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.
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Large-eddy simulation of swirling particle-laden flows in a coaxial-jet combustor
TL;DR: In this article, a large-eddy simulation of particle-laden, swirling flow in a coaxial-jet combustor is performed, where a mixture of air and lightly loaded, spherical, glass-particles with a prescribed size distribution are treated as point sources and influence the gas phase only through momentum exchange terms.
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On the coherent drag-reducing and turbulence-enhancing behaviour of polymers in wall flows
Yves Dubief,Christopher White,Vincent Terrapon,Eric S. G. Shaqfeh,Parviz Moin,Sanjiva K. Lele +5 more
TL;DR: In this paper, the authors used numerical simulations of turbulent polymer solutions using the FENE-P model to characterize the action of polymers on turbulence in drag-reduced flows and found that polymers are found to store and to release energy to the flow in a well-organized manner.
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Interaction of isotropic turbulence with shock waves: effect of shock strength
TL;DR: In this article, a shock-capturing scheme was developed to accurately simulate the unsteady interaction of vortical turbulence with shock waves, and an existing controversy between experiments and theoretical predictions on length scale change was thoroughly investigated through the shockcapturing simulation.
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Erratum: ''A dynamic subgrid-scale eddy viscosity model'' [Phys. Fluids A 3, 1760 (1991)]
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An improvement of fractional step methods for the incompressible Navier-Stokes equations
Hung Le,Parviz Moin +1 more
TL;DR: In this paper, a numerical method for computing three-dimensional, unsteady incompressible flows is presented, which is a predictor-corrector technique combined with a fractional step method.