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.
翻译:热源系统(TFI-HSS)的热源系统(TFI-HSS)的温度场反向变化是监测系统健康的关键,虽然提议了一些方法,例如内插方法来解决TFI-HSS,但这些现有方法忽略了数据限制和物理限制之间的相互关系,造成精确度低;在这项工作中,我们开发了一种物理知情神经网络反向温度场(PINN-TFI)的方法,以解决TFI-HIS任务,并以系数矩阵条件号条件选择观测位置的方法(CMCN-PSO)来选择噪音观测的优化位置(CMCN-PSO),对于TFI-HIS任务,PINN-TFI方法将限制条件编码为损失功能,从而将任务转化为最大限度地减少损失功能的优化问题。此外,我们发现噪音观测会严重影响PINN-TFI方法的重建性能。为了减轻噪音观测的影响,提议CMCNCN-PSO方法将找到最佳位置,其中使用观察条件号用于评价位置。结果表明PINN-TFI方法能够大大改进精确度的实地预测方法。