Reliability analysis of heat source layout temperature field prediction considering uncertainty in deep neural network surrogate models

Abstract

AbstractIn engineering systems such as energy systems and electronic devices, thermal reliability analysis of the heat source component area is critical to ensure the proper functioning of these components. Previous methods, due to the high data dimensionality of the temperature field of the whole heat source area, face high computational cost when analyzing the reliability. To solve the problem of high computational cost, reliability analysis methods based on deep neural network surrogate models are receiving more and more attention. However, the uncertainty of deep neural networks is rarely considered in reliability problems, leading to unreasonable or imperfect reliability analysis. Therefore, we propose a reliability analysis method for heat source layout temperature field prediction, considering the uncertainty of deep neural network surrogate models. The method uses the information on the standard deviation of temperature field prediction provided by the model uncertainty to obtain the interval prediction of the temperature field. Then, the temperature field reliability interval of the layout area can be obtained by combining the temperature field interval prediction and Monte Carlo sampling. Further, we propose a strategy for selecting the heat source layout scheme based on the reliability interval in the late engineering design stage. Finally, the method's validity is verified using some examples, which provides an important reference basis for engineers.