Jun Matsui

157 papers receiving 1.6k citations

Peers

Jun Matsui
Comparison fields: 5 of 148
  • Mechanics of Materials 364
  • Endocrinology, Diabetes and Metabolism 159
  • Computational Mechanics 217
  • Fluid Flow and Transfer Processes 59
  • Endocrinology 46
Replace Masao Takahashi with:
Masao Takahashi Japan
Martin Veselý Czechia
Keisuke Ikeda Japan
K. Tokunaga Japan
Kiyoshi Tamura Japan
Hirofumi Shimizu Japan
Sung Yang South Korea
Jianhui Yuan China
Qi Wen United States
Susumu Nakamura Japan
Jun Matsui relative to Masao Takahashi Japan Masao Takahashi's profile →
Citations per field
00.5×10×16.7×
Masao Takahashi · 1×
Citations per year

Countries citing papers authored by Jun Matsui

Since Specialization
Citations

This map shows the geographic impact of Jun Matsui's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jun Matsui with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Matsui more than expected).

Fields of papers citing papers by Jun Matsui

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jun Matsui. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jun Matsui. The network helps show where Jun Matsui may publish in the future.

Co-authors

The 25 scholars most cited alongside Jun Matsui, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jun Matsui Line = papers co-authored together Jun Matsui links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 169 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200473
2 200173
3 200063
4 200555
5 200051
6
CFD prediction and model experiment on suction vortices in pump sump
200750
7 200047
8 200947
9 200141
10 199938
11 200034
12 199431
13 200630
14 200829
15 199429
16 199428
17 200727
18 201227
19 199926
20 200426

About Jun Matsui

Jun Matsui is a scholar working on Mechanics of Materials, Mechanical Engineering, Electrical and Electronic Engineering, Materials Chemistry and Surgery, having authored 169 papers that have together received 1.7k indexed citations. Recurring topics across this work include Cavitation Phenomena in Pumps (25 papers), Hydraulic and Pneumatic Systems (19 papers), Material Dynamics and Properties (15 papers), Plasma Diagnostics and Applications (9 papers), Cholesterol and Lipid Metabolism (9 papers), Health, Medicine and Society (8 papers), Rocket and propulsion systems research (8 papers) and Aging, Elder Care, and Social Issues (8 papers). The work is most often cited by research in Mechanics of Materials (364 citations), Endocrinology, Diabetes and Metabolism (159 citations), Computational Mechanics (217 citations), Fluid Flow and Transfer Processes (59 citations) and Endocrinology (46 citations). Jun Matsui has collaborated with scholars based in Japan, United States and Ukraine. Frequent co-authors include Junichi Kurokawa, Naoki Tamasawa, Hiroshi Murakami, Toshihiro Suda, Toshiaki Makabe, Takashi Odagaki, Hiroshi IMAMURA, Y. Hiwatari, Young‐Do Choi and Jutaro Tanabe. Their work appears in journals such as Journal of Fluids Engineering, Diabetes Research and Clinical Practice, Journal of Atherosclerosis and Thrombosis, Atherosclerosis and Nuclear Fusion.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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