A data-driven adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for turbulent flow

DOI10.1016/J.JCP.2017.05.009zbMath1378.76034OpenAlexW2613918815MaRDI QIDQ1692004

Peng Zhang

Publication date: 26 January 2018

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2017.05.009

zbMATH Keywords

Reynolds-averaged Navier-Stokes equationsturbulenceadaptive\(k\)-\(\omega\) turbulence equationsdata-driven computational model

Mathematics Subject Classification ID

Robustness and adaptive procedures (parametric inference) (62F35) (k)-(varepsilon) modeling in turbulence (76F60)

Related Items (6)

Comparison of different data-assimilation approaches to augment RANS turbulence models ⋮ Retrospective cost adaptive Reynolds-averaged Navier-Stokes \(k\)-\(\omega\) model for data-driven unsteady turbulent simulations ⋮ Sharp interface approaches and deep learning techniques for multiphase flows ⋮ Small-scale reconstruction in three-dimensional Kolmogorov flows using four-dimensional variational data assimilation ⋮ RANS closures for non-neutral microscale CFD simulations sustained with inflow conditions acquired from mesoscale simulations ⋮ Efficient assimilation of sparse data into RANS-based turbulent flow simulations using a discrete adjoint method

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