An agent-based model for high-fidelity ECLSS and bioregenerative simulation.

Abstract

Mathematical models of complex systems can provide baseline assumptions about real-world systems. 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 (stochastic). 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. 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. In collaboration with the Biosphere 2, SIMOC was configured to approximate the non-linear functions of CO2 and biomass production in a real-world plant growth study conducted at the Biosphere 2. This publication sees the results of the first application of this novel approach to modeling a real-world plant study, where data generated by the SIMOC model is compared to data collected for the duration of the experiment, and then compared.