Physics-Infused Reduced-Order Modeling for Analysis of Multi-Layered Hypersonic Thermal Protection Systems

TL;DR

This paper introduces a physics-infused reduced-order model (PIROM) that accurately predicts transient thermal behavior in multi-layered hypersonic thermal protection systems, outperforming purely data-driven models in accuracy and efficiency.

Contribution

The paper develops a novel PIROM framework combining physics-based and data-driven approaches for thermal modeling of TPS, with superior accuracy and speed compared to existing non-intrusive ROMs.

Findings

PIROM achieves errors below 1% across various conditions.

PIROM is two orders of magnitude faster than full-order models.

PIROM outperforms OpInf and NODE in accuracy and robustness.

Abstract

This work presents a physics-infused reduced-order modeling (PIROM) framework for efficient and accurate prediction of transient thermal behavior in multi-layered hypersonic thermal protection systems (TPS). The PIROM architecture integrates a reduced-physics backbone, based on the lumped-capacitance model (LCM), with data-driven correction dynamics formulated via a coarse-graining approach rooted in the Mori-Zwanzig formalism. While the LCM captures the dominant heat transfer mechanisms, the correction terms compensate for residual dynamics arising from higher-order non-linear interactions and heterogeneities across material layers. The proposed PIROM is benchmarked against two non-intrusive reduced-order models (ROMs): Operator Inference (OpInf) and Neural Ordinary Differential Equations (NODE). The PIROM consistently achieves errors below 1% for a wide range of extrapolative settings involving time- and space-dependent boundary conditions and temperature-varying material property perturbations. In contrast, OpInf exhibits moderate degradation, and NODE suffers substantial loss in accuracy due to its lack of embedded physics. Despite higher training costs, PIROM delivers online evaluations of two orders of magnitude faster than the full-order model. These results demonstrate that PIROM effectively reconciles the trade-offs between accuracy, generalizability, and efficiency, providing a robust framework for thermal modeling of TPS under diverse operating conditions.