An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows

By S. Wallin, Arne V. Johansson. Journal of Fluid Mechanics, 2000

798 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/e1bc08ae342af38b0a934063b6b06aa588142142

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Citations in the corpus

The Scale-Adaptive Simulation Method for Unsteady Turbulent Flow Predictions. Part 1: Theory and Model Description (2010, background): “, [12]) or Explicit Algebraic Reynolds Stress Models (EARSM) (Pope, [26], Rodi, [27], Gatski and Speziale, [9], Wallin and Johansson, [36]) or non-linear stressstrain models (Craft et al.”
Evaluation of linear and nonlinear convection schemes on multidimensional non‐orthogonal grids with applications to KVLCC2 tanker (2009, methodology): “utilizing either a two equation closure based on Menter’s [39] blended k−ε/k− (BKW) or the explicit algebraic Reynolds stress (ARS) [40] which is modeled with an additional term aex i j k added to the right-hand side of Equation (3).”
The Onera elsA CFD software: input from research and feedback from industry (2013, methodology): “The turbulence closure is no more based on the Boussinesq assumption as for the basic (k, kl) model, but on a non-linear relation between the Reynolds tensor and the mean velocity gradients through the Wallin-Johansson expression of the anisotropy tensor [33].”
Turbulence modelling of the flow past a pitching NACA0012 airfoil at 105 and 106 Reynolds numbers (2008, methodology): “A slight improvement can be achieved by using non-linear eddy viscosity modelling (NLEVM), explicit algebraic Reynolds stress model [EARSM, Wallin and Johansson (2000)] or by calibrating a one-equation model based on the eddy viscosity transport equation of Spalart and Allmaras (1992) model,…”
Coherent structures in a swirl injector at Re = 4800 by nonlinear simulations and linear global modes (2016, methodology): “For swirling flows, URANS models generally struggle to predict the mean flow swirl profile accurately (Wallin & Johansson 2000; Dunham et al. 2008), whereas the mean swirl profile is crucial for vortex breakdown instabilities as seen above.”
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016, methodology): “Interestingly, even the advanced RANS models such as Reynolds stress transport models [8] and Explicit Algebraic Reynolds stress models [9] have not seen much development in the past few decades.”
Numerical modelling of complex turbulent free‐surface flows with the SPH method: an overview (2007, methodology): “Solution to (60), discussed in [26], becomes linear in S for large strain in the particular case of a shear flow (i.”