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Visualization of Input Parameters for Stream and Pathline Seeding

Tony McLoughlin, Matt Edmunds, Chao Tong, Robert S, Ian Masters Orcid Logo, Guoning Chen, Nelson Max, Harry Yeh, Bob Laramee Orcid Logo

International Journal of Advanced Computer Science and Applications, Volume: 6, Issue: 4

Swansea University Authors: Ian Masters Orcid Logo, Bob Laramee Orcid Logo

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DOI (Published version): 10.14569/IJACSA.2015.060417

Abstract

Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to gene...

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Published in: International Journal of Advanced Computer Science and Applications
Published: 2015
URI: https://cronfa.swan.ac.uk/Record/cronfa23190
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In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualiza-tion primitives to data. Our work is aimed at another source of uncertainty - that associated with user-controlled input param-eters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. 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spelling 2019-05-30T11:50:25.3238351 v2 23190 2015-09-15 Visualization of Input Parameters for Stream and Pathline Seeding 6fa19551092853928cde0e6d5fac48a1 0000-0001-7667-6670 Ian Masters Ian Masters true false 7737f06e2186278a925f6119c48db8b1 0000-0002-3874-6145 Bob Laramee Bob Laramee true false 2015-09-15 MECH Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualiza-tion primitives to data. Our work is aimed at another source of uncertainty - that associated with user-controlled input param-eters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. The reaction from a domain expert in fluid dynamics is also reported. - See more at: http://thesai.org/Publications/ViewPaper?Volume=6&Issue=4&Code=IJACSA&SerialNo=17#sthash.PNlUBslJ.dpuf Journal Article International Journal of Advanced Computer Science and Applications 6 4 31 12 2015 2015-12-31 10.14569/IJACSA.2015.060417 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2019-05-30T11:50:25.3238351 2015-09-15T14:29:30.2530741 College of Engineering Engineering Tony McLoughlin 1 Matt Edmunds 2 Chao Tong 3 Robert S 4 Ian Masters 0000-0001-7667-6670 5 Guoning Chen 6 Nelson Max 7 Harry Yeh 8 Bob Laramee 0000-0002-3874-6145 9 0023190-15092015143006.pdf IJACSA2015_McLoughlin_Vis_Input_Param4StreamPathSeeding.pdf 2015-09-15T14:30:06.1330000 Output 4243991 application/pdf Version of Record true 2015-09-15T00:00:00.0000000 Distributed under the terms of a Creative Commons Attribution (CC-BY-4.0) true
title Visualization of Input Parameters for Stream and Pathline Seeding
spellingShingle Visualization of Input Parameters for Stream and Pathline Seeding
Ian Masters
Bob Laramee
title_short Visualization of Input Parameters for Stream and Pathline Seeding
title_full Visualization of Input Parameters for Stream and Pathline Seeding
title_fullStr Visualization of Input Parameters for Stream and Pathline Seeding
title_full_unstemmed Visualization of Input Parameters for Stream and Pathline Seeding
title_sort Visualization of Input Parameters for Stream and Pathline Seeding
author_id_str_mv 6fa19551092853928cde0e6d5fac48a1
7737f06e2186278a925f6119c48db8b1
author_id_fullname_str_mv 6fa19551092853928cde0e6d5fac48a1_***_Ian Masters
7737f06e2186278a925f6119c48db8b1_***_Bob Laramee
author Ian Masters
Bob Laramee
author2 Tony McLoughlin
Matt Edmunds
Chao Tong
Robert S
Ian Masters
Guoning Chen
Nelson Max
Harry Yeh
Bob Laramee
format Journal article
container_title International Journal of Advanced Computer Science and Applications
container_volume 6
container_issue 4
publishDate 2015
institution Swansea University
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description Uncertainty arises in all stages of the visualization pipeline. However, the majority of flow visualization applications convey no uncertainty information to the user. In tools where uncertainty is conveyed, the focus is generally on data, such as error that stems from numerical methods used to generate a simulation or on uncertainty associated with mapping visualiza-tion primitives to data. Our work is aimed at another source of uncertainty - that associated with user-controlled input param-eters. The navigation and stability analysis of user-parameters has received increasing attention recently. This work presents an investigation of this topic for flow visualization, specifically for three-dimensional streamline and pathline seeding. From a dynamical systems point of view, seeding can be formulated as a predictability problem based on an initial condition. Small perturbations in the initial value may result in large changes in the streamline in regions of high unpredictability. Analyzing this predictability quantifies the perturbation a trajectory is subjugated to by the flow. In other words, some predictions are less certain than others as a function of initial conditions. We introduce novel techniques to visualize important user input parameters such as streamline and pathline seeding position in both space and time, seeding rake position and orientation, and inter-seed spacing. The implementation is based on a metric which quantifies similarity between stream and pathlines. This is important for Computational Fluid Dynamics (CFD) engineers as, even with the variety of seeding strategies available, manual seeding using a rake is ubiquitous. We present methods to quantify and visualize the effects that changes in user-controlled input parameters have on the resulting stream and pathlines. We also present various visualizations to help CFD scientists to intuitively and effectively navigate this parameter space. The reaction from a domain expert in fluid dynamics is also reported. - See more at: http://thesai.org/Publications/ViewPaper?Volume=6&Issue=4&Code=IJACSA&SerialNo=17#sthash.PNlUBslJ.dpuf
published_date 2015-12-31T03:33:40Z
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