Probabilistic collocation used in a Two-Step approached for efficient uncertainty quantification in computational fluid dynamics

In this paper a Two-Step approach is presented for uncertainty quantification for expensive problems with multiple uncertain parameters. Both steps are performed using the Probabilistic Collocation method. The first step consists of a sensitivity analysis to identify the most important parameters of the problem. The sensitivity derivatives are...

A TVD uncertainty quantification method with bounded error applied to transonic airfoil flutter

The Unsteady Adaptive Stochastic Finite Elements (UASFE) approach is a robust and efficient uncertainty quantification method for resolving the effect of random parameters in unsteady simulations. In this paper, it is shown that the underlying Adaptive Stochastic Finite Elements (ASFE) method for steady problems based on Newton-Cotes quadrature...

Efficient uncertainty quantification in unsteady aeroelastic simulations

An efficient uncertainty quantification method for unsteady problems is presented in order to achieve a constant accuracy in time for a constant number of samples. The approach is applied to the aeroelastic problems of a transonic airfoil flutter system and the AGARD 445.6 wing benchmark with uncertainties in the flow and the structure.

A robust and efficient uncertainty quantification method for coupled fluid-structure interaction problems

A robust and efficient uncertainty quantification method is presented for resolving the effect of uncertainty on the behavior of multi-physics systems. The extrema diminishing method in probability space maintains a bounded error due to the interpolation of deterministic samples at constant phase in a transonic airfoil flutter problem.

A monomial chaos approach for efficient uncertainty quantification on nonlinear problems

A monomial chaos approach is presented for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed monomial...

An unsteady adaptive stochastic finite elements uncertainty quantification method for fluid-structure interaction problems

A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics

A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed...

Efficient uncertainty quantification using a two-step approach with chaos collocation

In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...

A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics

A monomial chaos approach is proposed for efficient uncertainty quantification in nonlinear computational problems. Propagating uncertainty through nonlinear equations can still be computationally intensive for existing uncertainty quantification methods. It usually results in a set of nonlinear equations which can be coupled. The proposed...

Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos

Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...

Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos

Inherent physical uncertainties can have a significant influence on computational predictions. It is therefore important to take physical uncertainties into account to obtain more reliable computational predictions. The Galerkin polynomial chaos method is a commonly applied uncertainty quantification method. However, the polynomial chaos...

Efficient uncertainty quantification using a two-step approach with chaos collocation

In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluid-structure interactions is followed. In Step I, a Sensitivity Analysis is used to efficiently narrow the problem down from multiple uncertain parameters to one parameter which has the largest influence on the solution. In Step...