Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

Multi-objective optimization of elliptical tube fin heat exchangers based on neural networks and genetic algorithm

Článek WoS

The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimization variables. In order to obtain the optimal heat transfer performance and pressure drop performance, Computational Fluid Dynamics (CFD) simulation is carried out for different Reynolds based on the hydraulic diameter numbers (150-750) and tube ellipticity (0.2-1). Then use simulation data to train the Back-Propagation neural networks and establish the prediction model of heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic al-gorithm with elitist retention strategy (NSGA-II) is used to optimize two prediction results of NNs. Finally, the optimal heat transfer coefficient and pressure drop are given in the form of Pareto front. The optimization results show that when the Reynolds number is 541 and the ellipticity is 0.34, the pressure drop of the TFHE decreases 20%, and the heat transfer coefficient is basically unchanged, whose j/f is 1.28 times as much as that of the original heat exchanger.

The application of machine learning based on neural networks (NNs) and genetic algorithm (GA) in multi-objective optimization of heat exchangers is studied. Taking the tube fin heat exchanger (TFHE) as the research object, the inlet air velocity and the ellipticity of tubes are taken as the optimization variables. In order to obtain the optimal heat transfer performance and pressure drop performance, Computational Fluid Dynamics (CFD) simulation is carried out for different Reynolds based on the hydraulic diameter numbers (150-750) and tube ellipticity (0.2-1). Then use simulation data to train the Back-Propagation neural networks and establish the prediction model of heat transfer coefficient and pressure drop. The non-dominated multi-objective genetic al-gorithm with elitist retention strategy (NSGA-II) is used to optimize two prediction results of NNs. Finally, the optimal heat transfer coefficient and pressure drop are given in the form of Pareto front. The optimization results show that when the Reynolds number is 541 and the ellipticity is 0.34, the pressure drop of the TFHE decreases 20%, and the heat transfer coefficient is basically unchanged, whose j/f is 1.28 times as much as that of the original heat exchanger.

CFD; Genetic algorithm; Neural networks; Multi-objective optimization; PRESSURE-DROP CHARACTERISTICS; AIR SIDE PERFORMANCE; FRICTION CHARACTERISTICS; PLATE-FIN; CONFIGURATION; PARAMETERS; VORTICES

CFD; Genetic algorithm; Neural networks; Multi-objective optimization; PRESSURE-DROP CHARACTERISTICS; AIR SIDE PERFORMANCE; FRICTION CHARACTERISTICS; PLATE-FIN; CONFIGURATION; PARAMETERS; VORTICES

Zhang, Tianyi; Chen, Lei; Wang, Jin

2024

15.04.2023

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

PERGAMON-ELSEVIER SCIENCE LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND

0360-5442

Energy

269

1

Spojené království Velké Británie a Severního Irska

9

https://www.sciencedirect.com/science/article/pii/S0360544223001238?via%3Dihub