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Calibri 83ffff̙̙3f3fff3f3f33333f33333.@TU Delft Repositoryg Quuidrepository linktitleauthorcontributorpublication yearabstract
subject topiclanguagepublication type publisherisbnissnpatent
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departmentresearch group programmeprojectcoordinates)uuid:34bac75c966b41479433f4a177c06f83Dhttp://resolver.tudelft.nl/uuid:34bac75c966b41479433f4a177c06f83iA robust and efficient uncertainty quantification method for coupled fluidstructure interaction problemsWitteveen, J.A.S.; Bijl, H.QA robust and efficient uncertainty quantification method is presented for resolving the effect of uncertainty on the behavior of multiphysics 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.luncertainty quantification; fluidstructure interaction; polynomial chaos; extrema diminishing; error boundsenconference paperAInternational Center for Numerical Methods in Engineering (CIMNE)Aerospace EngineeringAerodynamics & Wind Energy)uuid:229fddd2cd2345479383b95da4e5d7b1Dhttp://resolver.tudelft.nl/uuid:229fddd2cd2345479383b95da4e5d7b1DExplicit inverse distance weighting mesh motion for coupled problemsAn 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 pointbypoint method is robust and flexible in case of large deformations, hanging nodes, and parallelization. Mesh quality results and CPU time comparisons are presented for triangular and hexahedral unstructured meshes in an airfoil flutter fluidstructure interaction problem.lmesh motion; inverse distance weighting; explicit interpolation; pointbypoint; fluidstructure interaction)uuid:a212a4affddd4966b03f4a9cca4e85bbDhttp://resolver.tudelft.nl/uuid:a212a4affddd4966b03f4a9cca4e85bbHEfficient uncertainty quantification in unsteady aeroelastic simulationsRAn 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.Luncertainty quantification; stochastic collocation; unsteady; aeroelasticity)uuid:29ee20c532ad447d9c3b3798511e9e49Dhttp://resolver.tudelft.nl/uuid:29ee20c532ad447d9c3b3798511e9e49lUnsteady adaptive stochastic finite elements for quantification of uncertainty in timedependent simulationsCivilComp Press)uuid:1bc396bd63b4409a92b80996be9ab50dDhttp://resolver.tudelft.nl/uuid:1bc396bd63b4409a92b80996be9ab50dTUnsteady adaptive stochastic finite elements for aeroelastic systems with randomnessAIAA)uuid:533e8611b48049b696328936a87c9829Dhttp://resolver.tudelft.nl/uuid:533e8611b48049b696328936a87c9829zAn unsteady adaptive stochastic finite elements uncertainty quantification method for fluidstructure interaction problemsuncertainty quantification; adaptive stochastic finite elements; fluidstructure interaction; unsteady problems, random parameters9International Center for Numerical Methods in Engineering)uuid:f7f6ec252e29498cb91995c2181ad716Dhttp://resolver.tudelft.nl/uuid:f7f6ec252e29498cb91995c2181ad716]Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial ChaosInherent 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 expansion has some limitations. Firstly, the polynomial chaos expansion based on classical polynomials can achieve exponential convergence for a limited set of standard distributions only. S<econdly, the application of polynomial chaos to nonlinearities can be difficult. These two limitations of the polynomial chaos expansion are discussed in this paper.Duncertainty quantification; polynomial chaos; orthogonal polynomials)uuid:2b9f4315bc5a4767a48f15d5bfb6be96Dhttp://resolver.tudelft.nl/uuid:2b9f4315bc5a4767a48f15d5bfb6be96UEfficient uncertainty quantification using a twostep approach with chaos collocation'Loeven, A.; Witteveen, J.A.S.; Bijl, H.In this paper a Two Step approach with Chaos Collocation for efficient uncertainty quantification in computational fluidstructure 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 II, for this most important parameter the Chaos Collocation method is employed to obtain the stochastic response of the solution. The Chaos Collocation method is presented in this paper, since a previous study showed that no efficient method was available for arbitrary probability distributions. The Chaos Collocation method is compared on efficiency with Monte Carlo simulation, the Polynomial Chaos method, and the Stochastic Collocation method. The Chaos Collocation method is nonintrusive and shows exponential convergence with respect to the polynomial order for arbitrary parameter distributions. Finally, the efficiency of the Two Step approach with Chaos Collocation is demonstrated for the linear piston problem with an unsteady boundary condition. A speedup of a factor of 100 is obtained compared to a full uncertainty analysis for all parameters.Computational Fluid Dynamics; fluidstructure interaction; nonintrusive; polynomial chaos; stochastic collocation; uncertainty quantification)uuid:0900707c6859473b9999cc666c57f741Dhttp://resolver.tudelft.nl/uuid:0900707c6859473b9999cc666c57f741bA 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 monomial chaos approach employs a polynomial chaos expansion with monomials as basis functions. The expansion coefficients are solved for using implicit differentiation of the governing equations. This results in a decoupled set of linear equations even for nonlinear problems, which reduces the computational work per additional polynomial chaos order to the equivalence of one Newton iteration. The results of the monomial chaos applied to nonlinear advectiondiffusion are compared with results of the perturbation method, the Galerkin polynomial chaos method and a nonintrusive polynomial chaos method with respect to a Monte Carlo reference solution. The accuracy of the monomial chaos can be further improved by estimating additional coefficients using extrapolation.Juncertainty quantification; computational fluid dynamics; polynomial chaos)uuid:2f0cb55507c447ad9c30c61c14a00bebDhttp://resolver.tudelft.nl/uuid:2f0cb55507c447ad9c30c61c14a00bebiDelft University of Technology; European Community on Computational Methods in Applied Sciences (ECCOMAS))uuid:9dc0f5110ba54b4b95919e34a49fc476Dhttp://resolver.tudelft.nl/uuid:9dc0f5110ba54b4b95919e34a49fc476)uuid:b52a1655621f45909ad50206c090d251Dhttp://resolver.tudelft.nl/uuid:b52a1655621f45909ad50206c090d251
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