A Neural Network-Based Method for Real-Time Inversion of Nonlinear Heat Transfer Problems

Abstract

Inverse heat transfer problems are important in numerous scientific research and engineering applications. This paper proposes a network-based method utilizing the nonlinear autoregressive with exogenous inputs (NARX) neural network, which can achieve real-time identification of thermal boundary conditions for nonlinear transient heat transfer processes. With the introduction of the NARX neural network, the proposed method offers two key advantages: (1) The proposed method can obtain inversion results using only surface temperature time series. (2) The heat flux can be estimated even when the state equation of the system is unknown. The NARX neural network is trained using the Bayesian regularization algorithm with a dataset comprising exact surface temperature and heat flux data. The neural network takes current and historical surface temperature measurements as inputs to calculate the heat flux at the current time. The capability of the NARX method has been verified through numerical simulation experiments. Experimental results demonstrate that the NARX method has high precision, strong noise resistance, and broad applicability. The composition of the input data, the surface temperature measurement noise, and the boundary heat flux shape have been studied in detail for their impact on the inversion results. The NARX method is a highly competitive solution to inverse heat transfer problems.

Heat transfer in material having random thermal conductivity using Monte Carlo simulation and deep neural network

“…A physics-informed neural network has been used in unsteady heat transfer addressing both forward and inverse problems [21]. In another study one-dimensional heat conduction problems using a Bayesian regularized nonlinear autoregressive with exogenous inputs (NARX) neural network was done [22] .A data-driven Reduced Order Model (ROM) utilizing deep Convolutional Neural Networks (CNNs) were used for rapid prediction of two-dimensional steady-state conduction temperature fields. [23] Monte Carlo simulation (MCs), a widely embraced probabilistic method requiring substantial computational power, has been used to validate perturbation methods and the Spectral Stochastic Finite Element Method (SSFEM).…”