Physics-Informed Machine Learning Towards A Real-Time Spacecraft Thermal Simulator

TL;DR

This paper introduces a physics-informed machine learning approach for real-time spacecraft thermal modeling, combining neural networks with finite-difference methods to achieve fast, accurate, and generalizable thermal state predictions suitable for onboard use.

Contribution

It presents a hybrid PIML model that predicts optimal thermal mesh configurations, improving computational efficiency and generalization over purely data-driven models.

Findings

Hybrid PIML model reduces computation time by up to 1.7x.

Provides better generalization than neural network and coarse mesh models.

Achieves accurate thermal state predictions suitable for onboard real-time applications.

Abstract

Modeling thermal states for complex space missions, such as the surface exploration of airless bodies, requires high computation, whether used in ground-based analysis for spacecraft design or during onboard reasoning for autonomous operations. For example, a finite-element thermal model with hundreds of elements can take significant time to simulate, which makes it unsuitable for onboard reasoning during time-sensitive scenarios such as descent and landing, proximity operations, or in-space assembly. Further, the lack of fast and accurate thermal modeling drives thermal designs to be more conservative and leads to spacecraft with larger mass and higher power budgets. The emerging paradigm of physics-informed machine learning (PIML) presents a class of hybrid modeling architectures that address this challenge by combining simplified physics models with machine learning (ML) models resulting in models which maintain both interpretability and robustness. Such techniques enable designs with reduced mass and power through onboard thermal-state estimation and control and may lead to improved onboard handling of off-nominal states, including unplanned down-time. The PIML model or hybrid model presented here consists of a neural network which predicts reduced nodalizations (distribution and size of coarse mesh) given on-orbit thermal load conditions, and subsequently a (relatively coarse) finite-difference model operates on this mesh to predict thermal states. We compare the computational performance and accuracy of the hybrid model to a data-driven neural net model, and a high-fidelity finite-difference model of a prototype Earth-orbiting small spacecraft. The PIML based active nodalization approach provides significantly better generalization than the neural net model and coarse mesh model, while reducing computing cost by up to 1.7x compared to the high-fidelity model.