Heat conduction problems can be categorized as direct and inverse problems. Calculating temperature distribution within a domain for given boundary conditions is a direct problem. In an inverse heat conduction problem (IHCP), however, the temperature distribution at one or multiple locations within the domain are known and the active boundary condition must be determined. The solution of IHCPs is highly sensitive to the measurement errors and therefore the problem is mathematically ill-posed. Developing accurate and stable solutions for 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 performed alternatively. Application of intelligent algorithms for solving IHCPs have 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 surface recession rate and temperature measurement data from an interior point for limited number of time steps as the inputs and calculate the surface heat flux 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 (TPS) in space vehicles.    


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

Division / Sponsors: 
Heat Transfer Division (HTD), Fluids Engineering Division (FED)

Heat Transfer Summer Conference
Paper Proceedings: https://asmedigitalcollection.asme.org/HT/HT2020/volume/83709

18th International Conference on Nanochannels, Microchannels, and Minichannels
Paper Proceedings: https://asmedigitalcollection.asme.org/ICNMM/ICNMM2020/volume/83693

Fluids Engineering Division Summer Meeting
Paper Proceedings: https://asmedigitalcollection.asme.org/FEDSM/FEDSM2020/volume/83716

HT-ICNMM2020 - Virtual Conference, SHTC - Virtual Conference, FEDSM2020 - Virtual Conference

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