Scaled Conjugate Gradient Algorithm Trained ANN for Parameter Retrieval in Combined Heat Transfer in Porous Ceramic Matrix

Abstract

AbstractA combined mode conduction radiation problem is considered in 2 D rectangular porous ceramic matrix (PCM). The governing equations of the problem are solved by finite volume method (FVM) to compute temperature profiles for solid and gas phases. This solution is then used to train artificial neural network (ANN). Very popular scaled conjugate gradient algorithm is employed to train the neurons in ANN. The trained ANN model is analyzed for its robustness with the help of performance curves, histogram and regression analysis. The trained ANN model is fed with an unknown gas and solid temperature profile, and the ANN model is able to give the corresponding heat transfer coefficient (HTC), with good accuracy of 5.4%. The refression coefficient of 0.998 is obtained for the ANN model.KeywordsPorous ceramic matrixConductive-radiative transferParameter retrievalInverse analysisArtificial neural networkScaled conjugate gradient algorithm