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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
Summary
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.
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.
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