Parametric optimization design for supercritical CO2 power cycle using genetic algorithm and artificial neural network

2010 ◽  
Vol 87 (4) ◽  
pp. 1317-1324 ◽  
Author(s):  
Jiangfeng Wang ◽  
Zhixin Sun ◽  
Yiping Dai ◽  
Shaolin Ma
2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.


2020 ◽  
Vol 37 (6) ◽  
pp. 429-436
Author(s):  
Kyu-Seok Jung ◽  
Sung-Min Cho ◽  
Jae-Hyeong Yu ◽  
Yo-Han Yoo ◽  
Jong-Bong Kim ◽  
...  

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammad Mehdi Arab ◽  
Abbas Yadollahi ◽  
Maliheh Eftekhari ◽  
Hamed Ahmadi ◽  
Mohammad Akbari ◽  
...  

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