Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks

TL;DR

This paper introduces a physics-informed neural network approach for temperature field inversion in heat-source systems, improving accuracy by considering physics constraints and optimizing observation positions to reduce noise impact.

Contribution

The paper presents a novel PINN-based method for temperature field inversion and a position selection strategy using condition number to enhance robustness against noise.

Findings

PINN-TFI significantly improves temperature prediction accuracy.

CMCN-PSO effectively identifies optimal observation positions.

The combined approach enhances robustness to noisy data.

Abstract

Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have been proposed to solve TFI-HSS, those existing methods ignore correlations between data constraints and physics constraints, causing the low precision. In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optima positions of noise observations. For the TFI-HSS task, the PINN-TFI method encodes constrain terms into the loss function, thus the task is transformed into an optimization problem of minimizing the loss function. In addition, we have found that noise observations significantly affect reconstruction performances of the PINN-TFI method. To alleviate the effect of noise observations, the CMCN-PSO method is proposed to find optimal positions, where the condition number of observations is used to evaluate positions. The results demonstrate that the PINN-TFI method can significantly improve prediction precisions and the CMCN-PSO method can find good positions to acquire a more robust temperature field.