P
Parviz Moin
Researcher at Stanford University
Publications - 495
Citations - 66028
Parviz Moin is an academic researcher from Stanford University. The author has contributed to research in topics: Turbulence & Large eddy simulation. The author has an hindex of 116, co-authored 473 publications receiving 60521 citations. Previous affiliations of Parviz Moin include Center for Turbulence Research & Ames Research Center.
Papers
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A dynamic subgrid-scale eddy viscosity model
TL;DR: In this article, a new eddy viscosity model is presented which alleviates many of the drawbacks of the existing subgrid-scale stress models, such as the inability to represent correctly with a single universal constant different turbulent field in rotating or sheared flows, near solid walls, or in transitional regimes.
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Direct numerical simulation of turbulent flow over riblets
TL;DR: In this article, a drag reduction mechanism by riblets with small spacings was proposed to reduce viscous drag by restricting the location of the streamwise vortices above the wetted surface.
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Numerical studies of flow over a circular cylinder at ReD=3900
Arthur G. Kravchenko,Parviz Moin +1 more
TL;DR: In this paper, a high-order accurate numerical method based on B-splines and compared with previous upwindbiased and central finite-difference simulations and with the existing experimental data is presented.
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Grid-point requirements for large eddy simulation: Chapman’s estimates revisited
Haecheon Choi,Parviz Moin +1 more
TL;DR: In this paper, the authors modified the resolution requirements for large eddy simulation (LES) using accurate formulae for high Reynolds number boundary layer flow and showed that the number of grid points required for wall-modeled LES is proportional to ReLx, where Lx is the flat-plate length in the streamwise direction.
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On the Effect of Numerical Errors in Large Eddy Simulations of Turbulent Flows
Arthur G. Kravchenko,Parviz Moin +1 more
TL;DR: In this paper, it was shown that discrepancies between the results of dealiased spectral and standard nondialiased finite-difference methods are due to both aliasing and truncation errors with the latter being the leading source of differences.