Statistical Design of Thermal Protection System Using Physics-Informed Neural Network

TL;DR

This paper introduces a physics-informed neural network framework combined with sequential Monte Carlo sampling to efficiently perform uncertainty quantification and reliability analysis for thermal protection system design in space vehicles.

Contribution

It presents a novel approach integrating PINNs and SMC for rapid, accurate TPS parameter estimation under uncertainty, improving over traditional computational methods.

Findings

PINN accuracy comparable to traditional numerical solutions

Significant speed-up in TPS reliability analysis

Effective parameter distribution estimation satisfying reliability constraints

Abstract

Thermal protection systems (TPS) of space vehicles are designed computationally rather than experimentally. They are validated using ground experiments, but all aspects of the flight cannot be replicated on ground. This ground-to-flight mapping introduces uncertainties which need to be accounted for while designing any thermal protection system. Thus, precise computational models along with uncertainty quantification in the models are required to design the TPS. The focus of this study is to estimate the thermal material parameters of TPS based on the target reliability requirements using statistical methods. To perform uncertainty quantification (UQ) of a system, a simulated model of the system needs to be solved many times on statistical samples, increasing the computational time and cost of the overall process. A physics-informed neural network (PINN) model is used in the analysis instead of traditional physics based numerical solutions. The accuracy of PINN is comparable to that of the numerical solution. To find the parameter distribution, sampling of the parameter space is performed using Sequential Monte- Carlo (SMC) method. The sampling method is efficient as it generates samples based on the target distribution in parallel and it also generates diverse samples for proper UQ. Combining the use of both PINN predictive model and SMC sampling, the framework can approximate the parameter distributions that satisfy the TPS design reliability constraints. The framework achieved remarkable increases in the speed of performing the reliability analysis of the TPS. This reliability analysis can be used for design optimization of the TPS based on risk analysis along with other systems of the vehicle.