A painless intrusive polynomial chaos method with RANS-based applications

DOI10.1016/J.CMA.2019.01.018zbMath1440.76129OpenAlexW2913056193WikidataQ128454540 ScholiaQ128454540MaRDI QIDQ1987892

K.-D. Kantarakias, M. Chatzimanolakis, V. G. Asouti, Kyriakos C. Giannakoglou

Publication date: 16 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2019.01.018

zbMATH Keywords

Navier-Stokes equationsuncertainty quantificationintrusive polynomial chaos expansion

Mathematics Subject Classification ID

Computational methods for problems pertaining to probability theory (60-08) Navier-Stokes equations for incompressible viscous fluids (76D05) Stochastic analysis applied to problems in fluid mechanics (76M35)

Related Items (3)

Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification ⋮ The anisotropic graph neural network model with multiscale and nonlinear characteristic for turbulence simulation ⋮ Variance-based adaptive sequential sampling for polynomial chaos expansion

Cites Work

• Unnamed Item
• A flexible uncertainty quantification method for linearly coupled multi-physics systems
• CFD-based analysis and two-level aerodynamic optimization on graphics processing units
• Quasi-Monte Carlo integration
• Stochastic simulation: Algorithms and analysis
• A hybrid collocation/Galerkin scheme for convective heat transfer problems with stochastic boundary conditions
• Numerical Challenges in the Use of Polynomial Chaos Representations for Stochastic Processes
• Uncertainty propagation in CFD using polynomial chaos decomposition
• A stochastic projection method for fluid flow. I: Basic formulation

This page was built for publication: A painless intrusive polynomial chaos method with RANS-based applications