Uncertainty Quantification in Computational Fluid Dynamics

Uncertainty Quantification in Computational Fluid Dynamics

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Fluid 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 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 methods and modified versions of the stochastic collocation methods that use adaptive stencil selection of the ENO-WENO type in both physical and stochastic space. The methods are also complemented by concrete applications such as flows around aerofoils and rockets, problems of aeroelasticity (fluid-structure interactions), and shallow water flows for propagating water waves. The wealth of numerical examples provide evidence on the suitability of each proposed method as well as comparisons of different approaches.Cavity. Flow. The second test case is a lid driven cavity flow. The geometry is a square box 0 alt; x;y alt; 1 of which the upper wall (y D 1) is moving with a velocity U D ... Implementation of Intrusive Polynomial Chaos in CFD Codes and Application.

Title:Uncertainty Quantification in Computational Fluid Dynamics
Author: Hester Bijl, Didier Lucor, Siddhartha Mishra, Christoph Schwab
Publisher:Springer Science & Business Media - 2013-09-20

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