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

Yongxiang Li,

Xiao Liu, Xiangdong Wang

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(3)

Published: April 16, 2025

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 material surface. Based on traditional physical models machine learning techniques, we systematically investigated mapping relationship between multiple parameters responses within carbonized layer pyrolysis ablative materials. By employing high-throughput modeling sure independence screening sparsity operator (SISSO) method for feature selection, first revealed that different layers dominated by distinct properties (e.g., density, conductivity, capacity, etc. ). The explicit relationships functioning features response curves associated with single-/double-layer structures well established. After key parameter based SISSO, further developed deep neural network surrogate model, capable accurately predicting entire process layers.

Language: Английский

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

Yongxiang Li,

Xiao Liu, Xiangdong Wang

et al.

Journal of Materials Informatics, Journal Year: 2025, Volume and Issue: 5(3)

Published: April 16, 2025

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 material surface. Based on traditional physical models machine learning techniques, we systematically investigated mapping relationship between multiple parameters responses within carbonized layer pyrolysis ablative materials. By employing high-throughput modeling sure independence screening sparsity operator (SISSO) method for feature selection, first revealed that different layers dominated by distinct properties (e.g., density, conductivity, capacity, etc. ). The explicit relationships functioning features response curves associated with single-/double-layer structures well established. After key parameter based SISSO, further developed deep neural network surrogate model, capable accurately predicting entire process layers.

Language: Английский

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