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Evolutionary Computing for the Radiative-Convective Heat Transfer of a Wetted Wavy Fin Using a Genetic

B S Poornima1, Ioannis E Sarris2, K Chandan1

  • 1Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru 560035, Karnataka, India.

Biomimetics (Basel, Switzerland)
|December 22, 2023
PubMed
Summary
This summary is machine-generated.

This study uses a genetic algorithm to model thermal variation on a wetted wavy fin, finding that temperature decreases with increased wet and convective-conductive parameters. The genetic algorithm enhances artificial neural network predictive accuracy.

Keywords:
artificial neural networkgenetic algorithmheat transferwavy finwet fin

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Area of Science:

  • Heat Transfer
  • Optimization Algorithms
  • Computational Fluid Dynamics

Background:

  • Evolutionary algorithms mimic natural selection for optimization.
  • Wetted wavy fins involve complex convective and radiative heat transfer.
  • Parameter estimation for artificial neural networks (ANNs) requires robust optimization.

Purpose of the Study:

  • To investigate thermal variation on a wetted wavy fin surface.
  • To apply a genetic algorithm (GA) for parameter estimation in ANNs.
  • To model convective and radiative heat transfer under wet surface conditions.

Main Methods:

  • A genetic algorithm was employed to optimize ANN parameters.
  • The governing ordinary differential equation was transformed into a dimensionless form.
  • Thermal profiles were analyzed for various non-dimensional variables.

Main Results:

  • Temperature profiles decrease with increasing wet parameters.
  • Higher convective-conductive parameters also lead to lower temperature profiles.
  • The GA demonstrated effective parameter tuning for ANNs, improving accuracy and convergence.

Conclusions:

  • Genetic algorithms are powerful tools for optimizing ANN models in heat transfer problems.
  • Understanding thermal behavior under wet conditions is crucial for fin design.
  • The study provides a validated approach for predicting thermal performance using ANNs.