Efficient Quantification of Aerodynamic Uncertainies Using Gradient-Employing Surrogate Methods

Abstract

Uncertainty quantification (UQ) in aerodynamic simulations is hindered by the high computational cost of CFD models. With gradient information obtained efficiently by using an adjoint solver, gradient-employing surrogate methods are promising in speeding up the UQ process. To investigate the efficiency of UQ methods we apply gradient-enhanced radial basis functions, gradient-enhanced point-collocation polynomial chaos, gradient-enhaced Kriging and quasi-Monte Carlo (QMC) quadrature to a test case where the geometry of an RAE2822 airfoil is perturbed by a Gaussian random field parameterized by 10 independant variables. The four methods are compared in their efficiency in estimating some statistics and the 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 than the QMC does