The Monte Carlo simulation method for turbomachinery uncertainty analysis often requires performing a huge number
of simulations, the computational cost of which can be greatly alleviated with the help of metamodeling techniques. An
intensive comparative study was performed on the approximation performance of three prospective artificial intelligence
metamodels, that is, artificial neural network, radial basis function, and support vector regression. The genetic algorithm
was used to optimize the predetermined parameters of each metamodel for the sake of a fair comparison. Through testing
on 10 nonlinear functions with different problem scales and sample sizes, the genetic algorithm–support vector
regression metamodel was found more accurate and robust than the other two counterparts. Accordingly, the genetic
algorithm–support vector regression metamodel was selected and combined with the Monte Carlo simulation method
for the uncertainty analysis of a wind turbine airfoil under two types of surface roughness uncertainties. The results
show that the genetic algorithm–support vector regression metamodel can capture well the uncertainty propagation
from the surface roughness to the airfoil aerodynamic performance. This work is useful to the application of metamodeling
techniques in the robust design optimization of turbomachinery
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)