BAYESIAN IDENTIFICATION OF PYROLYSIS MODEL PARAMETERS FOR THERMAL PROTECTION MATERIALS USING AN ADAPTIVE GRADIENT-INFORMED SAMPLING ALGORITHM WITH APPLICATION TO A MARS ATMOSPHERIC ENTRY

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