Research & Development

We did it! Today we provided key partners with access to a beta release of SIMOC Phase IIIa. We now await their feedback, notes, and bugs in order to help us wrap the past ten months of development with the close of the year. Then we move into Phase IIIb in 2020, with the goal of improving realworld fidelity, the user Dashboard experience, and reliability over scalability across a cloud service platform. Come mid 2020, we intend to launch SIMOC at the National Geographic Educational Resource Library.

The SIMOC developers are (as I type) hard at work to wrap up loose ends and tighten the details for a soft-release to National Geographic on Monday. The Nat Geo review team will provide feedback for their experience of SIMOC and the associated educational curriculum. The SIMOC development team will then update and improve SIMOC through the month of October. This marks two and a half years in development, a team of a dozen developers in total with countless thousands of hours, and the support of individuals at NASA Johnson Space Center, NASA Kennedy, Arizona State University’s School of Earth & Space Exploration and Interplanetary Initiative, Paragon Space Development Corporation, and many other individuals from both private and public sector institutions.

We are excited for the day in which we can share SIMOC with you!

The months of July and August have seen a steady improvement in the SIMOC Configuration Wizard, Dashboard, and back-end server. With new team member and developer Ezio Melotti we have focused on ease of deployment and the user interface while Iurii and Sinead continued to fine tune back-end efficiency. In a system as complex as SIMOC, we have had to rethink and retool a few of our systems, including the means by which we request and then deliver data to the front-end. In so doing, we have doubled the performance and stabilized long duration runs. SIMOC is now a proper research tool, fully function from the command line or with web interface. The simulation of a modest habitat on Mars can be completed in roughly twenty minutes on a laptop, less on a more substantial system.

Software development does not always move from exciting milestone to major update.

Sometimes, as with sailing, you find the wind to be steady but not terribly strong, the direction consistent and the course well known. Each day you welcome the rising sun, and each night check your progress against the stars.

Steady as she goes, SIMOC is getting closer to a new home.

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!

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!

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.

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.

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.