Retrieval of Parameter in Combined Mode Conduction–Radiation Problem in Porous Ceramic Matrix by Artificial Neural Network

Abstract

AbstractArtificial neural network (ANN) is used to retrieve one parameter in conduction–radiation heat transfer problem in porous ceramic matrix. Air flows through a 2D rectangular porous ceramic matrix (PCM) with uniform velocity. The PCM is assumed to be conducting and radiating, also a localized heat generation zone is situated at center. All the governing equations together with appropriate boundary conditions are solved by using finite volume method (FVM), to compute the temperature profiles of the gas and the solid phase. Both the temperature profiles are generated for different values of heat transfer coefficient (HTC). The ANN is trained by using the solid and gas temperature profile, along with the corresponding HTC. Neurons in the ANN are trained by using Levenberg–Marquardt (LM). Once the ANN model is trained, it is analyzed and explored to determine one parameter in the problem. The trained ANN model is fed with an unknown solid and gas temperature profiles as input, the ANN gives back the corresponding HTC as output. The retrieval of HTC by LM algorithm is found to be very accurate.KeywordsPorous ceramic matrixConductive–radiative transferParameter retrievalInverse analysisArtificial neural networkLevenberg–Marquardt algorithm