Fast Probabilistic Uncertainty Quantification and Sensitivity Analysis of a Mars Life Support System Model

Mars life support system models consist of numerous mission-critical, interrelated, and scenario-specific parameters. The large size and involved nature of these models make them computationally expensive, with parameters that are subject to several sources of uncertainty. Accurately characterizing these uncertainties and their impact on overall model predictions is crucial for decision-support and mission optimization. This paper focuses on uncertainty quantification of a model of a space crop cultivation system, which is one of several systems that are required on a long-duration manned Mars mission. The model performs constrained optimization of the equivalent system mass (ESM) metric, which augments shipped mass costs with those of pressurized volume, demanded power, thermal control, and needed crew time. This paper uses surrogate modeling for fast quantification of the effect of probabilistic uncertainty in mission-critical parameters of semi-empirical equations that describe crop growth and equipment operation. This work shows sparse polynomial chaos-Kriging (PCK) yields a computationally cheap-to-evaluate surrogate for the minimum ESM that accounts for probabilistic uncertainty in 86 model parameters. This surrogate model accelerates a global sensitivity analysis that elucidates which crop growth and equipment operation parameters are critical to mission outcome variability. The PCK surrogate model realizes a 100-fold computational speed gain in the estimation of the probability distribution of the minimum ESM. Copyright (C) 2020 The Authors.