Effect of material properties on the thermal responses of the carbonization and pyrolysis layers of polymer matrix composites for charring-ablators

Abstract

Ablative materials, a special type of thermal protection material, are widely used in extremely high-temperature environments such as hypersonic vehicles and re-entry capsules. They effectively mitigate heat conduction to the interior through ablation at the material surface. Based on traditional physical models and machine learning techniques, we systematically investigated the mapping relationship between multiple material parameters and thermal responses within the carbonized layer and pyrolysis layer of ablative materials. By employing high-throughput physical modeling and the sure independence screening and sparsity operator (SISSO) method for feature selection, we first revealed that the thermal responses of different layers are dominated by distinct material properties (e.g., density, thermal conductivity, heat capacity, etc.). The explicit relationships between the functioning material parameters and the features of the thermal response curves associated with single-/double-layer structures are well established. After the key parameter screening based on SISSO, we further developed a deep neural network surrogate model, capable of accurately predicting the entire thermal response process within the carbonized and pyrolysis layers.

Keywords

Thermal response / ablation / data-driven / symbolic regression / deep neural network

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