Some Recent Developments in Turbulence Closure Modeling

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Turbulence Modeling in the Age of Data (2018, background): “kes equations such as convection and diusion; additional contributions and source terms are often included to represent known sensitivities, such as near wall dynamics, rotational corrections, etc. (Durbin 2017). Although it is possible to derive dierential models formally through repeated applicati”
Quantification of model uncertainty in RANS simulations: A review (2018, methodology): “Recent development of turbulence modeling in RANS, LES, and hybrid approaches has been reviewed by Durbin [11].”
Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows (2018, methodology): “…learning methods in the ﬁeld of computer vision [KSH12, IZZE16, KALL17], and ﬁrst success stories of applications in the area of physics simulations [TSSP16, XFCT18, BFM18, RYK18, BSHHB18], the corrsponding research communities retain a skeptical stance towards deep learning algorithms [Dur18].”