Transonic velocity fluctuations simulated using extremum diminishing uncertainty quantification based on inverse distance weighting

DOI10.1007/s00162-011-0233-yzbMath1294.76207OpenAlexW2027228028MaRDI QIDQ400485

Hester Bijl, Jeroen A. S. Witteveen

Publication date: 22 August 2014

Published in: Theoretical and Computational Fluid Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00162-011-0233-y

zbMATH Keywords

transonic flowuncertainty quantificationextremum diminishinginverse distance weighting

Mathematics Subject Classification ID

Transonic flows (76H05) Stochastic analysis applied to problems in fluid mechanics (76M35)

Related Items (1)

Mesh deformation based on artificial neural networks

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• An adaptive stochastic finite elements approach based on Newton-Cotes quadrature in simplex elements
• Multi-resolution analysis of Wiener-type uncertainty propagation schemes
• A mathematical introduction to fluid mechanics
• Effect of randomness on multi-frequency aeroelastic responses resolved by unsteady adaptive stochastic finite elements
• Higher-order time integration through smooth mesh deformation for 3D fluid-structure interaction simulations
• A local solution method for shock boundary-layer interaction on a swept wing
• Stochastic approaches to uncertainty quantification in CFD simulations
• Second-order time-accurate and geometrically conservative implicit schemes for flow computations on unstructured dynamic meshes
• A stochastic projection method for fluid flow. II: Random process
• Modeling uncertainty in flow simulations via generalized polynomial chaos.
• Ingredients for a general purpose stochastic finite elements implementation
• Dual-based a posteriori error estimate for stochastic finite element methods
• An adaptive multi-element generalized polynomial chaos method for stochastic differential equations
• Probabilistic collocation used in a Two-Step approached for efficient uncertainty quantification in computational fluid dynamics
• Uncertainty Quantification and Polynomial Chaos Techniques in Computational Fluid Dynamics
• A Monomial Chaos Approach for Efficient Uncertainty Quantification in Nonlinear Problems
• Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations
• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
• Positive schemes and shock modelling for compressible flows
• The Homogeneous Chaos
• A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data
• Solution of stochastic partial differential equations using Galerkin finite element techniques
• A stochastic projection method for fluid flow. I: Basic formulation

This page was built for publication: Transonic velocity fluctuations simulated using extremum diminishing uncertainty quantification based on inverse distance weighting