Temperature monitoring during the life time of heat source components in engineering systems becomes essential to guarantee the normal work and the working life of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may decrease the availability and reliability of the system and sharply increase the monitoring cost. To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly. First, we define the TFR-HSS task mathematically, and numerically model the task, and hence transform the task as an image-to-image regression problem. Then this work develops the deep reversible regression model which can better learn the physical information, especially over the boundary. Finally, considering the physical characteristics of heat conduction as well as the boundary conditions, this work proposes the physics-informed reconstruction loss including four training losses and jointly learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness of the proposed method.
翻译:工程系统热源组成部分寿命期内的温度监测对于保证正常工作和这些组成部分的工作寿命至关重要,然而,以前的方法主要是利用内推估计从有限的监测点重建温度场,而以前的方法主要是利用内部估计从有限的监测点重建温度场,需要大量的温度拉值才能准确估计。这可能会减少系统的可用性和可靠性,并大大增加监测成本。为了解决这个问题,这项工作为热源系统(TFR-HSS)的温度场重建开发了一种新的物理知情的深反向回归模型,这种模型可以以不受监督的方式更好地以有限的监测点重建温度场。首先,我们用数学和数字模型来界定TFR-HSS的任务,从而将任务转换为图像到图像回归问题。然后,这项工作开发了深重的可逆回归模型,以更好地了解物理信息,特别是在边界上。最后,考虑到热导的物理特性以及边界条件,这项工作提议进行物理知情的重建损失,包括4项培训损失,并共同学习深基模模型,同时演示典型的热源效率。