Research & Development

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.

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!

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

Storytelling is a core part of SIMOC, especially when it comes to the educational components. As we integrate Biosphere 2 into SIMOC, we’re focused on telling one story in particular: how the soil and concrete conspired to crash O2 levels during Mission 1.

This story was first told in a research paper from January 1994, Oxygen Loss in Biosphere 2. Over the course of Mission 1, the O2 level in the habitat fell quickly, but the CO2 levels didn’t show the expected corresponding rise. The obvious hypothesis is that there was an unaccounted-for O2 consumer. The paper showed that in fact two separate processes were more active than was expected: soil respiration, which consumed O2 and produced CO2, and concrete carbonation, which consumed CO2. The combined effect of both of these was the observed ‘O2 consumer’. We created 5 new agents in SIMOC – 1 concrete and 4 soil-containing biomes – and calibrated them to the measurements in the research paper.

The concrete agent models the process of carbonation. Each step of the simulation, carbonation occurs based on a diffusion rate and a saturation point. The saturation point is heavily dependent on the current CO2 concentration in the atmosphere, however, more than 10x higher under extreme conditions like those at Biosphere 2, which accounted for the unexpected difference in total uptake. We corresponded by email with Bill Dempster, an author of the paper above, about the problem and our approach to it.

The biomes model the combined effect of the soil and ground vegetation in the rainforest, savannah, desert and intensive agriculture biome (farm). A paper from 1999 measured the net productivity of the rainforest and desert biomes, and the Oxygen Loss in Biosphere 2 paper gives the habitat-wide soil respiration.

If all goes according to plan, the net effect of the humans, plants, concrete and biomes will be the drop in O2 observed in real life. This then becomes a story we can tell in SIMOC: the oxygen levels fall much faster than carbon dioxide rises, and the user can discover that it’s being ‘caused’ by the concrete and biomes.

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

In mid-2022, our team took on the challenge of modeling Biosphere 2 (B2) in SIMOC. Beyond simply describing the outcomes and making sure that they match the published data, we also had to make sure that — The system and sub-systems responded correctly to changes in the configuration. When a user changes the area of sweet potatoes or the CO2 scrubber activation threshold, other agents are responding appropriately to those changes.

The sub-systems remain accurate on the original SIMOC validation data, the NASA CELSS growth-chamber experiments. There, plants were grown in highly-controlled environments in order to maximize their food productivity. For certain plants, the production rate in kg/m2-day was as much as 10x greater at CELSS than in Biosphere 2.

Light was found to be the biggest contributing factor to the differences in plant productivity. At CELSS, plants received constant-output electric lighting at an optimal level for an optimal number of hours per day. These ranged from 1.4 Mol/m2-h for 12 hours/day for rice, to 3.53 Mol/m2-h for 20 hours/day for wheat. At B2, the plants received whatever sunlight passed through the B2 windows and structural frame. Our approach was to use the CELSS data as the baseline for SIMOC plant consumption and production, and

The first step toward implementing SIMOC-B2 was to add a ‘light-response’ mechanism to the SIMOC plant agent. We added a new variable, ‘par_factor’, which scales the rate of biomass accumulation to the species-specific, hourly light requirement. The other functions of the plant (photosynthesis) are scaled to its total accumulated biomass, so the light response will be applied to those exchanges as well.

So this was a big step forward for the accuracy and flexibility of our plant growth model, but that wasn’t all. Some of the deeper changes to how exchange values are calculated led to a major reduction in memory footprint – more than 80% for simulations with more than one plant – which is great news for our cloud computing budget 🙂

The SIMOC development team has for the past six months been hard at work in developing two new versions of SIMOC: SIMOC B2 and SIMOC Live. SIMOC B2 is a new kind of simulation, a model of the first and second missions at Biosphere 2 in order to present citizen scientists with the tools to understand the challenges faced by the world’s largest and longest running human-in-the-loop bioregenerative experiment. SIMOC Live enables the real-time monitoring and capture of data from sensor arrays, such as those used to monitor carbon dioxide or oxygen levels, and other air quality measures.

We apologize for the lack of updates to this forum, but promise to catch-up (and back date) several stories soon!

With more than six months development effort by the core SIMOC development team Ezio, Grant, and Kai, working in parallel to the Arizona State University Computer Science Capstone team (Meridith, Greg, Ryan, David, and Ian) who are nearly complete with their effort to introduce live sensors feeds into a new SIMOC back-end (server) and modified front-end (web) interface for use at SAM and in classrooms worldwide!

The Phase V development of SIMOC (July 2021 – May 2022) introduces staggered crop rotation of food cultivars, a foundation for tracking food nutrition, advanced life cycle plant growth functions, the addition of random variation and total system entropy, and improved server-side management of agent definitions and data to provide a more dynamic, rich experience with even greater potential for non-linear outcomes and simulation of the real world.

With construction of the SAM Mars habitat analog at the iconic Biosphere 2, SIMOC now includes SAM Presets, a configuration of the SIMOC simulation that closely approximates the real-world SAM habitat. And with the newly introduced 3D objects during Configuration and on the Dashboard, the user can now visualize the habitat before and during the simulation, including SAM.

• Learn about all the improvements and updates at Phase V.
• First Time Users: Enjoy a quick introduction before you dive.
• Advanced Users: Learn how the SIMOC ABM functions, and how to analyze your data.
• If you are a returning user eager to try the new version, launch SIMOC now!

