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contributor authorShobayo, Olalekan O.
contributor authorWalters, D. Keith
date accessioned2022-05-08T09:11:11Z
date available2022-05-08T09:11:11Z
date copyright2/23/2022 12:00:00 AM
date issued2022
identifier issn0098-2202
identifier otherfe_144_06_061107.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4284829
description abstractComputational fluid dynamics (CFD) results are presented for synthetic turbulence generation by a proposed statistically targeted forcing (STF) method. The new method seeks to introduce a fluctuating velocity field with a distribution of first and second moments that approximate a user-specified target mean velocity and Reynolds stress tensor, by incorporating deterministic time-dependent forcing terms into the momentum equation for the resolved flow. The STF method is formulated to extend the applicability of previously documented methods and provide flexibility in regions where synthetic turbulence needs to be generated or sustained, for use in engineering level large eddy and hybrid large eddy/Reynolds-averaged Navier–Stokes CFD simulations. The objective of this study is to evaluate the performance of the proposed STF method in large-eddy simulation (LES) simulations of isotropic and anisotropic homogeneous turbulent flow test cases. Results are interrogated and compared to target statistical velocity and turbulent stress distributions and evaluated in terms of energy spectra. Analysis of the influence of STF model parameters, mesh resolution, and LES subgrid stress model on the results is investigated. Results show that the new method can successfully reproduce desired statistical distributions in a homogeneous turbulent flow.
publisherThe American Society of Mechanical Engineers (ASME)
titleEvaluation of a Statistically Targeted Forcing Method for Synthetic Turbulence Generation in Scale-Resolving Simulations
typeJournal Paper
journal volume144
journal issue6
journal titleJournal of Fluids Engineering
identifier doi10.1115/1.4053551
journal fristpage61107-1
journal lastpage61107-13
page13
treeJournal of Fluids Engineering:;2022:;volume( 144 ):;issue: 006
contenttypeFulltext


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