Multi-Objective Optimization of Vortex Generators Positions and Angles in Fin-Tube Compact Heat Exchanger at Low Reynolds Number Using Neural Network and Genetic Algorithm

2014 ◽  
Author(s):  
Leandro O. Salviano ◽  
Daniel J. Dezan ◽  
Jurandir Yanagihara
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reynolds Number ◽  
Heat Exchanger ◽  
Low Reynolds Number ◽  
Vortex Generators ◽  
Compact Heat Exchanger ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fin Tube

scholarly journals Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Aerospace ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 35
Author(s):  
Abu Bakar ◽  
Ke Li ◽  
Haobo Liu ◽  
Ziqi Xu ◽  
Marco Alessandrini ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Reynolds Number ◽  
Convolutional Neural Network ◽  
Low Reynolds Number ◽  
Multi Objective Optimization ◽  
Aerodynamic Coefficients ◽  
Nsga Ii ◽  
Low Reynolds ◽  
Low Reynolds Number Airfoil

The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

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