Ryoichi Kurose

5.6k total citations
234 papers, 4.6k citations indexed

About

Ryoichi Kurose is a scholar working on Computational Mechanics, Fluid Flow and Transfer Processes and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Ryoichi Kurose has authored 234 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 205 papers in Computational Mechanics, 90 papers in Fluid Flow and Transfer Processes and 63 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Ryoichi Kurose's work include Combustion and flame dynamics (165 papers), Advanced Combustion Engine Technologies (89 papers) and Fire dynamics and safety research (63 papers). Ryoichi Kurose is often cited by papers focused on Combustion and flame dynamics (165 papers), Advanced Combustion Engine Technologies (89 papers) and Fire dynamics and safety research (63 papers). Ryoichi Kurose collaborates with scholars based in Japan, China and United States. Ryoichi Kurose's co-authors include Satoru Komori, Hisao Makino, Hiroaki Watanabe, Tomoaki Kitano, Fumiteru AKAMATSU, Abhishek Lakshman Pillai, Masaya Muto, Hirofumi TSUJI, Masashi KATSUKI and Yong Hu and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Journal of Fluid Mechanics.

In The Last Decade

Ryoichi Kurose

210 papers receiving 4.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ryoichi Kurose Japan 37 3.6k 1.6k 1.3k 1.0k 806 234 4.6k
Michael Fairweather United Kingdom 33 2.2k 0.6× 593 0.4× 686 0.5× 768 0.7× 830 1.0× 202 4.0k
Satoru Komori Japan 34 2.2k 0.6× 392 0.2× 533 0.4× 283 0.3× 762 0.9× 149 3.3k
Björn H. Hjertager Norway 27 3.2k 0.9× 962 0.6× 1.5k 1.1× 1.1k 1.1× 811 1.0× 93 4.8k
J.M. Beér United States 31 2.7k 0.8× 1.2k 0.7× 1.5k 1.2× 536 0.5× 548 0.7× 125 4.4k
M. P. Escudier United Kingdom 29 2.4k 0.7× 982 0.6× 511 0.4× 90 0.1× 513 0.6× 76 3.1k
Bendiks Jan Boersma Netherlands 36 3.1k 0.9× 573 0.4× 495 0.4× 93 0.1× 669 0.8× 94 3.5k
Josette Bellan United States 33 3.6k 1.0× 1.2k 0.7× 1.4k 1.1× 487 0.5× 923 1.1× 173 4.5k
J. Janicka Germany 45 6.8k 1.9× 3.6k 2.3× 697 0.5× 1.8k 1.7× 603 0.7× 269 7.5k
David G. Sloan United States 5 2.8k 0.8× 248 0.2× 1.7k 1.3× 79 0.1× 1.4k 1.8× 10 4.6k
S. Elghobashi United States 34 4.7k 1.3× 203 0.1× 758 0.6× 142 0.1× 4.0k 5.0× 84 5.8k

Countries citing papers authored by Ryoichi Kurose

Since Specialization
Citations

This map shows the geographic impact of Ryoichi Kurose'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 Ryoichi Kurose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryoichi Kurose more than expected).

Fields of papers citing papers by Ryoichi Kurose

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ryoichi Kurose. 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 Ryoichi Kurose. The network helps show where Ryoichi Kurose may publish in the future.

