UFAT-a particle tracer for time-dependent flow fields

Lane, David A.

doi:10.1109/visual.1994.346311

Cited by 60 publications

(35 citation statements)

References 12 publications

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“…In addition, the field-object model is used to represent thermal layer (such as temperature thermal layer, salinity thermal layer, density thermal layer and acoustic thermal layer), eddy (such as cold eddy and warm eddy), frontal surface, water mass, and other marine phenomena. There are a lot of previous visualization researches based on these expression models [11][12][13][14][15][16].…”

Section: Visualization Methodsmentioning

confidence: 99%

Web-based spatiotemporal visualization of marine environment data

et al. 2010

Chin. J. Ocean. Limnol.

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Abstract-As the long-term marine survey and research, especially with the development of new marine environment monitoring technologies, prodigious amounts of complex marine environment data keep continued and rapid growth. This paper recommends an integrative visualization solution to those data, to enhance the visual display of data and data archives, and to develop a joint use of those distributed data from different organizations or communities. Following this strategy, after the analysis of web services technologies and the concept definition of marine information gird, this paper focuses on the spatiotemporal visualization method and proposes a process-oriented spatiotemporal visualization method. It also provides an original visualization architecture which is integrative and based on the explored technologies. Then it shows how the marine environment data are organized based on the spatiotemporal visualization method, and how the organized data are represented for use with web services and stored in a reusable fashion. Finally, the prototype system for the marine environment data of the South China Sea provides visualization of Argo floats, sea surface temperature fields, sea current fields, salinity, in-situ investigation data and ocean stations. The integration visualization architecture is illustrated on the prototype system which highlights the processoriented temporal visualization method, which proves the good effect of the architecture and the method promoted by this work.

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Section: Visualization Methodsmentioning

confidence: 99%

Web-based spatiotemporal visualization of marine environment data

et al. 2010

Chin. J. Ocean. Limnol.

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“…In previous work it has also been proposed to precompute and store particle trajectories for a number of prescribed seed points, and to restrict the visualization to subsets of these trajectories (Lane, 1994;Bruckschen et al, 2001;Ellsworth et al, 2004). In this way, all computation is shifted to the preprocessing stage, and storage as well as bandwidth limitations at runtime can be overcome.…”

Section: Related Workmentioning

confidence: 99%

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Treib

Burger

et al. 2015

Proceedings of the 6th International Conference on Information Visualization Theory and Applications

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Abstract:We shed light on the accuracy of particle trajectories in turbulent vector fields when lossy data compression is used. So far, data compression has been considered rather hesitantly due to supposed accuracy issues. Motivated by the observation that particle traces are always afflicted with inaccuracy, we quantitatively analyze the additional inaccuracies caused by lossy compression. In some experiments we confirm that the compression has only minor impact on the accuracy of the trajectories. Even though our experiments are not generally valid, they indicate that a more thorough analysis of the error in particle integration due to compression is necessary, and that in some cases lossy compression is valid and can significantly reduce performance limitations due to memory and communication bandwidth.

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“…An integral curve is communicated among processors as it traverses different blocks. Other examples of applying parallel computation to integral curve-based visualization include the use of multiprocessor workstations to parallelize integral curve computation (e.g., [14]), and research efforts focusing on accelerating specific visualization techniques [4]. Similarly, PC cluster systems were leveraged to accelerate advanced integration-based visualization algorithms, such as time-varying Line Integral Convolution (LIC) volumes [16] or particle visualization for very large data [7].…”

Section: Parallel Considerationsmentioning

confidence: 99%

Parallel stream surface computation for large data sets

Camp

Childs

Garth

et al. 2012

IEEE Symposium on Large Data Analysis and Visualization (LDAV)

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Figure 1: Stream surfaces generated during this study. On the left, flow around an ellipsoid. In the middle, flow in a tokamak. On the right, flow from a thermal hydraulics simulation. ABSTRACTParallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

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UFAT-a particle tracer for time-dependent flow fields

Cited by 60 publications

References 12 publications

Web-based spatiotemporal visualization of marine environment data

Web-based spatiotemporal visualization of marine environment data

Analyzing the Effect of Lossy Compression on Particle Traces in Turbulent Vector Fields

Parallel stream surface computation for large data sets

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