Non-iterative estimation of heat transfer coefficients using artificial neural network models

S. S. Sablani, A. Kacimov, J. Perret, A. S. Mujumdar, A. Campo

Research output: Contribution to journalArticle

79 Citations (Scopus)

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 languageEnglish
Pages (from-to)665-679
Number of pages15
JournalInternational Journal of Heat and Mass Transfer
Volume48
Issue number3-4
DOIs
Publication statusPublished - Jan 2005

Fingerprint

heat transfer coefficients
Heat transfer coefficients
Neural networks
Heat conduction
conductive heat transfer
Biot number
Temperature
trigonometric functions
performance prediction
temperature ratio
temperature
thermophysical properties
Parameter estimation
Thermal conductivity
education
thermal conductivity
Thermodynamic properties
assembly
histories
Boundary conditions

Keywords

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

ASJC Scopus subject areas

  • Fluid Flow and Transfer Processes
  • Energy(all)
  • Mechanical Engineering

Cite this

Non-iterative estimation of heat transfer coefficients using artificial neural network models. / Sablani, S. S.; Kacimov, A.; Perret, J.; Mujumdar, A. S.; Campo, A.

In: International Journal of Heat and Mass Transfer, Vol. 48, No. 3-4, 01.2005, p. 665-679.

Research output: Contribution to journalArticle

Sablani, S. S. ; Kacimov, A. ; Perret, J. ; Mujumdar, A. S. ; Campo, A. / Non-iterative estimation of heat transfer coefficients using artificial neural network models. In: International Journal of Heat and Mass Transfer. 2005 ; Vol. 48, No. 3-4. pp. 665-679.
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