Geurts, BJ, Rouhi, A ORCID: https://orcid.org/0000-0002-7837-418X and Piomelli, U, 2019. Recent progress on reliability assessment of large-eddy simulation. Journal of Fluids and Structures, 91: 102615. ISSN 0889-9746
Preview |
Text
1378990_Rouhi2.pdf - Post-print Download (3MB) | Preview |
Abstract
Reliability assessment of large-eddy simulation (LES) of turbulent flows requires consideration of errors due to shortcomings in the modeling of sub-filter scale dynamics and due to discretization of the governing filtered Navier–Stokes equations. The Integral Length-Scale Approximation (ILSA) model is a pioneering sub-filter parameterization that incorporates both these contributions to the total simulation error, and provides user control over the desired accuracy of a simulation. It combines an imposed target for the ‘sub-filter activity’ and a flow-specific length-scale definition to achieve LES predictions with pre-defined fidelity level. The performance of the ‘global’ and the ‘local’ formulations of ILSA, implemented as eddy-viscosity models, for turbulent channel flow and for separated turbulent flow over a backward-facing step are investigated here. We show excellent agreement with reference direct numerical simulations, with experimental data and with predictions based on other, well-established sub-filter models. The computational overhead is found to be close to that of a basic Smagorinsky sub-filter model.
Item Type: | Journal article |
---|---|
Publication Title: | Journal of Fluids and Structures |
Creators: | Geurts, B.J., Rouhi, A. and Piomelli, U. |
Publisher: | Elsevier |
Date: | November 2019 |
Volume: | 91 |
ISSN: | 0889-9746 |
Identifiers: | Number Type 10.1016/j.jfluidstructs.2019.03.008 DOI S0889974618307333 Publisher Item Identifier 1378990 Other |
Divisions: | Schools > School of Science and Technology |
Record created by: | Linda Sullivan |
Date Added: | 28 Oct 2020 10:55 |
Last Modified: | 31 May 2021 15:03 |
URI: | https://irep.ntu.ac.uk/id/eprint/41419 |
Actions (login required)
Edit View |
Statistics
Views
Views per month over past year
Downloads
Downloads per month over past year