Quantifying the effect of physical uncertainties in                                                         unsteady fluid-structure interaction problems

Witteveen, Jeroen; Loeven, Alex; Bijl, H.

doi:10.2514/6.2007-1942

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…One can think of the curve-fitting procedure as a time-independent parameterisation of the transient aeroelastic response. A similar approach was implemented by Witteveen et al (2007) in the time-independent characterisation of limit cycle oscillations. This type of an approach is particularly useful in the stochastic analysis of the time-dependent output with Polynomial Chaos, since the accuracy of the approximation should not be affected from the growing non-linearity of the response in time, which is one of the challenges in the application of the NIPC methods to highly non-linear unsteady stochastic problems.…”

Section: Stochastic Results For the Aeroelastic Wing Casementioning

confidence: 98%

Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification

Hosder

2012

IJMMNO

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This paper gives an overview of computationally efficient stochastic response surface techniques based on non-intrusive polynomial chaos (NIPC) for uncertainty quantification in numerical models. The results of uncertainty analysis can be used in robust and reliability-based design and optimisation studies as well as the assessment of the accuracy of the simulation results. In particular, the uncertainty quantification with NIPC methods which require no modification to the existing deterministic models are demonstrated on computational fluid dynamics (CFD) simulations in this paper. The NIPC methods have been increasingly used for uncertainty propagation in high-fidelity CFD simulations due to their non-intrusive nature and strong potential for addressing the computational efficiency and accuracy requirements associated with large-scale complex stochastic simulations. The theory and description of various NIPC methods used for non-deterministic CFD simulations are presented, which can be applied to any other computational models used in analysis and optimisation problems. Several stochastic fluid dynamics examples are given to demonstrate the application and effectiveness of NIPC methods for uncertainty quantification in fluid dynamics. These examples include stochastic computational analysis of a laminar boundary layer flow over a flat plate, supersonic expansion wave problem, and inviscid transonic flow over a three-dimensional wing with rigid and aeroelastic assumptions.Keywords: stochastic response surface; polynomial chaos; computational fluid dynamics; CFD; uncertainty quantification.Reference to this paper should be made as follows: Hosder, S. (2012) 'Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification', Int. . He received his PhD in Aerospace Engineering from Virginia Tech., Blacksbusrg, VA, USA in 2004. His research interests include aerodynamics, computational fluid dynamics, hypersonics, uncertainty quantification in computational simulations, and multidisciplinary design and optimisation. 118 S. Hosder

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Section: Stochastic Results For the Aeroelastic Wing Casementioning

confidence: 98%

Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification

Hosder

2012

IJMMNO

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“…16,20 The parametrization consist of a damping factor, frequency, relative phase, amplitude, a reference value, and the normalized (higher-period) shape function. Due to this time-independent parametrization the interpolation accuracy is independent of time.…”

Section: Introductionmentioning

confidence: 99%

Unsteady Adaptive Stochastic Finite Elements for Aeroelastic Systems with Randomness

Witteveen¹,

Bijl²

2008

49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference &Amp;lt;br> 16th AIAA/ASME/AHS Ada

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It is recognized in the engineering community that there is an increasing need to move towards unsteady simulations in computational fluid dynamics. This trend also dictates an increasing application of uncertainty quantification methods to unsteady problems. In this paper, an Unsteady Adaptive Stochastic Finite Elements method based on interpolation at constant phase (UASFE-cp) is introduced for resolving the effect of random parameters in unsteady simulations. It achieves a constant accuracy in time with a constant number of samples, in contrast with the usually fast increasing number of samples required by other non-intrusive methods. Results for the stochastic bifurcation behavior of an elastically mounted airfoil with nonlinearity in the flow and the structure are presented.

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“…The Collocation methods (pseudo-spectral methods) include a variety of different collocation techniques for random variables based on global [61] or multi-element collocation [62,63].…”

Section: Spectral and Collocation Methodsmentioning

confidence: 99%

“…Accuracy of all methods is validated using the MCS approach. Then, Witteveen et al [61] have proposed an extension to the general non-intrusive PCE method in order to avoid the problem of grow ing variance in the unsteady analysis of Limit Cycle Oscillations. This method, referred to as PCLCO, bounds the variance for long-term results, where it obtains results comparable with the MCS.…”

Section: Spectral and Collocation Methodsmentioning

confidence: 99%

“…The ^-method, however, determines only the worse case scenario of the flutter phenomenon, and hence it does not actually quantify the risk of failure. The probabilistic analysis of the flutter phenomenon was also conducted by Witteveen et al [61] due to the random variability of the free flow airspeed. Although only a low amount of input uncertainty was selected, the results have shown a very substantial prob ability of flutter failure.…”

Section: Probabilistic Flutter Speed Estimationmentioning

confidence: 99%

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Parametric uncertainty quantification in coalescene flutter

Matachniouk¹

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Quantifying the effect of physical uncertainties in unsteady fluid-structure interaction problems

Cited by 5 publications

References 18 publications

Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification

Stochastic response surfaces based on non-intrusive polynomial chaos for uncertainty quantification

Unsteady Adaptive Stochastic Finite Elements for Aeroelastic Systems with Randomness

Parametric uncertainty quantification in coalescene flutter

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