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Dataset for 'The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties'

Dataset for 'The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties'
Dataset for 'The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties'
Data for the publication "The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties" by Qian-Cheng Wang, Zhen-Guo Wang, Ming-Bo Sun, Rui Yang, Yu-Xin Zhao and Zhiwei Hu published by Journal of Fluid Mechanics Vol. 863, March 2019, pp 454-493.
streamline concave curvature, Turbulent boundary layer, Supersonic flow, Direct numerical simulation, VLSM
University of Southampton
Hu, Zhiwei
dd985844-1e6b-44ba-9e1d-fa57c6c88d65
Hu, Zhiwei
dd985844-1e6b-44ba-9e1d-fa57c6c88d65

Hu, Zhiwei (2019) Dataset for 'The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties'. University of Southampton doi:10.5258/SOTON/D0836 [Dataset]

Record type: Dataset

Abstract

Data for the publication "The amplification of large-scale motion in a supersonic concave turbulent boundary layer and its impact on the mean and statistical properties" by Qian-Cheng Wang, Zhen-Guo Wang, Ming-Bo Sun, Rui Yang, Yu-Xin Zhao and Zhiwei Hu published by Journal of Fluid Mechanics Vol. 863, March 2019, pp 454-493.

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08_DataFile.zip - Dataset
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Text
readmeWangJFM.RTF - Text
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More information

Published date: 1 March 2019
Keywords: streamline concave curvature, Turbulent boundary layer, Supersonic flow, Direct numerical simulation, VLSM

Identifiers

Local EPrints ID: 428845
URI: http://eprints.soton.ac.uk/id/eprint/428845
PURE UUID: 1788b09a-00ad-4b99-b355-902a11dd869d

Catalogue record

Date deposited: 11 Mar 2019 17:31
Last modified: 12 Nov 2023 14:36

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