Co-authorship network of co-authors of Ryoichi Kurose

This figure shows the co-authorship network connecting the top 25 collaborators of Ryoichi Kurose. A scholar is included among the top collaborators of Ryoichi Kurose based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ryoichi Kurose. Ryoichi Kurose is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Kurose, Ryoichi, et al.. (2025). An Extended Height‐Function Method for 3D VOF Simulations of Wetting Phenomena on Super‐Hydrophilic and Hydrophobic Surfaces. International Journal for Numerical Methods in Fluids. 97(7). 1073–1091.
3.
Kai, Reo, et al.. (2024). A new semi-implicit pressure-based solver considering real gas effect. Journal of Computational Physics. 501. 112782–112782. 2 indexed citations
4.
Xing, Jiangkuan, Zhenhua An, & Ryoichi Kurose. (2024). Analysis and flamelet modeling of preferential evaporation in SAF/Jet A spray flames. Proceedings of the Combustion Institute. 40(1-4). 105707–105707. 10 indexed citations
5.
Kai, Reo, et al.. (2024). Effects of preferential diffusion and flame stretch on FGM method for numerical simulations of ammonia/air premixed combustion. Applications in Energy and Combustion Science. 17. 100253–100253. 5 indexed citations
6.
7.
Xing, Jiangkuan, et al.. (2023). Chemical Reaction Neural Network Modelling of Lignocellulosic Biomass Pyrolysis. SSRN Electronic Journal. 1 indexed citations
8.
An, Zhenhua, Jiangkuan Xing, & Ryoichi Kurose. (2023). Numerical study on the phase change and spray characteristics of liquid ammonia flash spray. Fuel. 345. 128229–128229. 54 indexed citations
9.
Pillai, Abhishek Lakshman, et al.. (2022). Numerical investigation of wall effects on combustion noise from a lean-premixed hydrogen/air low-swirl flame. Physics of Fluids. 35(1). 10 indexed citations
10.
Kai, Reo, et al.. (2021). Unsteady flamelet modeling for N 2 H 4 /N 2 O 4 flame accompanied by hypergolic ignition and thermal decomposition. Applications in Energy and Combustion Science. 5. 100022–100022. 1 indexed citations
11.
Kurose, Ryoichi, Takuya Tsuji, Chiharu Tokoro, Takashi Shirai, & Satoshi Watanabe. (2017). Special Invited Reviews “Numerical Simulations of Granular Flows”. Journal of the Society of Powder Technology Japan. 54(2). 104–104. 1 indexed citations
12.
Tanno, Kenji, Ryoichi Kurose, Takenobu Michioka, Hisao Makino, & Satoru Komori. (2013). Effect of Particle Collision and Rebound Behavior on Adhesion Characteristics on the Wall of Honeycomb Shaped Catalyst. Journal of the Society of Powder Technology Japan. 50(3). 204–211.
13.
Watanabe, Hiroaki, et al.. (2012). Numerical Simulation of Soot Formation in Spray Jet Flames. Journal of the Society of Powder Technology Japan. 49(6). 467–477. 3 indexed citations
14.
Tanno, Kenji, Ryoichi Kurose, Takenobu Michioka, Hisao Makino, & Satoru Komori. (2012). Effect of Flow Behavior in Honeycomb Channel on Characteristics of Particle Adhesion to the Wall. Journal of the Society of Powder Technology Japan. 49(10). 738–744. 2 indexed citations
15.
Watanabe, Hiroaki, Ryoichi Kurose, Hisao Makino, & Satoru Komori. (2009). Effect of Droplet Size on Soot Formation in Spray Combustion. Journal of the Society of Powder Technology Japan. 46(6). 426–435. 2 indexed citations
16.
Watanabe, Hiroshi, Ryoichi Kurose, Sung‐Joo Hwang, & Fumiteru AKAMATSU. (2007). Characteristics of flamelets in spray flames formed in a laminar counterflow. Combustion and Flame. 148(4). 234–248. 90 indexed citations
17.
Kurose, Ryoichi, Takenobu Michioka, Hisao Makino, & Satoru Komori. (2007). Effect of Flow Behavior in De-NOx Catalyst Honeycomb on Adhesion of Particles to the Wall. Journal of the Society of Powder Technology Japan. 44(2). 107–112. 2 indexed citations
18.
Kanda, Hideki, et al.. (2004). Study on Influential Factor in Hardening Phenomena of Coal Ash Bed. Journal of the Society of Powder Technology Japan. 41(7). 508–513. 3 indexed citations
19.
Kurose, Ryoichi, Fumiteru AKAMATSU, & Hisao Makino. (2004). . Journal of the Society of Powder Technology Japan. 41(2). 114–123. 1 indexed citations
20.
Kurose, Ryoichi, Hisao Makino, & Satoru Komori. (2002). . Journal of the Society of Powder Technology Japan. 39(5). 353–361. 3 indexed citations

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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