POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps

A new application of proper orthogonal decomposition (POD) to uncover the relation of the instantaneous energetic large-scale turbulent structures to the dominant POD modes had been reported. Motivated on this method, the data mining from the velocity vector fields measured by the particle image vel...

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Main Authors: Yang, Shaoqiong, Wu, Yanhua, Song, Yang, Wang, Yanhui, Yang, Ming
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2019
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Online Access:https://hdl.handle.net/10356/105992
http://hdl.handle.net/10220/48867
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1059922023-03-04T17:18:04Z POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps Yang, Shaoqiong Wu, Yanhua Song, Yang Wang, Yanhui Yang, Ming School of Mechanical and Aerospace Engineering Proper Orthogonal Decomposition Data Mining DRNTU::Engineering::Mechanical engineering A new application of proper orthogonal decomposition (POD) to uncover the relation of the instantaneous energetic large-scale turbulent structures to the dominant POD modes had been reported. Motivated on this method, the data mining from the velocity vector fields measured by the particle image velocimetry and structural analysis on the selected POD-based reconstructed turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps (FFSs) has been performed. The velocity fields containing the most energetic large-scale structures are conditionally chosen. The conditional criterion is that the chosen velocity fields whose POD temporal coefficients of the first or second mode are correspondingly larger than twice of their root mean square values. Typically, the most energetic instantaneous structures are the large-scale second-quadrant (Q2) or fourth-quadrant (Q4) events and the mostly open separation bubbles in front of these FFSs; while the large-scale structures behave as a strong shear layer, and near or in which are a few spanwise prograde or retrograde vortices; and sometimes the alternating node and saddle points appeared. Similarly, for the flow on top of the FFSs, the most energetic structures are presented as a great many large-scale Q4 or Q2 events and a few secondary vortices at the very near wall; while the large-scale structures are overall exhibited as a strong shear layer, and in which a large number of vertex structures are created. These energetic large-scale structures here are not only sensitive to the surface roughness conditions but also to the spanwise locations. MOE (Min. of Education, S’pore) Published version 2019-06-20T04:57:08Z 2019-12-06T22:02:25Z 2019-06-20T04:57:08Z 2019-12-06T22:02:25Z 2019 Journal Article Yang, S., Wang, Y., Yang, M., Song, Y., & Wu, Y. (2019). POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps. IEEE Access, 7, 18234-18255. doi:10.1109/ACCESS.2019.2894715 https://hdl.handle.net/10356/105992 http://hdl.handle.net/10220/48867 10.1109/ACCESS.2019.2894715 en IEEE Access © 2019 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information 22 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Proper Orthogonal Decomposition
Data Mining
DRNTU::Engineering::Mechanical engineering
spellingShingle Proper Orthogonal Decomposition
Data Mining
DRNTU::Engineering::Mechanical engineering
Yang, Shaoqiong
Wu, Yanhua
Song, Yang
Wang, Yanhui
Yang, Ming
POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
description A new application of proper orthogonal decomposition (POD) to uncover the relation of the instantaneous energetic large-scale turbulent structures to the dominant POD modes had been reported. Motivated on this method, the data mining from the velocity vector fields measured by the particle image velocimetry and structural analysis on the selected POD-based reconstructed turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps (FFSs) has been performed. The velocity fields containing the most energetic large-scale structures are conditionally chosen. The conditional criterion is that the chosen velocity fields whose POD temporal coefficients of the first or second mode are correspondingly larger than twice of their root mean square values. Typically, the most energetic instantaneous structures are the large-scale second-quadrant (Q2) or fourth-quadrant (Q4) events and the mostly open separation bubbles in front of these FFSs; while the large-scale structures behave as a strong shear layer, and near or in which are a few spanwise prograde or retrograde vortices; and sometimes the alternating node and saddle points appeared. Similarly, for the flow on top of the FFSs, the most energetic structures are presented as a great many large-scale Q4 or Q2 events and a few secondary vortices at the very near wall; while the large-scale structures are overall exhibited as a strong shear layer, and in which a large number of vertex structures are created. These energetic large-scale structures here are not only sensitive to the surface roughness conditions but also to the spanwise locations.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Yang, Shaoqiong
Wu, Yanhua
Song, Yang
Wang, Yanhui
Yang, Ming
format Article
author Yang, Shaoqiong
Wu, Yanhua
Song, Yang
Wang, Yanhui
Yang, Ming
author_sort Yang, Shaoqiong
title POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
title_short POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
title_full POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
title_fullStr POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
title_full_unstemmed POD-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
title_sort pod-based data mining of turbulent flows in front of and on top of smooth and roughness-resolved forward-facing steps
publishDate 2019
url https://hdl.handle.net/10356/105992
http://hdl.handle.net/10220/48867
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