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Fernando F. Grinstein

Researcher at Los Alamos National Laboratory

Publications -  152
Citations -  7306

Fernando F. Grinstein is an academic researcher from Los Alamos National Laboratory. The author has contributed to research in topics: Turbulence & Large eddy simulation. The author has an hindex of 36, co-authored 152 publications receiving 6952 citations. Previous affiliations of Fernando F. Grinstein include United States Department of the Navy & United States Naval Research Laboratory.

Papers
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Journal ArticleDOI

New Insights into Large Eddy Simulation

TL;DR: In this paper, the authors proposed a monotone integrated large eddy simulation approach, which incorporates a form of turbulence modeling applicable when the large-scale flows of interest are intrinsically time dependent, thus throwing common statistical models into question.

New insights into large eddy simulation

TL;DR: In this paper, the authors proposed a monotone integrated large eddy simulation approach, which incorporates a form of turbulence modeling applicable when the large-scale flows of interest are intrinsically time dependent, thus throwing common statistical models into question.
BookDOI

Implicit large eddy simulation : computing turbulent fluid dynamics

TL;DR: In this article, a rationale for ILES for turbulent flows is presented, with a rationale based on physics with Numerics (PHN) with numerical regularization.
Journal ArticleDOI

Flow control with noncircular jets

TL;DR: Noncircular jets have been identified as an efficient technique of passive flow control that allows significant improvements of performance in various practical systems at a relatively low cost because noncircular jet rely solely on changes in the geometry of the nozzle as discussed by the authors.
Journal ArticleDOI

Monotonically integrated large eddy simulation of free shear flows

TL;DR: In this paper, a monotonically integrated large eddy simulation (MILES) approach is proposed, which involves solving the unfiltered Navier-Stokes equations (NSEs) using high-resolution monotone algorithms.