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...

Higher period stochastic bifurcation of nonlinear airfoil fluid-structure interaction

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.

Explicit inverse distance weighting mesh motion for coupled problems

An explicit mesh motion algorithm based on inverse distance weighting interpolation is presented. The explicit formulation leads to a fast mesh motion algorithm and an easy implementation. In addition, the proposed point-by-point method is robust and flexible in case of large deformations, hanging nodes, and parallelization. Mesh quality results...

A meshless front tracking method for the Euler equations of fluid dynamics

A second order front tracking method is developed for solving the Euler equations of inviscid fluid dynamics numerically. Front tracking methods are usually limited to first order accuracy, since they are based on a piecewise constant approximation of the solution. Here the second order convergence is achieved by building a piecewise linear...

Efficient and robust uncertainty quantification for computational fluid dynamics and fluid-structure interaction

Physical uncertainties due to atmospheric variations and production tolerances can nowadays have a larger effect on the accuracy of computational predictions than numerical errors. It is essential to quantify the effect of these uncertainties for reducing design safety factors and robust design optimization. This eventually contributes to the...

Higher Period Stochastic Bifurcation of Nonlinear Airfoil Fluid-Structure Interaction

The higher period stochastic bifurcation of a nonlinear airfoil fluid-structure interaction system is analyzed using an efficient and robust uncertainty quantification method for unsteady problems. The computationally efficient numerical approach achieves a constant error with a constant number of samples in time. The robustness of the method is...

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

Unsteady adaptive stochastic finite elements for quantification of uncertainty in time-dependent simulations

Unsteady adaptive stochastic finite elements for aeroelastic systems with randomness

A Second-Order Improved Front Tracking Method for the Numerical Treatment of the Hyperbolic Euler Equations

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...

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...