Stochastic Reconstruction of Thermal Protection Material Properties from Arcjet Experiments

This work revisits the capability of extracting material properties from arcjet experiments while acknowledging and modeling uncertainty using cutting-edge methods and algorithms. To achieve this, efficient simulation of the experiments is crucial. Specifically, we focus on simulating the temporal evolution of in-depth temperatures for comparison with thermocouple data. In this work, we introduce a comprehensive Bayesian analysis by developing an ideal surrogate model based on the Karhunen–Loève expansion capable of approximating the functional output, such as temperature versus time. This approach offers dual benefits. First, it relies solely on a dataset of model solutions to address uncertainty quantification (UQ) problems, including forward analysis, sensitivity analysis, and inverse analysis. Second, it allows us to maintain the necessary computational complexity of the model solver that best reflects the experimental data. Consequently, this methodology constitutes a pivotal advancement for facilitating more intricate Bayesian inference analyses in the future. This work presents the theoretical framework, initial findings to assess the feasibility of the proposed surrogate model, and a complete Bayesian inference analysis to refine material properties based on arcjet experiments, leveraging state-of-the-art computational tools.