Multi-objective optimization of turbomachinery using improved NSGA-II and approximation model

Computer Methods in Applied Mechanics and Engineering

Volumn 200, Issue 9-12, 2011, Pages 883-895

  Wang, X D ^a,c   Hirsch, C ^b   Kang, S ^a   Lacor, C ^c  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b NUMECA Intenational SA   (Belgium)

^c VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

Author keywords

Axial compressor; Back propagation neural network; CFD simulation; Genetic algorithm; Multi objective optimization

Indexed keywords

AERODYNAMIC OPTIMIZATION; APPROXIMATION MODEL; AXIAL COMPRESSORS; BACK PROPAGATION NEURAL NETWORKS; CFD SIMULATIONS; COARSE-TO-FINE; COMPUTATIONAL COSTS; COUPLED METHOD; CROWDING DISTANCE; ENGINEERING OPTIMIZATION; IMPROVED NSGA-II; MASS FLOW; MATHEMATICAL PROBLEMS; MULTI-OBJECTIVE GENETIC ALGORITHM; NSGA-II; OPTIMIZATION METHOD; PARETO FRONT; PERFORMANCE EVALUATION; PREDICTION ERRORS; SINGLE OBJECTIVE OPTIMIZATION; TRAINING SAMPLE; TRANSONIC AXIAL COMPRESSORS; VERIFICATION RESULTS; WEIGHTING FUNCTIONS; WORKING POINT;

APPROXIMATION ALGORITHMS; AXIAL FLOW TURBOMACHINERY; BACKPROPAGATION; COMPRESSORS; COMPUTATIONAL FLUID DYNAMICS; COMPUTER SIMULATION; GENETIC ALGORITHMS; INDUSTRIAL APPLICATIONS; MATHEMATICAL MODELS; MODELS; NEURAL NETWORKS;

MULTIOBJECTIVE OPTIMIZATION;

EID: 78650612499     PISSN: 00457825     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cma.2010.11.014     Document Type: Article

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