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Stochastic and non-stochastic explicit algebraic models for les
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0002-3173-7502
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW. KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Turbulence.ORCID iD: 0000-0002-9819-2906
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0001-8692-0956
KTH, School of Engineering Sciences (SCI), Centres, Linné Flow Center, FLOW.ORCID iD: 0000-0002-2711-4687
2011 (English)In: 7th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2011, 2011Conference paper, Published paper (Refereed)
Abstract [en]

This paper consists of three parts. In the first part, we demonstrate the performance of the explicit algebraic (EA) subgrid-scale (SGS) stress model at Reτ = 934 and Reτ = 2003, based on friction velocity and channel half-width, for the case of large eddy simulation (LES) of turbulent channel flow. Performance of the EA model is compared to that of the dynamic Smagorinsky (DS) model for four different coarse resolutions and statistics are compared to the DNS of del Álamo & Jiménez (2003) and Hoyas & Jiménez (2008). Mean velocity profiles and Reynolds stresses are presented for the different cases. The EA model predictions are found to be reasonably close to the DNS profiles at all resolutions, while the DS model predictions are only in agreement at the finest resolution. The EA model predictions are found to be less resolution dependent than those with the DS model at both Reynolds numbers. In the second and third parts, we use Langevin stochastic differential equations to extend the EA model with stochastic contributions for SGS stresses and scalar fluxes. LES of turbulent channel flow at Reτ = 590, including a passive scalar, is carried out using the stochastic EA (SEA) models and the results are compared to the EA model predictions as well as DNS data. Investigations, show that the SEA model provides for a reasonable amount of backscatter of energy both for velocity and scalar, while the EA models do not provide for backscatter. The SEA model also improves the variance and length-scale of the SGS dissipation for velocity and scalar. However, the resolved statistics like the mean velocity, temperature, Reynolds stresses and scalar fluxes are hardly affected by the inclusion of the stochastic terms.

Place, publisher, year, edition, pages
2011.
Keywords [en]
Algebra, Backscattering, Channel flow, Differential equations, Forecasting, Large eddy simulation, Reynolds number, Shear flow, Stochastic systems, Turbulence, Velocity, Explicit algebraic models, Friction velocity, Mean velocities, Mean velocity profiles, Model prediction, Stochastic differential equations, Subgrid-scale stress models, Turbulent channel flows, Stochastic models
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-308782Scopus ID: 2-s2.0-84886278126OAI: oai:DiVA.org:kth-308782DiVA, id: diva2:1637254
Conference
7th International Symposium on Turbulence and Shear Flow Phenomena, TSFP 2011, Ottowa, Canada, Jul 28-31, 2011
Note

QC 20220212

Available from: 2022-02-12 Created: 2022-02-12 Last updated: 2022-06-25Bibliographically approved

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Rasam, AminBrethouwer, GertWallin, StefanJohansson, Arne V.

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