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Journal of Flow Visualization and Image Processing

Published 4 issues per year

ISSN Print: 1065-3090

ISSN Online: 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

INTERNAL FLOW PATTERN AND HEAT TRANSPORT PERFORMANCE OF AN OSCILLATING HEAT PIPE WITH GROOVED CHANNELS

Volume 22, Issue 1-3, 2015, pp. 81-96
DOI: 10.1615/JFlowVisImageProc.2015015685
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ABSTRACT

An oscillating heat pipe (OHP) shows a high heat-transport efficiency because of the spontaneous internal flow convecting both sensible and latent heats. The OHP consists of a meandering tube that connects the heating and cooling sections. When a certain level of temperature difference between these sections is attained, self-oscillating flow of liquid and vapor with a phase change occurs and the OHP is activated to work. In order to reveal the heat transport mechanism and characteristics of the OHP, experimental and numerical studies have been performed. However, the relationship between the two-phase internal flow pattern and heat transport performance had not been clarified quantitatively as yet. In this study, simultaneous measurements of heat transport rate and internal flow pattern were performed in order to discuss the relationship between the internal flow pattern and the heat transport rate. Furthermore, the characteristic frequency and the number of interfaces of the internal flow were calculated from the results. In the present results, when the heat transport rate was increased, the amplitude of the oscillating flow was increased so that the internal flow from the bottom heating section can reach the top cooling section. At the highest heat transport rate, the intensified unidirectional flow component resulted in the fixed upward/ downward flows in each straight section. The characteristic frequency and the number of interfaces of the internal flow gave distinctive values depending on the fixed upward/downward flows in the straight sections.

CITED BY
  1. KOYAMA Ryo, INOKUMA Kento, MURATA Akira, IWAMOTO Kaoru, SAITO Hiroshi, Machine learning-based prediction of heat transport performance in oscillating heat pipe, Journal of Thermal Science and Technology, 17, 1, 2022. Crossref

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