Postcards from Mars

The Agent-Based Model (ABM) at the heart of SIMOC has been rewritten from scratch for consistency and speed. Over the years, many individuals had contributed to the SIMOC ABM, each bringing their own programming skills and vision for how SIMOC could develop into the future. Now, as we take SIMOC open-source, it’s a good time to remove inconsistencies and establish norms going forward.

Some key features of the updated ABM are:

• A single agent schema is used throughout the entire application: defining the agent, defining the configuration, initializing the ABM, exporting data and saving the ABM. This will greatly reduce the learning curve for new-comers.
• All agent parameters are stored as either `properties` (static, e.g. ‘harvest_ratio’) or `attributes` (dynamic, e.g. ‘daily_growth_factor’). Besides streamlining the code, this will make the SIMOC web app compatible with custom agents out-of-the-box.
• Unit testing and documentation are incorporated from the start.

We’re seeing up to 85% speedup for large simulations, meaning it’s much faster for users and the demand on our servers is lower.

Altogether, the new ABM is a tremendous upgrade for our users, and puts us on a better footing for long-term growth and collaboration.

The SIMOC development team has just a few, finishing touches to apply to the code and documentations before providing both the back-end server (Agent-Based Model, or ABM) and front-end client (Dashboard) available via the GPL3 and Community Commons licenses, respectively.

We are eager to take six years of development and provide it to you, for free, to gain your input, ideas, and ultimately improved code.

Stay tuned!

The SIMOC developer team is preparing to open source the entire platform, from back-end server to front-end web client so that developers, educators, and citizen scientists too can benefit from this unique, powerful agent-based model and simulation around the world, for free.

Stay tuned!

by Grant Hawkins, lead developer for SIMOC B2 at Over the Sun, LLC

The final step of integrating Biosphere 2 into SIMOC was validating the outputs. From the beginning, we’ve focused on telling the story of oxygen depletion during Mission 1, as described in the paper Oxygen Loss in Biosphere 2, published in 1994. This paper describes the overall O2 and CO2 behavior within B2 for the first ~18 months of Mission 1, as well as the rates of O2 and CO2 consumption and production for key agents such as the concrete and soil.

First, we compared overall levels of O2 and CO2 throughout the mission:

We also compared net effects of processes specified in the paper:

These figures help us identify where the model is reliable, and where it needs to be improved. Below is the ‘Discussion’ section of a paper we’ve written on this process:

The results of the simulation are directionally accurate, but with a low degree of precision. Relatively few measurements of plant productivity are available from the B2 experiments, and SIMOC’s other validation reference is a highly dissimilar experiment. To improve precision of the plant model, we make the following recommendations:

• Include fruit trees and other perennial plants in addition to vegetables. These made up a large part of the B2 crew’s actual diet, and follow a different growth cycle.
• Model the impact of specific pests and parasites. As records allow, match periods of low productivity for specific plants with their causes – most often fungi or mites. In the case of corn, no edible food was produced in Mission 1a and 1b because it is wind-pollinated, and there was no wind inside the structure. This and other species-specific behaviors could increase the accuracy as well as the educational potential of SIMOC.
• Include some metric of human wellness besides simple survival. Some of the difference in plant productivity between Missions 1 and 2 can be attributed to crew energy levels and morale. Taking into account nutrition, workload, variety of diet, etc. and giving an overall indication of crew’s well-being will be useful in optimizing these systems, and for making the simulations more engaging.

Despite this imprecision, the measured system-level behaviors of the B2 missions are observable in the simulation. By making adjustments the base configurations, users can also observe the following phenomena:

• Plant growth can be increased by adding supplemental lighting (at the cost of increased electric consumption), or decreased by reducing CO2 setpoint to <700ppm.
• Crop layouts can be adjusted to add higher-yield crops, or crops which transpire and photosynthesize at different rates, affecting overall gas balances.
• The starting concrete carbonation rate can be adjusted, which affects the rate at which it consumes CO2. This is also affected by the ambient CO2 levels, with lower rates of carbonation when CO2 levels are lower.
• Adjusting the areas of each biome impacts (1) the size of the air sink, and therefore the overall changes in concentrations, (2) O2 and CO2 exchanges, as each biome has a different rate.

In completion of a seven months development endeavor, a simulation of the original and second missions at Biosphere 2 are now incorporated into SIMOC and available for free from the National Geographic Society’s educational web portal.

The intent of SIMOC is to provide a model of Biological Life Support Systems for human space exploration. SIMOC was originally built around an ideal plant growth scenario (NASA CELSS growth chamber experiments and the NASA BVAD document), and was calibrated so that the simulation reproduced similar outcomes as given in published NASA experiments. The reality of a Moon or Mars habitat will be less than ideal, where the original Biosphere 2 missions are likely a closer approximation to how things might actually play out.

SIMOC B2 adds new degrees of freedom to the plant model (light response, planting density, crop management) as well as other new agents (concrete, biomes) such that the model reproduces experimental data from both NASA CELSS and the original Biosphere 2 missions with a single model. We attribute the large differences in plant productivity (up to 10x) and validate the system-level outputs against experimental B2 data.

By calibrating the model to these two extremes, we’re granting researchers, students and citizen scientists greater insight into the historical data from both and an opportunity to evaluate and compare a wider range of different BLSS scenarios with higher accuracy.

by Grant Hawkins, lead developer for SIMOC B2 at Over the Sun, LLC

The final step of integrating Biosphere 2 into SIMOC was building the configuration files, which specify how many of each agent to include in a simulation, their starting resource balances, etc. Our aim was to replicate the real-life Biosphere 2 experiments: Mission 1 (September 26, 1991 to September 26, 1993) and Mission 2 (March 6, 1994 to September 7, 1994).

There was a major non-linearity in Mission 1 of course, which was the extra oxygen added to the habitat, beginning on January 12, 1993, 475 days into Mission 1. Some other changes were made throughout the experiment, such as adjusting the planting areas of different crops to maximize calorie-production and CO2-sequestration. To account for this, we split Mission 1 into two configurations: Mission 1a for before the O2 was added, and Mission 1b for after O2 was added. The configuration of 1b starts with the final atmosphere and concrete carbonation of Mission 1a, and includes an O2 resupply system and modified greenhouse layout.

The feature of significance for Mission 2 was improved plant productivity. We spoke with Tilak Mahato, one of the crew member on Mission 2 usually credited with improving output, and currently a researcher of Controlled Environment Agriculture at the University of Arizona. He described several specific practices that improved output:

• Removing pests immediately. Because pest populations grow so quickly, catching and remediating an infestation early has an outsized impact.
• Taking care not spread pests, fungi or diseases via contaminated tools.
  Washing diseased leaves with soap and water.
• Protect seedling growing areas from roaches and other pests.
• Pollinating plants by hand. During Mission 1, the entire corn crop had failed to produce food because there was no wind to spread pollen from the (male) tassels to the (female) silk. Other plants’ pollen had been washed away by overhead irrigation.

There was no ‘primary’ factor, according to Tilak, to which the improvements in productivity could be attributed. For this reason, we added a simple field to SIMOC, ‘Improved Crop Management’, which increases productivity of the plants by 50%, and describe the specific processes above in the SIMOC web app.

The result of these 3 configurations is encouraging so far!