Abstract: Various sources of uncertainty exist in numerical simulations of industry relevant geometries. Uncertainty quantification plays an important role during the design and assessment process. The computational cost increases dramatically with the number of stochastic input variables. Therefore, more efficient approaches are necessary. This paper develops a non-intrusive sparse polynomial chaos method and investigates the effects of uncertainty in turbulence model closure coefficients on the simulation of flow over RAE2822 airfoil and the effects of uncertainty in material properties on the prediction of charring ablator thermal response are investigated. It is proved that the method is capable of recovering the freedoms of several most important bases under the condition of under-determined system by solving P1 programming problem. The prediction of system response, including mean value, variance, and the relative contribution of each closure coefficient to the variation of the output quantities, is reasonable compared with full polynomial chaos. The method provides an efficient solution to the uncertainty quantification problems in industry applications.

Key words: uncertainty quantification, polynomial chaos, compressed sensing, verification and validation, sparsity