Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ijheatmasstransfer.2004.09.005
Title: Non-iterative estimation of heat transfer coefficients using artificial neural network models
Authors: Sablani, S.S.
Kacimov, A.
Perret, J.
Mujumdar, A.S. 
Campo, A.
Keywords: Back-propagation algorithm
Cube
Inverse heat conduction problem (IHCP)
Non-linear problem
Semi-infinite plate
Sensitivity analysis
Uncertainty analysis
Issue Date: Jan-2005
Source: Sablani, S.S.,Kacimov, A.,Perret, J.,Mujumdar, A.S.,Campo, A. (2005-01). Non-iterative estimation of heat transfer coefficients using artificial neural network models. International Journal of Heat and Mass Transfer 48 (3-4) : 665-679. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ijheatmasstransfer.2004.09.005
Abstract: The Inverse Heat Conduction Problem (IHCP) dealing with the estimation of the heat transfer coefficient for a solid/fluid assembly from the knowledge of inside temperature was accomplished using an artificial neural network (ANN). Two cases were considered: (a) a cube with constant thermophysical properties and (b) a semi-infinite plate with temperature dependent thermal conductivity resulting in linear and nonlinear problem, respectively. The Direct Heat Conduction Problems (DHCP) of transient heat conduction in a cube and in a semi-infinite plate with a convective boundary condition were solved. The dimensionless temperature-time history at a known location was then correlated with the corresponding dimensionless heat transfer coefficient/Blot number using appropriate ANN models. Two different models were developed for each case i.e. for a cube and a semi-infinite plate. In the first one, the ANN model was trained to predict Biot number from the slope of the dimensionless temperature ratio versus Fourier number. In the second, an ANN model was developed to predict the dimensionless heat transfer coefficient from non-dimensional temperature. In addition, the training data sets were transformed using a trigonometric function to improve the prediction performance of the ANN model. The developed models may offer significant advantages when dealing with repetitive estimation of heat transfer coefficient. The proposed approach was tested for transient experiments. A 'parameter estimation' approach was used to obtain Biot number from experimental data. © 2004 Elsevier Ltd. All rights reserved.
Source Title: International Journal of Heat and Mass Transfer
URI: http://scholarbank.nus.edu.sg/handle/10635/60889
ISSN: 00179310
DOI: 10.1016/j.ijheatmasstransfer.2004.09.005
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