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 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...

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

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...

Multi-fidelity methods for fluid-structure interaction and uncertainty quantification

The cost and turnaround time of the load calculation cycle in the design process of aircraft can be reduced by developing new numerical simulation technologies aimed at efficient prediction of steady and unsteady force coefficients in the flight envelope. In order to capture the most extreme stress levels due to gust and manoeuvres, efficient...

Uncertainty quantification in particle image velocimetry and advances in time-resolved image and data analysis

Particle Image Velocimetry (PIV) is a well-established technique for the measurement of the flow velocity in a two or three-dimensional domain. As in any other technique, PIV data is affected by measurement errors, defined as the difference between the measured velocity and its actual value, which is unknown. Aim of uncertainty quantification is...

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 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...

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...

A Bayesian study of uncertainty in ultrasonic flow meters under non-ideal flow conditions

This paper presents an approach for updating the epistemic uncertainty of ultrasonic flow meter measurements under non-ideal flow conditions. Instead of re-calibrating the instrument to correct its behavior in these difficult working conditions, a Bayesian calibration of a computer model of the real process is used. The numerical model is...