Towards in-situ vortex identification for peta-scale CFD using contour trees
File(s)
Accepted version
Author(s)
Koch, Marius K
Kelly, Paul HJ
Vincent, Peter E
Type
Conference Paper
Abstract
Turbulent flows exist in many fields of science and occur in a wide range of engineering applications. While in the past broad knowledge has been established regarding the statistical properties of turbulence at a range of Reynolds numbers, there is a lack of under-standing of the detailed structure of these flows. Since the physical processes involve a vast number of structures, extremely large data sets are required to fully resolve a flow field in both space and time. To make the analysis of such data sets possible, we propose a frame-work that uses state-of-the-art contour tree construction algorithms to identify, classify and track vortices in turbulent flow fields produced by large-scale high-fidelity massively-parallel computational fluid dynamics solvers such as PyFR. Since disk capacity and I/O have become a bottleneck for such large-scale simulations, the proposed framework will be applied in-situ, while relevant data is still in device memory.
Date Issued
2019-06-20
Date Acceptance
2018-10-21
Citation
2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV), 2019, pp.104-105
ISBN
9781538668740
Publisher
Institute of Electrical and Electronics Engineers
Start Page
104
End Page
105
Journal / Book Title
2018 IEEE 8th Symposium on Large Data Analysis and Visualization (LDAV)
Copyright Statement
© 2018 Institute of Electrical and Electronics Engineers.
Sponsor
The Leverhulme Trust
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000480379800017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
PLP-2016-196
EP/R030340/1
EP/R029423/1
Source
8th IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Computer Science
Applied Computing
Physical Sciences and Engineering
Physics; Applied Computing
Aerospace
Publication Status
Published
Start Date
2018-10-21
Finish Date
2018-10-21
Coverage Spatial
Berlin, Germany
Date Publish Online
2019-06-20