Inverse algorithm for boundary heat flux density based on the NARX neural network

Abstract

The inverse heat transfer problem is vital for scientific research and engineering applications. This paper introduces a method using the Nonlinear Autoregressive with Exogenous Inputs (NARX) neural network to identify heat boundary conditions in nonlinear transient heat transfer processes in real time. This method has two notable advantages: (1) It relies solely on surface temperature time series to obtain inversion results; (2) Even in the absence of knowledge regarding the system’s state equations, it can estimate heat flux density. The NARX neural network is trained by using Bayesian regularization with surface temperature and heat flux data. (3) As per the inversion results, the NARX neural network’s accuracy in predicting the boundary heat flux density (BHFD) increases as the temperature measurement points approach the heat flux boundary. This neural network calculates the current heat flux density by incorporating both present and past surface temperature measurements as inputs. Through numerical simulation experiments, the efficacy of the NARX method is confirmed, showcasing its exceptional accuracy, robustness against noise, and broad suitability.