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Journal of Flow Visualization and Image Processing

年間 4 号発行

ISSN 印刷: 1065-3090

ISSN オンライン: 1940-4336

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 0.6 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00013 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.14 SJR: 0.201 SNIP: 0.313 CiteScore™:: 1.2 H-Index: 13

Indexed in

HIGH-PERFORMANCE FLOW VISUALIZATION FOR EFFECTIVE DATA ANALYSIS

巻 23, 発行 1-2, 2016, pp. 41-57
DOI: 10.1615/JFlowVisImageProc.2017019896
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要約

As data grows at exponential rates toward exa-scale (1018) computing, it is necessary to exploit scientific visualization in the form of a visual, informative, and interactive methodology to help resolve daunting problems arising from a wide variety of disciplines that involve big data analysis. While novel visualization algorithms are investigated for effective as well as efficient exploration of surface and volume flows, there are signs of revisiting sparse geometry-based methods with further improvement, back from dense texture-based approaches, and resorting to parallel visualization. This paper presents our innovative research along this path in high-performance visualization of flow data for exploration, recognition, representation, and analysis of overall patterns and salient features, with techniques from texture-based to geometry-based, flows from 2D to 3D, complexity from steady to unsteady, and computing from serial to parallel. Specifically, a high-level description is primarily focused on four algorithms, i.e., accelerated unsteady flow line integral convolution, its extension to time-varying volume flows, advanced evenly spaced streamline placement, and interactive view-driven evenly spaced streamline placement. Also introduced are four systems, i.e., ActiveLIC, ActiveIBFV, ActiveFLOVE, and DOXIV, which we developed for high-performance flow visualization coupled with applications to demonstrate the practical applicability.

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  1. Zhang Cuicui, Wei Hao, Bi Chongke, Liu Zhilei, Helmholtz–Hodge decomposition-based 2D and 3D ocean surface current visualization for mesoscale eddy detection, Journal of Visualization, 22, 2, 2019. Crossref

  2. Liu Zhanping, NUMERICAL FLOW VISUALIZATION: VISTA AND EXPEDITION , Journal of Flow Visualization and Image Processing, 29, 3, 2022. Crossref

  3. Wu Keqin, Zhang Song , Moorhead Robert J. , A NEW FAST LIC-LIKE FLOW VISUALIZATION METHOD WITH FLOW PATTERN ACCENTUATION AND DELINEATION ENHANCEMENT , Journal of Flow Visualization and Image Processing, 29, 3, 2022. Crossref

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