A DNS/URANS approach for simulating rough-wall turbulent flows
A DNS/URANS approach for simulating rough-wall turbulent flows
A novel hybrid method combining direct numerical simulation (DNS) and the Reynolds-averaged Navier Stokes (RANS), denoted as a stress-blended method (SBM), has been developed. The SBM is targeted at simulating turbulent flows over arbitrary rough surfaces in which computational savings can be achieved by making the DNS domain as small as possible. Within the SBM framework, a RANS model is enforced above the roughness layer to prevent the momentum build-up which arises in simulations where the computational domain is too small to represent the largest eddies. The SBM is validated for turbulent channel flow, both for smooth wall turbulence and using a parametric forcing approach to mimic roughness effects, with a computational cost that scales linearly with Reτ. The method is then applied to selected subsets of a scanned grit-blasted surface. For the same subset, the roughness function is found to be within 1% of available DNS. Comparisons of small and large subsets showed differences of over a factor of two in equivalent sand grain roughness, indicating the importance of choosing representative surface samples. Simulations in the fully rough regime are carried out using one to two orders of magnitude fewer points than in a typical DNS. Since no assumptions on the roughness properties or the flow structure (such as outer layer similarity) are made, we expect the SBM to be applicable to non-equilibrium turbulent boundary layer flows.
hydrodynamic roughness, turbulent flow, direct numerical simulation
Alves Portela, Felipe
111319ab-31b1-4fbe-9afd-49958c6d9c4c
Sandham, Neil
0024d8cd-c788-4811-a470-57934fbdcf97
1 October 2020
Alves Portela, Felipe
111319ab-31b1-4fbe-9afd-49958c6d9c4c
Sandham, Neil
0024d8cd-c788-4811-a470-57934fbdcf97
Alves Portela, Felipe and Sandham, Neil
(2020)
A DNS/URANS approach for simulating rough-wall turbulent flows.
International Journal of Heat and Fluid Flow, [108627].
(doi:10.1016/j.ijheatfluidflow.2020.108627).
Abstract
A novel hybrid method combining direct numerical simulation (DNS) and the Reynolds-averaged Navier Stokes (RANS), denoted as a stress-blended method (SBM), has been developed. The SBM is targeted at simulating turbulent flows over arbitrary rough surfaces in which computational savings can be achieved by making the DNS domain as small as possible. Within the SBM framework, a RANS model is enforced above the roughness layer to prevent the momentum build-up which arises in simulations where the computational domain is too small to represent the largest eddies. The SBM is validated for turbulent channel flow, both for smooth wall turbulence and using a parametric forcing approach to mimic roughness effects, with a computational cost that scales linearly with Reτ. The method is then applied to selected subsets of a scanned grit-blasted surface. For the same subset, the roughness function is found to be within 1% of available DNS. Comparisons of small and large subsets showed differences of over a factor of two in equivalent sand grain roughness, indicating the importance of choosing representative surface samples. Simulations in the fully rough regime are carried out using one to two orders of magnitude fewer points than in a typical DNS. Since no assumptions on the roughness properties or the flow structure (such as outer layer similarity) are made, we expect the SBM to be applicable to non-equilibrium turbulent boundary layer flows.
Text
Alves Portela IJHFF19 accepted
- Accepted Manuscript
More information
Accepted/In Press date: 14 May 2020
e-pub ahead of print date: 3 July 2020
Published date: 1 October 2020
Keywords:
hydrodynamic roughness, turbulent flow, direct numerical simulation
Identifiers
Local EPrints ID: 440927
URI: http://eprints.soton.ac.uk/id/eprint/440927
ISSN: 0142-727X
PURE UUID: c1b89157-4675-40ae-9224-c02ff91e116a
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Date deposited: 22 May 2020 16:40
Last modified: 17 Mar 2024 05:35
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Contributors
Author:
Felipe Alves Portela
Author:
Neil Sandham
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