Temperature monitoring during the life time of heat source components in engineering systems becomes essential to ensure the normal work and even the long working life of these heat sources. However, prior methods, which mainly use the interpolate estimation to reconstruct the whole temperature field with the temperature value 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. Furthermore, limited number of labelled training samples are available for the training of deep models. 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 the given limited monitoring points unsupervisedly. First, we define the temperature field reconstruction task of heat-source systems mathematically, numerically model the problem, and further transform the problem as an image-to-image regression problem. Then, based on the law of forward and backward propagation of deep models, this work develops the deep reversible regression model which can better learn the physical information near the boundary and improve the reconstruction performance. 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 joint learns the deep surrogate model with these losses unsupervisedly. Experimental studies have conducted over typical two-dimensional heat-source systems to demonstrate the effectiveness and efficiency of the proposed physics-informed deep reversible regression models for TFR-HSS task.
翻译:工程系统热源组成部分生命期内的温度监测对于确保正常工作甚至这些热源的长期工作寿命至关重要,然而,以前的方法,主要是利用内推估计,从有限的监测点以温度值重建整个温度场,需要大量温度加压,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;此外,为深层模型的培训提供了有限的贴标签培训样本;为了解决这一问题,这项工作为热源系统(TFR-HSS)的温度场重建开发了一种新的深深、有物理意识的深、可逆回归模型,这种模型可以更好地利用有限的监测点来重建温度场,而不受监督。首先,我们从数学角度界定热源系统的温度实地重建任务,用数字模型来模拟问题,并进一步将问题转化成一个图像到图像回归的问题。然后,根据深层模型的前瞻性和后向后传播法,这项工作开发了深、可逆转的深度回归模型,可以更好地了解边界附近的物理信息并改进典型的热源源系统(TFTFR-HSS-HSS)的回归模型,这可以更好地利用有限的监测点重建效率。最后,我们用数学的物理模型来模拟学习这些深度研究的物理损失的物理特征,作为基础的学习基础的学习基础的学习,最后,作为基础损失的学习的实验,并进行。