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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

DEVELOPING PARTICLE IMAGE VELOCIMETRY SOFTWARE BASED ON A DEEP NEURAL NETWORK

巻 27, 発行 4, 2020, pp. 359-376
DOI: 10.1615/JFlowVisImageProc.2020033180
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要約

As an experimental technique for fluid mechanics, particle image velocimetry (PIV) can extract global and quantitative velocity field from images. With the development of artificial intelligence, designing PIV method based on deep learning is quite promising and worth exploring. First, in this paper, the authors introduce the optical flow neural network based on one proposed in the computer vision community. Second, a data set including particle images and the ground truth fluid motion is generated to train the parameters of the networks. This leads to a deep neural network for PIV which can provide estimation of dense motion (down to maximum one vector for one pixel) with the high degree of efficiency. The featuring of particle image extracted by the neural network is also investigated in this paper. It is found that feature matching improves the accuracy of estimation. The proposed network model is firstly evaluated by a synthetic image sequence of turbulent flow. An experiment measuring the flow over an aerofoil is used to validate the practicability. The experimental results indicate that compared with the traditional cross correlation method, the proposed deep neural network has advantages in accuracy, spatial resolution, and efficiency.

参考
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によって引用された
  1. Wolański Wojciech, Ples Marek, Sobkowiak-Pilorz Marta, Gruszka Grzegorz, Burkacki Michał, Suchoń Sławomir, Gzik Marek, Experimental Study the Blood Flows in a Transparent Models of a Blood Vessels with Bifurcation—Preliminary Report, in Innovations in Biomedical Engineering, 409, 2023. Crossref

  2. Piskur Paweł, Side Fins Performance in Biomimetic Unmanned Underwater Vehicle, Energies, 15, 16, 2022. Crossref

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