Uncertainty Quantification Using Surrogate Models

Abstract

Uncertainty quantification (UQ) in aerodynamic simulations is retarded by the high computational cost of CFD models. With gradient information obtained at a reduced cost by using an adjoint solver, gradient-employing surrogate methods are promising in speeding up the UQ process. To investigate efficiency of UQ methods we apply gradient-enhanced radial basis functions, gradient-enhanced point-collocation polynomial chaos, gradient-enhanced Kriging and quasi-Monte Carlo quadrature to a test case where the geometry of a RAE2822 airfoil is perturbed by a Gaussian random field parameterized by 10 independent variables. The four methods are compared in their efficiency in estimating some statistics and probability distribution of the uncertain lift and drag coefficients. The results show that with the same computational effort the gradient-employing surrogate methods achieve better accuracy in the UQ than the QMC does.