Probabilistic Modeling of Aerothermal and Thermal Protection Material Response Uncertainties

Abstract

A Monte-Carlo-based methodology is presented for physics-based probabilistic uncertainty analysis of aerothermodynamics and thermal protection system (TPS) material response modeling for aerocapture or direct entry missions. The objective of the methodology is to identify and quantify the most important sources of uncertainty in aeroheating and the resulting thermal protection material selection, design, and sizing based on inaccuracies in current knowledge of the parametric input modeling parameters. The resulting parametric modeling uncertainty would be combined with other uncertainty sources to determine the final aeroheating and TPS response modeling uncertainty for a given application, which can then be used to define appropriate margins and factors of safety that should be applied to the TPS. These techniques facilitate a risk-based probabilistic design approach, whereby the thermal protection system can be designed to a desired risk tolerance, and any remaining risk can be effectively compared to that of other subsystems via a system-level risk mitigation analysis. Modeling sensitivities, which are a byproduct of the uncertainty analysis, can be used to rank input uncertainty drivers. Key input uncertainties can then be prioritized and targeted for further analysis or testing. The strengths and limitations of this technique are discussed. Sample results are presented for two cases: Titan aerocapture and Mars Pathfinder. These cases demonstrate the utility of the methodology to quantify the uncertainty levels, rank sources of input uncertainty, and assist in the identification of structural uncertainties in the models employed.

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