Application of Artificial Neural Network As a Near-Real Time Technique for Solving Non-Linear Inverse Heat Conduction Problems in a One-Dimensional Medium With Moving Boundary

Abstract

Abstract Developing accurate and stable solutions for inverse heat conduction problems (IHCPs) is crucial in many industrial applications where direct measurement of surface conditions, such as heat flux or temperature, is not possible in practice and temperature measurement from interior points can be obtained alternatively. IHCPs are mathematically ill-posed and therefore developing stable solutions for them is challenging. Application of intelligent algorithms for solving IHCPs has been successfully explored for several cases. In the present paper, the problem of near real-time surface heat flux estimation in a one-dimensional domain with temperature dependent material properties and moving boundary is considered. An artificial neural network (ANN) is developed to use the temperature measurement data from interior points for limited number of time steps as the inputs and calculate the surface heat flux and recession rate at the current time step as the output of the network. For this purpose, a multi-layer perceptron (MLP) network is selected, trained and tested using heat flux-temperature data that were evaluated via COMSOL Multiphysics for a 1D medium that is exposed to standard heat flux profiles on its surface (including triangular, parabolic and step function). A randomly generated heat flux profile is also applied to the surface of the medium and temperature distribution is calculated via COMSOL Multiphysics. The temperature data are then used as the inputs to the network and surface heat flux is evaluated under this condition to assess the capability of the developed ANN in surface heat flux estimation. The performance of the network when using different number of inputs (previous and future time steps from which temperature data are needed for surface heat flux estimation) as well as different network topology are explored in the presence of random measurement error. The results show that the developed approach allows accurate near real-time surface heat flux estimation in a 1-D medium with temperature dependent material properties and moving boundary. The solution of this problem can be further extended to be used in sensors for ablative thermal protection system in space vehicles.