## 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.

Original language | English |
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Pages (from-to) | 665-679 |

Number of pages | 15 |

Journal | International Journal of Heat and Mass Transfer |

Volume | 48 |

Issue number | 3-4 |

DOIs | |

Publication status | Published - Jan 2005 |

Externally published | Yes |

## Keywords

- Back-propagation algorithm
- Cube
- Inverse heat conduction problem (IHCP)
- Non-linear problem
- Semi-infinite plate
- Sensitivity analysis
- Uncertainty analysis

## ASJC Scopus subject areas

- Condensed Matter Physics
- Mechanical Engineering
- Fluid Flow and Transfer Processes