Titlebook: Uncertainty Quantification in Computational Fluid Dynamics; Hester Bijl,Didier Lucor,Christoph Schwab Book 2013 Springer International Pub

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BOAST

Chauvinistic

enumaerate

Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method,o solve the underlying uncertain systems, we rely on moment theory and the construction of a moment model in the framework of parametric polynomial approximations. We prove the spectral convergence of this approach for the uncertain inviscid Burgers’ equation. We also emphasize the difficulties aris

critique

Adaptive Uncertainty Quantification for Computational Fluid Dynamics,rst is “.”, which approximates the model response in the stochastic space on a triangular grid with quadratic reconstruction on the elements. Limiting reduces the reconstruction to linear in the presence of discontinuities, and the mesh is refined using the Hessian of the response as an indicator. T

尊嚴(yán)

獨(dú)行者

humectant

Essentially Non-oscillatory Stencil Selection and Subcell Resolution in Uncertainty Quantification,cs are extended to uncertainty quantification for the reliable approximation of discontinuities in stochastic computational problems. These two robustness principles are introduced into the simplex stochastic collocation uncertainty quantification method, which discretizes the probability space usin

AWE

Book 2013lume addresses the pertinent issue of efficiently computing the flow uncertainty, given this initial randomness. It collects seven original review articles that cover improved versions of the Monte Carlo method (the so-called multi-level Monte Carlo method (MLMC)), moment-based stochastic Galerkin m

Concrete

1439-7358 cal examples as verification of the proposed methods and theFluid flows are characterized by uncertain inputs such as random initial data, material and flux coefficients, and boundary conditions. The current volume addresses the pertinent issue of efficiently computing the flow uncertainty, given th