流动显示和图像处理期刊 Magazine cover
流动显示和图像处理期刊

每年出版 4 

ISSN 打印: 1065-3090

ISSN 在线: 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

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目标与范围

The Journal of Flow Visualization and Image Processing is a quarterly refereed research journal that publishes original papers to disseminate and exchange knowledge and information on the principles and applications of flow visualization techniques and related image processing algorithms.
 Flow visualization and quantification have emerged as powerful tools in velocity, pressure, temperature and species concentration measurements, combustion diagnostics, and process monitoring related to physical, biomedical, and engineering sciences. Measurements were initially based on lasers but have expanded to include a wider electromagnetic spectrum. Numerical simulation is a second source of data amenable to image analysis. Direct visualization in the form of high speed, high resolution imaging supplements optical measurements. A combination of flow visualization and image processing holds promise to breach the holy grail of extracting instantaneous three dimensional data in transport phenomena.
 Optical methods can be enlarged to cover a wide range of measurements, first by factoring in the applicable physical laws and next, by including the principle of image formation itself. These steps help in utilizing incomplete data and imperfect visualization for reconstructing a complete scenario of the transport process.
 Many applications involve gas-liquid interfaces that move in time and over a solid surface. Locating such boundaries from image sequences is an important step that can provide considerable insight. Flow visualization data can also be the starting point of inversion algorithms for retrieval of material properties and boundary conditions such as wall heat fluxes and shear stresses.
 Images acquired in an experiment or from simulation contain a wealth of data and on multiple scales. Useful information can then be extracted using statistical and image processing tools. New developments such as tomographic reconstruction, digital correlation technique, and data mining algorithms, including AI, are quite appropriate for interpreting flow visualization data.
 The journal will promote academic and industrial advancement and improvement of flow imaging techniques internationally. It seeks to convey practical information in this field covering all areas in science, technology, and medicine for engineers, scientists, and researchers in industry, academia, and government.