Physics-constrained identification of graph-based thermal networks for spacecraft digital twins
This work addresses the challenge of building accurate and efficient thermal models for spacecraft digital twins from sparse measurements, a problem for aerospace engineers.
The paper introduces a physically-constrained calibration framework for Lumped Parameter Thermal Models (LPTMs) that reconstructs thermal dynamics from temperature measurements without prior parameter values, enforcing physical admissibility. Validated on synthetic data from high-fidelity simulations, the method accurately reproduces long-term temperature evolution and is robust to noise, enabling efficient models for spacecraft thermal digital twins.
Reconstructing a thermal model capable of efficiently simulating the behavior of a spacecraft from sparse and localized temperature measurements remains a challenging task. To address this, we introduce a physically-constrained calibration framework for Lumped Parameter Thermal Models (LPTMs), formulated as a trajectory-based inverse problem for graph dynamical systems. The model reconstructs thermal dynamics directly from temperature measurements and known inputs, without relying on a priori parameter values derived from material properties or geometric assumptions. Physical admissibility is enforced at the parameterization level: positivity of nodal coefficients and symmetry of conductive interactions are imposed by construction. This guarantees stable dynamics and restricts the identification problem to a physically meaningful parameter space, improving conditioning without the need of additional regularization. The identification problem is addressed through trajectory matching, ensuring stable rollout over extended time horizons. The methodology is validated on synthetic datasets generated from high-fidelity finite element simulations under progressively complex forcing conditions. The calibrated LPTMs accurately reproduce long-term temperature evolution and exhibit robustness to measurement noise. The proposed framework provides a systematic approach to the calibration of reduced-order thermal models by combining physical structure with data-driven identification. The numerical results show a favorable balance between accuracy and computational efficiency, making the models suitable for integration in spacecraft thermal Digital Twin applications.