Reynolds number dependence of mean flow structure in square duct turbulence

By A. Pinelli, Markus Uhlmann, A. Sekimoto, Genta Kawahara. Journal of Fluid Mechanics, 2010

195 citations recorded by the source corpus.

Subfields: Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/946315d598b95bbd3a5e0265945dc8c17a17c31d

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In Computational Mechanics

In Environmental Engineering

Citations in the corpus

Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned (2018, unknown): “We further study the model conditioning of RANS equations for more complex flows including the flow in a square duct at Reb = 3500 (Pinelli et al. 2010) and the flow over periodic hills atReb = 5600 (Breuer et al.”
Reynolds averaged turbulence modelling using deep neural networks with embedded invariance (2016, methodology): “Six cases were used for training: duct flow at Reb = 3500 (Pinelli et al. 2010), channel flow at Reτ = 590 (Moser, Kim & Mansour 1999), a perpendicular jet in cross-flow (Ruiz, Oefelein & Lacaze 2015), an inclined jet in cross-flow (Ling et al. 2016c), flow around a square cylinder (Ray et al.…”
Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data (2016, methodology): “The training data are obtained from DNS simulations [48] of the duct ﬂows in the same geometry but at lower Reynolds numbers The baseline RANS simulations are performed for all training and test ﬂows.”
Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks (2018, unknown): “[55] Gopalan et al.”
The Significance of Simple Invariant Solutions in Turbulent Flows (2011, background): “Recent vortex eduction studies based on DNS data have shown that the mean secondary flow can be linked to a non-homogeneous probability distribution of coherent flow structures along the duct perimeter (Pinelli et al. 2010, Uhlmann et al. 2007).”
Aspect ratio effects in turbulent duct flows studied through direct numerical simulation (2014, methodology): “bulence intensity profiles from several studies [13,19,20] and the simulations presented here are compared in Figure 20, both at the duct centreplane and close to the corner (z = 0.”