SIMOC: An agent-based model of hybrid physico-chemical and bioregenerative life support and food production for long-duration missions and other-world habitation.

Authors: Kai Staats and Ezio Melotti

Scalable, Interactive Model of an Off-World Community (SIMOC) is an agent-based model and web interface to a human habitat simulator built on NASA human factors and plant physiology data. Funded by Arizona State University, University of Arizona, and National Geographic, a SIMOC users design a habitat by selecting various combinations of mechanical (physico-chemical) and plant (bioregenerative) systems, greenhouse and food cultivars, energy generation and storage; astronauts, rations, and mission duration. The dashboard provides numerical and graphical display of various atmosphere components, water, and power levels; food cultivar growth, harvest, and consumption; and the overall health of the human inhabitants. All data can be downloaded and analyzed, and simulations stored and shared by other users.

In its current offering, SIMOC simulates hour-by-hour, up to two years of a closed ecosystem. At the core of this Python engine is an agent-based model whose functions are not bound to any planetary location or time frame. With relatively minor adjustments to the agent definitions file, SIMOC could be made to simulate much longer duration missions, extending to multi-generational interstellar voyages.

Objective – To discover the minimum complexity required by a hybrid physico-chemical/bioregenerative life support system for long-duration missions and other-world habitation.

Methods – Development of a research-grade, agent-based model built upon four decades human factors and plant physiology data at NASA, Universities, and Paragon SDC.

Results – The results depend on the input parameters of the given habitat design and subsequent simulation.

Conclusions – Users learn the challenge in finding a balance between rations and harvested food, mechanical and bioregenerative air recycling, power generation, storage, and consumption. Relatively simple, linear functions form non-linear interactions that over time make long-duration mission design quite challenging.