A Predictive Model for the Production Rates of a Bioregenerative Life Support System

Abstract

Future long-term human space flight will require systems that are reliable, sustainable, and can continue to function for the entire length of the mission. Included in these possibilities are a relatively short two-year expedition to Mars as well as the indefinite colonization of another celestial body. Life support presents a significant challenge to this concept as many of the systems currently in use would require bringing a prohibitively heavy and bulky amount of supplies and consumables. An additional challenge is what to do with all the refuse from humans and other systems within the habitat. An innovative solution that addresses both of these problems is the use of bioregenerative life support (BLS). This type of life support utilizes the evolutionarily refined abilities of biological organisms to produce oxygen and sequester carbon dioxide through photosynthesis, purify water through transpiration, and produce edible biomass and calories for human consumption. In addition to being reliable, these systems operate at ambient conditions which reduces risk, and also provide psychological benefits to the crew. A successful BLS system will likely include many subsystems of which a higher plants production chamber will be one. The inclusion of biology as part of a life support system introduces complexity and mathematical models that can accurately predict the production rates of these systems is necessary. One such model that describes the production of a higher plants growth system is the Modified Energy Cascade Model (MEC) which was developed by James Cavazzoni in the early 2000’s. Around this model, a wrapper was developed to allow a user or system designer to input parameters for a hypothetical design and receive graphics depicting the production rates of said system over the entire course of the proposed mission for two different harvest strategies. This allows the user to accurately predict the production of any design and optimize to find the solution that best fits the needs of the mission. Four different BLS system example analogues were analyzed to show this utility and to allow for the further discussion of the design process surrounding such a system. This tool could also be modified to allow the design to identify deficiencies in the astronauts’ diets and show the effect of variable environmental conditions. In its current form, this tool would have the most potential benefit during the system and concept of operations (ConOps) design phases of a life support system for a long duration human space flight mission.