Gradient-based methods for uncertainty quantification in hypersonic flows

DOI10.1016/j.compfluid.2012.09.003zbMath1290.76055OpenAlexW2068761959MaRDI QIDQ2015969

Dimitri J. Mavriplis, Brian Lockwood

Publication date: 18 June 2014

Published in: Computers and Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.compfluid.2012.09.003

zbMATH Keywords

hypersonic flowadjoint methodsuncertainty quantificationgradient-based optimization

Mathematics Subject Classification ID

Hypersonic flows (76K05)

Related Items

Forward and backward uncertainty quantification with active subspaces: application to hypersonic flows around a cylinder, On polynomial chaos expansion via gradient-enhanced \(\ell_1\)-minimization, Sensitivity-enhanced generalized polynomial chaos for efficient uncertainty quantification, A gradient enhanced \(\ell_{1}\)-minimization for sparse approximation of polynomial chaos expansions, Enhancing sparsity of Hermite polynomial expansions by iterative rotations, Active-subspace analysis of exceedance probability for shallow-water waves, Compressive Hermite interpolation: sparse, high-dimensional approximation from gradient-augmented measurements

Uses Software

• L-BFGS-B
• LBFGS-B
• AUSM
• TAPENADE
• EGO
• FUN3D

Cites Work

• Adjoint-based error estimation and adaptive mesh refinement for the RANS and \(k-\omega\) turbulence model equations
• Efficient global optimization of expensive black-box functions
• A low-diffusion flux-splitting scheme for Navier-Stokes calculations
• Classic works on the Dempster-Shafer theory of belief functions
• A sequel to AUSM II: AUSM\(^+\)-up for all speeds
• Uncertainty Analysis Utilizing Gradient and Hessian Information
• On optimum design in fluid mechanics
• The origins of kriging
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