Grant Hawkins, SIMOC developer offers this insight to his research into the effect of elevated CO2 levels on plant growth, and how he is modifying SIMOC to capture these response systems.

Modeling plant responses to elevated CO2
One of the research papers being submitted to ICES this year looks at the impact of elevated CO2 on plant growth and bioregeneration. Scientists have been experimenting with eCO2 since the 1970s, so there is ample literature on what the impact is and how to model. Our goal is to use SIMOC to show the system-level dynamics of these effects in an enclosed habitat like SAM.

Grant dug into the literature and augmented our SIMOC plant agents to vary their currency exchanges based on the co2 levels in the greenhouse. Below are some charts from his initial testing and implementation. The next steps are to investigate the system-level impacts, and then compile the results into a paper for ICES. Stay tuned!

Plant Nutrition
Nutrition is a critical aspect of bioregenerative system design, and one we’ve long meant to incorporate into SIMOC. And now, as part of the research for our ICES paper on plant responses to CO2, it’s finally been
added!

Plant descriptions in SIMOC include several currency exchanges (inputs and outputs), a lifetime, and an edible/inedible ratio. We also have currency descriptions, which specify currency classes (‘atmosphere’, ‘food’), labels, etc. For food currencies, these now include nutrition fields: kcal, water, protein, carbohydrates and fat (per 1kg).

Over a plant’s lifetime, it accumulates biomass based on its natural growth cycle (sigmoidal), augmented by resource availability and ambient co2. When it’s ready to harvest, the total accumulated biomass is converted to waste biomass and food, based on the edible/inedible ratio. The food then goes to ‘food storage’, which the human agents consume immediately, grateful to be eating something besides rations.

Nutrition will be incorporated into our Plant CO2 Response paper for ICES as one of two primary ‘plant utility metrics’. Stay tuned for more!

The initial effort is underway to introduce varied plant growth performance based on varied input levels of critical currencies, starting with carbon dioxide (CO2). Grant Hawkins of the SIMOC development team is simultaneously preparing a paper for the International Conference on Environmental Systems (ICES 2022) as he develops a deeper understanding of these known functions, as assembled through a literature review.

Today the ASU Computer Science Capstone team conducted a live demo of a sensor generating data and delivering it into the SIMOC front-end dashboard. This marks an exciting point in development as we move to provide SAM with a rich, dynamic sensor array for real-time monitoring of the breathable air, capture of the data for local observation, and display to the world via the National Geographic hosted SIMOC interface.

Notes:
– interpolated every second
– 24 seconds load and cache
– demonstrated an increase to 14,000 ppm with Greg’s breathing on the sensor
– It works! and looks great!

The SIMOC development team lead by core Python developer Ezio Melotti and Grant Hawkins, and Meridith, Greg, Ryan, David, and Ian of the Arizona State University Computer Science Capstone team have made significant strides in SIMOC development this past six months.

June-July
A major re-write of the Advanced Configuration Editor (ACE) now matches the current agent descriptions and capabilities, enabling local-install users to modify and download the configuration file, which can then be used on local installations to run custom simulations.

Other updates include:
* Jun 21, FE/INFRA: added linting check to the front-end
* Jun 23, BE/INFRA: added a basic testing framework
* Jul 1-9, BE: removed the ACE
* Jul 13, BE/SAM: added SAM agents
* Jul 26, FE: new confirmation popup
* Jul 28, FE: improved plant validation
* Jul 29, FE/INFRA: update docker image and dependencies

August-September
A User feedback survey is now included, made available from the Main menu and prompted when
exiting a simulation for the first time. This enables the SIMOC development team and sponsor National Geographic to receive feedback from users during run-time engagement.

The new 3D view now matches the user-defined habitat configuration, visible on both the Configuration and Dashboard screens.

Other updates include:
* Aug 4, FE: new modal popups
* Aug 9, FE: added the survey
* Sep 8, BE: added documentation with Sphinx
* Sep 11, FE: update to VueJS 3, bumped FE version to 1.0.0
* Sep 13, FE: added the 3D view
* Sep 22, BE/INFRA: removed DockerHub dependency
* Sep 23, BE/INFRA: removed staging branch, misc infra updates
* Sep 27, FE: improved the 3D view, fixed bugs, added rocket
* Sep 28, FE: added the simoc-web.py script
* Sep 29, BE/ABM: added a CO2 tank and makeup valve agents

October-November
The holy grail of software development, SIMOC now incorporates Pytest for unit and integration testing for the SIMOC configuration files, model and agents. Finally!

Other updates include:
* Oct 5, BE/ABM: refactored connections and added the agent_conn.json file
* Oct 10, FE/INFRA: disabled artifact creation on GitHub
* Oct 12, BE/ABM: all agents are now storages too
* Oct 18, BE/INFRA: added custom SSL certs for NGS
* Oct 20, BE/ABM: atmo storages included in cq/gh, added atmo_equalizer agent
* Oct 26, BE/INFRA: added a separate DB for testing and more BE tests
* Oct 27, BE/ABM: added the currency_desc.json file
* Oct 29, BE/INFRA: added an adminer container for DB inspection
* Nov 2, BE/ABM: created the data_files dir and moved the JSON files there
* Nov 2, BE/ABM: currency classes, sold_fertilizer replaced sold_n/p/k, more tests
* Nov 13, BE/ABM: food/ration prioritization