A Multiple Regression Convolutional Neural Network for Estimating Multi-Parameters Based on Overall Data in the Inverse Heat Transfer Problem

Abstract

Abstract The inverse heat transfer problem (IHTP) is a central task for estimating parameters in heat transfer. It is ill-posedness that is characterized by instability and non-uniqueness of the solution. Recently, novel algorithms using deep learning (DL) and neural networks have been applied to various sparse data in the IHTP. In order to overcome the optimization problem of input nodes under sparse data, we try to use the overall data of the target as the basis for inversion. In this work, we used a multiple regression convolutional neural network (MRCNN) to estimate multi-parameters in the IHTP. Computational fluid dynamics and DL are fused to provide datasets for training of the proposed model. The proposed model was verified by experiments with a cubic cavity. Additionally, the MRCNN model was used to predict the different parameters of the more complex armored vehicle model. The results showed that the model has good prediction accuracy for estimating multi-parameters on different datasets. These attempts of introducing convolutional neural network to the IHTP in the present study were successful and it was significant for the study of the IHTP of estimating multi-parameters.