11268

FlowTour: An Automatic Guide for Exploring Internal Flow Features

Jun Ma, James Walker, Chaoli Wang, Scott A. Kuhl, Ching-Kuang Shene
Michigan Technological University
IEEE Pacific Visualization Symposium, 2014
@article{taoflow2014string,

   title={FlowString: Partial Streamline Matching Using Shape Invariant Similarity Measure for Exploratory Flow Visualization},

   author={Tao, Jun and Wang, Chaoli and Shene, Ching-Kuang},

   year={2014}

}

Download Download (PDF)   View View   Source Source   

329

views

We present FlowTour, a novel framework that provides an automatic guide for exploring internal flow features. Our algorithm first identifies critical regions and extracts their skeletons for feature characterization and streamline placement. We then create candidate viewpoints based on the construction of a simplified mesh enclosing each critical region and select best viewpoints based on a viewpoint quality measure. Finally, we design a tour that traverses all selected viewpoints in a smooth and efficient manner for visual navigation and exploration of the flow field. Unlike most existing works which only consider external viewpoints, a unique contribution of our work is that we also incorporate internal viewpoints to enable a clear observation of what lies inside of the flow field. Our algorithm is thus particularly useful for exploring hidden or occluded flow features in a large and complex flow field. We demonstrate our algorithm with several flow data sets and perform a user study to confirm the effectiveness of our approach.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Like us on Facebook

HGPU group

194 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1330 peoples are following HGPU @twitter

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: