"uuid","repository link","title","author","contributor","publication year","abstract","subject topic","language","publication type","publisher","isbn","issn","patent","patent status","bibliographic note","access restriction","embargo date","faculty","department","research group","programme","project","coordinates"
"uuid:34bac75c-966b-4147-9433-f4a177c06f83","http://resolver.tudelft.nl/uuid:34bac75c-966b-4147-9433-f4a177c06f83","A robust and efficient uncertainty quantification method for coupled fluid-structure interaction problems","Witteveen, J.A.S.; Bijl, H.","","2009","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.","uncertainty quantification; fluid-structure interaction; polynomial chaos; extrema diminishing; error bounds","en","conference paper","International Center for Numerical Methods in Engineering (CIMNE)","","","","","","","","Aerospace Engineering","Aerodynamics & Wind Energy","","","",""
"uuid:229fddd2-cd23-4547-9383-b95da4e5d7b1","http://resolver.tudelft.nl/uuid:229fddd2-cd23-4547-9383-b95da4e5d7b1","Explicit inverse distance weighting mesh motion for coupled problems","Witteveen, J.A.S.; Bijl, H.","","2009","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 and CPU time comparisons are presented for triangular and hexahedral unstructured meshes in an airfoil flutter fluid-structure interaction problem.","mesh motion; inverse distance weighting; explicit interpolation; point-by-point; fluid-structure interaction","en","conference paper","International Center for Numerical Methods in Engineering (CIMNE)","","","","","","","","Aerospace Engineering","Aerodynamics & Wind Energy","","","",""
"uuid:a212a4af-fddd-4966-b03f-4a9cca4e85bb","http://resolver.tudelft.nl/uuid:a212a4af-fddd-4966-b03f-4a9cca4e85bb","Efficient uncertainty quantification in unsteady aeroelastic simulations","Witteveen, J.A.S.; Bijl, H.","","2009","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.","uncertainty quantification; stochastic collocation; unsteady; aeroelasticity","en","conference paper","","","","","","","","","Aerospace Engineering","Aerodynamics & Wind Energy","","","",""
"uuid:29ee20c5-32ad-447d-9c3b-3798511e9e49","http://resolver.tudelft.nl/uuid:29ee20c5-32ad-447d-9c3b-3798511e9e49","Unsteady adaptive stochastic finite elements for quantification of uncertainty in time-dependent simulations","Witteveen, J.A.S.; Bijl, H.","","2008","","","en","conference paper","Civil-Comp Press","","","","","","","","Aerospace Engineering","","","","",""
"uuid:1bc396bd-63b4-409a-92b8-0996be9ab50d","http://resolver.tudelft.nl/uuid:1bc396bd-63b4-409a-92b8-0996be9ab50d","Unsteady adaptive stochastic finite elements for aeroelastic systems with randomness","Witteveen, J.A.S.; Bijl, H.","","2008","","","en","conference paper","AIAA","","","","","","","","Aerospace Engineering","","","","",""
"uuid:533e8611-b480-49b6-9632-8936a87c9829","http://resolver.tudelft.nl/uuid:533e8611-b480-49b6-9632-8936a87c9829","An unsteady adaptive stochastic finite elements uncertainty quantification method for fluid-structure interaction problems","Witteveen, J.A.S.; Bijl, H.","","2008","","uncertainty quantification; adaptive stochastic finite elements; fluid-structure interaction; unsteady problems, random parameters","en","conference paper","International Center for Numerical Methods in Engineering","","","","","","","","Aerospace Engineering","","","","",""
"uuid:f7f6ec25-2e29-498c-b919-95c2181ad716","http://resolver.tudelft.nl/uuid:f7f6ec25-2e29-498c-b919-95c2181ad716","Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos","Witteveen, J.A.S.; Bijl, H.","","2006","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 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. Secondly, the application of polynomial chaos to nonlinearities can be difficult. These two limitations of the polynomial chaos expansion are discussed in this paper.","uncertainty quantification; polynomial chaos; orthogonal polynomials","en","conference paper","","","","","","","","","","","","","",""
"uuid:2b9f4315-bc5a-4767-a48f-15d5bfb6be96","http://resolver.tudelft.nl/uuid:2b9f4315-bc5a-4767-a48f-15d5bfb6be96","Efficient uncertainty quantification using a two-step approach with chaos collocation","Loeven, A.; Witteveen, J.A.S.; Bijl, H.","","2006","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 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 non-intrusive 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 speed-up of a factor of 100 is obtained compared to a full uncertainty analysis for all parameters.","Computational Fluid Dynamics; fluid-structure interaction; non-intrusive; polynomial chaos; stochastic collocation; uncertainty quantification","en","conference paper","","","","","","","","","","","","","",""
"uuid:0900707c-6859-473b-9999-cc666c57f741","http://resolver.tudelft.nl/uuid:0900707c-6859-473b-9999-cc666c57f741","A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics","Witteveen, J.A.S.; Bijl, H.","","2006","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 advection-diffusion are compared with results of the perturbation method, the Galerkin polynomial chaos method and a non-intrusive 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.","uncertainty quantification; computational fluid dynamics; polynomial chaos","en","conference paper","","","","","","","","","","","","","",""
"uuid:2f0cb555-07c4-47ad-9c30-c61c14a00beb","http://resolver.tudelft.nl/uuid:2f0cb555-07c4-47ad-9c30-c61c14a00beb","A Monomial Chaos Approach for Efficient Uncertainty Quantification in Computational Fluid Dynamics","Witteveen, J.A.S.; Bijl, H.","","2006","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 advection-diffusion are compared with results of the perturbation method, the Galerkin polynomial chaos method and a non-intrusive 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.","uncertainty quantification; computational fluid dynamics; polynomial chaos","en","conference paper","Delft University of Technology; European Community on Computational Methods in Applied Sciences (ECCOMAS)","","","","","","","","Aerospace Engineering","","","","",""
"uuid:9dc0f511-0ba5-4b4b-9591-9e34a49fc476","http://resolver.tudelft.nl/uuid:9dc0f511-0ba5-4b4b-9591-9e34a49fc476","Reliable Computational Predictions by Modeling Uncertainties Using Arbitrary Polynomial Chaos","Witteveen, J.A.S.; Bijl, H.","","2006","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 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. Secondly, the application of polynomial chaos to nonlinearities can be difficult. These two limitations of the polynomial chaos expansion are discussed in this paper.","uncertainty quantification; polynomial chaos; orthogonal polynomials","en","conference paper","Delft University of Technology; European Community on Computational Methods in Applied Sciences (ECCOMAS)","","","","","","","","Aerospace Engineering","","","","",""
"uuid:b52a1655-621f-4590-9ad5-0206c090d251","http://resolver.tudelft.nl/uuid:b52a1655-621f-4590-9ad5-0206c090d251","Efficient uncertainty quantification using a two-step approach with chaos collocation","Loeven, A.; Witteveen, J.A.S.; Bijl, H.","","2006","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 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 non-intrusive 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 speed-up of a factor of 100 is obtained compared to a full uncertainty analysis for all parameters.","Computational Fluid Dynamics; fluid-structure interaction; non-intrusive; polynomial chaos; stochastic collocation; uncertainty quantification","en","conference paper","Delft University of Technology; European Community on Computational Methods in Applied Sciences (ECCOMAS)","","","","","","","","Aerospace Engineering","","","","",""