Integrating Mushrooms into an Agent-based Model of a Physico-chemical and Bioregenerative ECLSS

The Scalable, Interactive Model of an Off-World Community (SIMOC) is an agent-based model, a simulation through which semi-autonomous agents interact and whose behavior, when allowed to unfold over a specified time, may exhibit non-linear, dynamic, and probabilistic behavior. Data were derived from plant physiology studies [Wheeler, NASA 2008], the Baseline Values and Assumptions Document [NASA BVAD 2018], and ECLSS modeling validated by Paragon Space Development Corporation. SIMOC approximates photosynthetic activated radiation (PAR), H2O, O2, and CO2 parameters with non-linear plant growth functions derived from a barley fodder experiment conducted at the University of Arizona�s Biosphere 2 [Staats, ICES 2019]. To design a stable and integrated bioregenerative life support system for long-duration, off-world missions, several biological subsystems will need to be included in the initial trade study and modeling effort. One such proposed subsystem is mushrooms. The inclusion of mushrooms in the physical system provides the capability to begin the breakdown of the inedible biomass produced by plant-centric subsystems and provides a protein-rich food source for the crew. There are multiple advantages to mushrooms over other decomposers such as mealworms and insects, not the least of which is palatability across a broad set of cultures. Including mushrooms as the first step in the decomposition process opens the possibility to other uses for the inedible biomass like construction materials.
To further advance the utility of this model and to give foundation to the design of an integrated bioregenerative life support system, a mushroom agent was added into the SIMOC simulation engine for use in habitat simulations. The data used to program the mushroom agent were derived from several terrestrial studies [Citations] and multiple simulations including several different combinations of biological subsystems were run. Validation of the modeled data and systems is in progress and will be discussed in this publication.