shade cast by the LEO structure), we did see drastic reduction of CO2 in the data. We had considered full sunlight in this experiment, to invoke a higher CO2 draw-down, but knew it would be difficult to model the real-world weather (full sun, partial or full cloud cover, even a snow storm as occurred). What’s more, in a lunar or martian habitat, it is anticipated that all greenhouses will be located in lava tubes or buried beneath regolith to provide radiation protection for both the plants and humans that tend to them.
While we had originally been concerned for of our ability to capture the increase in biomass (plant structure + water retained), in fact, we were quite successful. The digital scales with 0.1g sensitivity proved more than ample to provide this data. In fact, through the careful reduction of the data we are able to discern the amount of water lost to evaporation and plant respiration (combined). Now, we are drying the total, final plant biomass (over 5Kg, having started with just 800g) to learn how much was water retained and how much was true plant structure built from carbon intake, as no nutrients were added at any point in time.
As often happens with scientific experiments, we learn something different than the anticipated outcome, and know better how to conduct the experiment the next time through.
SpaceTalk, The Next Generation
In SpaceTalk, The Next Generation, February 2019, the magazine for the all International Space University Alumni, SIMOC is featured! In this 7-page spread, the story of how SIMOC got started through the first two phases of development is told. Read the full publication at Calameo.com
Arizona Science Center set to host world’s first SIMOC learning center
The Arizona Science Center, located at the heart of the Phoenix metropolitan district, today agreed to host the world’s first SIMOC learning center. Their “Blue Team” will host live, iterative and interactive learning sessions in which visitors learn about the challenges of living off of planet Earth.
In a conversational format, visitors will be asked to consider which of two dozen plants would they bring to grow in a human habitat on the Moon or Mars in order to support carbon dioxide reduction, oxygen production, and nutritious foods. Engaged citizen scientists will have to find a balance between those plants that yield a high volume of oxygen yet may require a long time from planting to harvest, or simply not taste very good without extensive preparation, versus those you can eat almost immediately after removing from the soil or hydroponics grow chamber.
By selecting plants in the SIMOC model, which is built upon NASA plant study data, we see the outcome of several weeks, even months of bioregenerative life support systems in a matter of minutes.
The first live demo and training discussion is slated for March 6, 2019.
Stay tuned!
SIMOC at Biosphere 2, a research proposal
The University of Arizona and Biosphere 2 have agreed to support a research project, hosted on-site at Biosphere 2, Oracle, Arizona, in order to monitor and record light, temperature, relative humidity, carbon dioxide, and biomass. The goal? —capture the non-linear functions inherent in plant growth and provide a ground-truth data set for the SIMOC agent-based model.
The abstract reads as follows …
Mathematical models of complex systems can provide baseline assumptions about the real-world. While Environmental Control and Life Support System (ECLSS) can be modeled as linear, static, and deterministic, deployed systems do not often behave as modeled for the full duration of a mission. Models of bioregenerative systems are considerably more complex and readily identified as probabilistic. Non-linear models are typically built upon differential equations and/or computer software applications designed specifically for simulation of particular real-world systems.
An agent-based model (ABM) employs the actions and interactions of individual and collective, autonomous agents such that their behavior, when allowed to unfold over a specified time, may exhibit non-linear, dynamic, and probabilistic behavior. Used extensively in finance, biology, ecology, and social sciences ABMs are a proven alternative to more traditional systems of modeling.
SIMOC (a scalable, interactive model of an off-world community) is a Python-based ABM developed as an Interplanetary Initiative pilot project at Arizona State University. In collaboration with the Biosphere 2, SIMOC is employed to model a semi-closed BLSS built upon the NASA funded Prototype Lunar Greenhouse.
SIMOC’s web-based agent library editor enables rapid design of new agents to match real-world systems. The configuration wizard and interactive dashboard provides a graphical interface with ABM readouts and a full command-line, back-end data capture for analytical and machine learning post processing.
This publication sees the results of the first application of this novel approach to modeling a real-world
BLSS in which continuous temperature, relative humidity, luminosity, and carbon dioxide data are
collected for the full duration of the experiment and then compared to the output of the ABM that has
generated the same four parameters for the same duration.