Temperature field inversion of heat-source systems via physics-informed neural networks. (August 2022)

• Record Type: Journal Article
• Authors: Liu, Xu; Peng, Wei; Gong, Zhiqiang; Zhou, Weien; Yao, Wen
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Temperature field inversion of heat-source systems -- Physics-informed neural network -- Position selection -- Condition number -- Upper bound of error; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104902
• Languages: English
• ISSNs: 0952-1976

Abstract

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 optimal positions of noisy observations. For the TFI-HSS task, the PINN-TFI method encodes constrain terms into the loss function and thus the task is transformed into an optimization problem of minimizing the loss function. In addition, we have found that noise significantly affect reconstruction performances of the PINN-TFI method. To alleviate the effect of noises in observations, we propose the CMCN-PSO method 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 improve the robustness of the PINN-TFI method.