Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models

Optimization and Engineering

Volumn 9, Issue 3, 2008, Pages 239-255

  Mengistu, Temesgen ^a,b   Ghaly, Wahid ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

^b CENAERO   (Belgium)

Author keywords

Aerodynamic design; Artificial neural networks; Computational fluid dynamics; Genetic algorithm; Global optimization; NURBS; Response surface approximation

Indexed keywords

AERODYNAMICS; AIR; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; BIOELECTRIC PHENOMENA; BOOLEAN FUNCTIONS; ELECTRIC GENERATORS; FLOW SIMULATION; FLUID DYNAMICS; FLUID MECHANICS; FUEL ADDITIVES; FUNCTION EVALUATION; GAS DYNAMICS; GENETIC ALGORITHMS; MICROPROCESSOR CHIPS; NAVIER STOKES EQUATIONS; NEURAL NETWORKS; TWO DIMENSIONAL;

AERODYNAMIC OPTIMIZATION; AERODYNAMIC PERFORMANCE; APPLIED (CO); ARTIFICIAL NEURAL NETWORK (ANN) MODELS; ARTIFICIAL NEURAL NETWORK (ANNS); BACK-PROPAGATION (BP) ALGORITHMS; BLADE PERFORMANCE; BLADE PROFILE; BUSINESS MEDIA; COMPUTATION TIME; COMPUTING TIME; DATA BASE; DESIGN POINTS; DESIGN VARIABLES; EVOLUTIONARY METHODS; GENETIC ALGORITHM (GA); HIGH FIDELITY; LINEAR CASCADES; LOW FIDELITY (LOW FI); MULTI POINT OPTIMIZATION; NURBS FUNCTIONS; OBJECTIVE FUNCTION (OF); OPERATING RANGES; OPTIMIZATION METHODOLOGY; OPTIMIZATION METHODS; OPTIMIZATION SCHEMES; PARALLELIZING; PERFORMANCE OBJECTIVES; REYNOLDS-AVERAGED NAVIER STOKES (RANS) EQUATIONS; SIMULATION-BASED OPTIMIZATIONS; SPRINGER (CO); SUBSONIC COMPRESSOR; SURROGATE MODELING; TRANSONIC TURBINE; TURN-AROUND; TWO DIMENSIONAL FLOWS; WEIGHTED SUM;

OPTIMIZATION;

EID: 46849105646     PISSN: 13894420     EISSN: 15732924     Source Type: Journal    
DOI: 10.1007/s11081-007-9031-1     Document Type: Article

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