Bayesian Identification of Pyrolysis Model Parameters for Thermal Protection Materials Using an Adaptive Gradient-Informed Sampling Algorithm With Application to a Mars Atmospheric Entry

Abstract

For space missions involving atmospheric entry, a thermal protection system is essential to shield the spacecraft and its payload from the severe aerothermal loads. Carbon/phenolic composite materials have gained renewed interest to serve as ablative thermal protection materials (TPMs). New experimental data relevant to the pyrolytic decomposition of the phenolic resin used in such carbon/phenolic composite TPMs have recently been published in the literature. In this paper, we infer from these new experimental data an uncertainty-quantified pyrolysis model. We adopt a Bayesian probabilistic approach to account for uncertainties in the model identification. We use an approximate likelihood function involving a weighted distance between the model predictions and the time-dependent experimental data. To sample from the posterior, we use a gradient-informed Markov chain Monte Carlo method, namely, a method based on an Ito stochastic differential equation, with an adaptive selection of the numerical parameters. To select the decomposition mechanisms to be represented in the pyrolysis model, we proceed by progressively increasing the complexity of the pyrolysis model until a satisfactory fit to the data is ultimately obtained. The pyrolysis model thus obtained involves six reactions and has 48 parameters. We demonstrate the use of the identified pyrolysis model in a numerical simulation of heat-shield surface recession in a Martian entry.