Inverse Estimation and Sensitivity Analysis of Mars 2020 Entry Aeroheating Environments

Abstract

The Mars Entry, Descent, and Landing Instrumentation 2 (MEDLI2) sensor suite collected heating and pressure data during entry of the Mars 2020 Perseverance rover into Mars’s atmosphere. MEDLI2 included temperature, heat flux, and pressure sensors on the heat shield and backshell. This paper covers inverse estimation of surface heating based on the MEDLI2 instrumented sensor plugs, which are a network of thermocouples embedded in thermal protection system plugs across the aeroshell. Monte Carlo analysis was conducted to assess the sensitivity of calculated surface heat rate and temperature to uncertainties in thermocouple depth and material properties, and a variance decomposition method was employed to understand the relative contributions of each uncertainty parameter. Performing this analysis using results from the inverse analysis tool FIAT_Opt was found to require an extremely high computation time; thus, machine learning models were trained and evaluated as a surrogate for FIAT_Opt. This paper demonstrates that machine learning models can be efficient, accurate alternatives to state-of-the-art inverse analysis tools like FIAT_Opt, especially for computationally expensive processes. Ensuing sensitivity analysis showed that the main drivers for overall uncertainty in the reconstructed heat flux and heat load were heat capacity and thermal conductivity on the backshell, and they were thermal conductivity and thermocouple depth on the heat shield.