Uncertainty quantification in computational fluid dynamics / Hester Bijl, Didier Lucor, Siddhartha Mishra, Christoph Schwab, editors.

Contributor(s): Bijl, Hester [editor] | Lucor, Didier [editor] | Mishra, Siddhartha [editor] | Schwab, Christoph [editor]Material type: TextTextLanguage: English Series: Lecture notes in computational science and engineering ; 92Copyright date: New York : Springer , 2013Edition: First EditionDescription: xi, 333 pages : illustrations (some colors) ; 24 cmISBN: 9783319008844Subject(s): Computational fluid dynamics | Uncertainty -- Mathematical models | Astronautics | Computer science | Engineering mathematicsDDC classification: 620.1064
Partial contents:
Non-intrusive Uncertainty Propagation with Error Bounds for Conservation Laws Containing Discontinuities / Timothy Barth -- Uncertainty Quantification in Aeroelasticity / Philip Beran and Bret Stanford -- Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method / Bruno Després, Gaël Poëtte and Didier Lucor -- Adaptive Uncertainty Quantification for Computational Fluid Dynamics / Richard P. Dwight and Jeroen A.S. Witteveen -- Implementation of Intrusive Polynomial Chaos in CFD Codes and Application to 3D Navier-Stokes / Chris Lacor and Cristian Dinescu -- Multi-level Monte Carlo Finite Volume Methods for Uncertainty Quantification in Nonlinear Systems of Balance Laws / Siddhartha Mishra and Christoph Schwab -- Essentially Non-oscillatory Stencil Selection and Subcell Resolution in Uncertainty Quantification / Jeroen A.S. Witteveen and Gianluca Iaccarino.
Abstract: 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.
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Libro académico Libro académico Biblioteca del Campus
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Non-intrusive Uncertainty Propagation with Error Bounds for Conservation Laws Containing Discontinuities / Timothy Barth --
Uncertainty Quantification in Aeroelasticity / Philip Beran and Bret Stanford --
Robust Uncertainty Propagation in Systems of Conservation Laws with the Entropy Closure Method / Bruno Després, Gaël Poëtte and Didier Lucor --
Adaptive Uncertainty Quantification for Computational Fluid Dynamics / Richard P. Dwight and Jeroen A.S. Witteveen --
Implementation of Intrusive Polynomial Chaos in CFD Codes and Application to 3D Navier-Stokes / Chris Lacor and Cristian Dinescu --
Multi-level Monte Carlo Finite Volume Methods for Uncertainty Quantification in Nonlinear Systems of Balance Laws / Siddhartha Mishra and Christoph Schwab -- Essentially Non-oscillatory Stencil Selection and Subcell Resolution in Uncertainty Quantification / Jeroen A.S. Witteveen and Gianluca Iaccarino.

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.

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