Proper orthogonal decomposition closure models for turbulent flows: A numerical comparison

By Zhu Wang, Imran Akhtar, Jeff Borggaard, Traian Iliescu. 2011

359 citations recorded by the source corpus.

This paper puts forth two new closure models for the proper orthogonal decomposition reduced-order modeling of structurally dominated turbulent flows: the dynamic subgrid-scale model and the variational multiscale model. These models, which are considered state-of-the-art in large eddy simulation, together with the mixing length and the Smagorinsky closure models, are tested in the numerical simulation of 3D turbulent flow past a circular cylinder at Re=1000. Five criteria are used to judge the performance of the proper orthogonal decomposition reduced-order models: the kinetic energy spectrum, the mean velocity, the Reynolds stresses, the root mean square values of the velocity fluctuations, and the time evolution of the POD coefficients. All the numerical results are benchmarked against a direct numerical simulation. Based on these numerical results, we conclude that the dynamic subgrid-scale and the variational multiscale models are the most accurate.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d15eec1b68ee2c1b9f0c9b3eeb078e48f9559b65

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Citations in the corpus

Model Order Reduction in Fluid Dynamics: Challenges and Perspectives (2014, methodology): “It was first introduced in [83] in the context of fluid dynamics as a method for discerning and analyzing coherent structures in experimental turbulent flows, and more recently in direct numerical simulations of turbulent flows in [53, 126], where also the concept of space-time windowing of POD has ”
POD-Galerkin method for finite volume approximation of Navier–Stokes and RANS equations (2016, methodology): “The POD-FV-ROM procedure is described starting from the modifications to the classic POD-G-ROM that should be adopted in a FV framework.”
On the need for a nonlinear subscale turbulence term in POD models as exemplified for a high-Reynolds-number flow over an Ahmed body (2013, methodology): “Nonlinear models based on the Galerkin projection of filtered Navier-Stokes equations have been pursued by Wang et al. (2011, 2012).”
Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders (2020, background): “In order to reduce costs, there has recently been significant interest in the reduced-order modeling (ROM) of such systems [1, 2, 3, 4, 5, 6, 7].”