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Published October 25, 2019 | public
Journal Article

Turbulent shear-layer mixing: initial conditions, and direct-numerical and large-eddy simulations

Abstract

Aspects of turbulent shear-layer mixing are investigated over a range of shear-layer Reynolds numbers, Re_δ = ΔUδ/ν, based on the shear-layer free-stream velocity difference, ΔU, and mixing-zone thickness, δ, to probe the role of initial conditions in mixing stages and the evolution of the scalar-field probability density function (p.d.f.) and variance. Scalar transport is calculated for unity Schmidt numbers, approximating gas-phase diffusion. The study is based on direct-numerical simulation (DNS) and large-eddy simulation (LES), comparing different subgrid-scale (SGS) models for incompressible, uniform-density, temporally evolving forced shear-layer flows. Moderate-Reynolds-number DNS results help assess and validate LES SGS models in terms of scalar-spectrum and mixing estimates, as well as other metrics, to R_eδ ≲ 3.3×10^4. High-Reynolds-number LES investigations to R_eδ ≲ 5×10^5 help identify flow parameters and conditions that influence the evolution of scalar variance and p.d.f., e.g. marching versus non-marching. Initial conditions that generate shear flows with different mixing behaviour elucidate flow characteristics in each flow regime and identify elements that induce p.d.f. transition and scalar-variance behaviour. P.d.f. transition is found to be largely insensitive to local flow parameters, such as Re_δ, or a previously proposed vortex-pairing parameter based on downstream distance, or other equivalent criteria. The present study also allows a quantitative comparison of LES SGS models in moderate- and high-Re_δ forced shear-layer flows.

Additional Information

© 2019 Cambridge University Press. Received 26 October 2018; revised 17 July 2019; accepted 17 July 2019; first published online 19 August 2019. This work was supported by the Department of Energy, National Nuclear Security Administration (DOE/NNSA), Award Number DE-NA0002382, the AFOSR Grant FA9550-12-1-0461 and the John K. Northrop Chair of the California Institute of Technology, with additional computational equipment support by the NSF MRI Grant EIA-0079871 and the AFOSR DURIP Grant FA9550-10-1-0553. This research also used resources of the Argonne Leadership Computing Facility (ALCF), which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. The 2048^3 DNS and all LES described in this paper were performed on the 512-core Lykos-Skepsis GALCIT cluster developed by Dr D. Lang under DOE support. Additional support and assistance by Dr D. Lang, Dr R. Balakrishnan and the ALCF technical support staff with computational resources used in this study is gratefully acknowledged.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023