Hyeok-Bin Kwon

650 total citations
44 papers, 518 citations indexed

About

Hyeok-Bin Kwon is a scholar working on Aerospace Engineering, Civil and Structural Engineering and Environmental Engineering. According to data from OpenAlex, Hyeok-Bin Kwon has authored 44 papers receiving a total of 518 indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Aerospace Engineering, 19 papers in Civil and Structural Engineering and 14 papers in Environmental Engineering. Recurrent topics in Hyeok-Bin Kwon's work include Aerodynamics and Fluid Dynamics Research (33 papers), Engineering Applied Research (19 papers) and Wind and Air Flow Studies (14 papers). Hyeok-Bin Kwon is often cited by papers focused on Aerodynamics and Fluid Dynamics Research (33 papers), Engineering Applied Research (19 papers) and Wind and Air Flow Studies (14 papers). Hyeok-Bin Kwon collaborates with scholars based in South Korea, United States and Slovakia. Hyeok-Bin Kwon's co-authors include Kyu Hong Kim, Dong‐Ho Lee, Kwanjung Yee, Seong‐Won Nam, Van Luu‐The, G. Pelletier, Jean‐Luc Do‐Régo, Jae Young Seong, Ludovic Galas and Arlette Burlet and has published in prestigious journals such as Journal of Neuroscience, Knee Surgery Sports Traumatology Arthroscopy and Structural and Multidisciplinary Optimization.

In The Last Decade

Hyeok-Bin Kwon

37 papers receiving 483 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyeok-Bin Kwon South Korea 10 391 237 233 84 79 44 518
Kiyoshi NAGAKURA Japan 8 301 0.8× 136 0.6× 168 0.7× 62 0.7× 30 0.4× 31 353
D.J. Taunton United Kingdom 11 67 0.2× 67 0.3× 226 1.0× 84 1.0× 51 0.6× 48 427
Takahide Nouzawa Japan 14 410 1.0× 288 1.2× 250 1.1× 17 0.2× 14 0.2× 58 518
Γεώργιος Παπαϊωάννου Sweden 12 128 0.3× 208 0.9× 316 1.4× 85 1.0× 131 1.7× 34 611
Albert R. George United States 11 247 0.6× 100 0.4× 126 0.5× 87 1.0× 42 0.5× 34 367
Michal Španěl Czechia 10 199 0.5× 114 0.5× 28 0.1× 33 0.4× 13 0.2× 28 409
C Talotte France 4 210 0.5× 89 0.4× 108 0.5× 100 1.2× 30 0.4× 5 291
A. Cogotti Italy 17 553 1.4× 413 1.7× 303 1.3× 26 0.3× 15 0.2× 30 607
J. F. Wilby United States 12 290 0.7× 106 0.4× 94 0.4× 30 0.4× 49 0.6× 43 452
István L. Vér United States 8 89 0.2× 46 0.2× 69 0.3× 85 1.0× 114 1.4× 24 455

Countries citing papers authored by Hyeok-Bin Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Hyeok-Bin Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hyeok-Bin Kwon. 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 Hyeok-Bin Kwon. The network helps show where Hyeok-Bin Kwon may publish in the future.

Co-authorship network of co-authors of Hyeok-Bin Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Hyeok-Bin Kwon. A scholar is included among the top collaborators of Hyeok-Bin Kwon 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 Hyeok-Bin Kwon. Hyeok-Bin Kwon 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.
Kwon, Hyeok-Bin, et al.. (2024). Computational Analysis to Derive Passenger Ear Discomfort Criteria for 400 km/h High Speed Train. Journal of the Korean society for railway. 27(12). 1055–1064.
2.
Kim, Seung Joong, et al.. (2023). Aerodynamic performance of a pantograph cover for high-speed train. Journal of Mechanical Science and Technology. 37(9). 4681–4693. 3 indexed citations
3.
Kim, Jin‐Young, et al.. (2022). Characteristics of a Flow Field Generated by a Breach on an Evacuated Tube. International Journal of Aeronautical and Space Sciences. 23(5). 848–858. 3 indexed citations
4.
Kwon, Hyeok-Bin, et al.. (2022). Numerical investigation of snow accumulation on a high-speed train by snow saltation. International Journal of Rail Transportation. 11(4). 465–489. 9 indexed citations
5.
Kwon, Hyeok-Bin, et al.. (2017). A Study on the Aerodynamic Drag of Transonic Vehicle in Evacuated Tube Using Computational Fluid Dynamics. International Journal of Aeronautical and Space Sciences. 18(4). 614–622. 38 indexed citations
6.
Kwon, Hyeok-Bin. (2017). A study on the resistance force and the aerodynamic drag of Korean high-speed trains. Vehicle System Dynamics. 56(8). 1250–1268. 17 indexed citations
8.
Kwon, Hyeok-Bin, et al.. (2014). Assessment of the Running Resistance of a High-speed Train Using a Coasting Test. Journal of the Korean society for railway. 17(3). 165–170. 5 indexed citations
9.
Kwon, Hyeok-Bin, et al.. (2013). A numerical study on the pressure relief by a vertical shaft in a high speed railway tunnel. Journal of Korean Tunnelling and Underground Space Association. 15(6). 559–570. 2 indexed citations
10.
Nam, Seong‐Won, et al.. (2012). Characteristics Method Analysis of Wind Pressure of Train Running in Tunnel. Journal of the Korean society for railway. 15(5). 436–441. 4 indexed citations
11.
Kwon, Hyeok-Bin, et al.. (2010). Parametric Study on the Aerodynamic Drag of Ultra High-speed Train in Evacuated Tube - Part 1. Journal of the Korean society for railway. 13(1). 44–50. 8 indexed citations
12.
Kwon, Hyeok-Bin & Daesung Chang. (2010). A Study on the Window Glass Pressure for High-speed Train. Journal of the Korean society for railway. 13(4). 371–375. 1 indexed citations
13.
Lee, Dong‐Ho, et al.. (2009). Experimental Study of the Internal/external Pressure Variation of TTX Travelling through a Tunnel. Journal of the Korean society for railway. 12(2). 309–314. 1 indexed citations
14.
Lee, Hyung-Woo, et al.. (2009). Analysis of the Magnetic Effect on the Tube Infrastructure for a Super Speed Tube Train. 2(4). 170–174. 4 indexed citations
15.
Kwon, Hyeok-Bin, et al.. (2009). A Real-scale Wind Tunnel Testing on a Pantograph for High-speed Train to Assess the Aerodynamic Characteristics. Journal of the Korean society for railway. 12(5). 732–737. 1 indexed citations
16.
Kwon, Hyeok-Bin, et al.. (2009). Assessment of the Pressure Transient Inside the Passenger Cabin of High-speed Train Using Computational Fluid Dynamics. Journal of the Korean society for railway. 12(1). 65–71. 4 indexed citations
17.
Nam, Seong‐Won & Hyeok-Bin Kwon. (2007). A Study of Aerodynamical Effects for Determining the Distance between Track Centers by using Real Train Experiment. Journal of the Korean society for railway. 10(5). 487–491.
18.
Kwon, Hyeok-Bin, et al.. (2006). A Numerical Analysis on the Pressure Field Around KTX Train Using the Standard Framework of CFD Analysis for Railway System. Journal of the Korean society for railway. 9(5). 511–516. 1 indexed citations
19.
Do‐Régo, Jean‐Luc, Jae Young Seong, Ludovic Galas, et al.. (2006). Vasotocin and Mesotocin Stimulate the Biosynthesis of Neurosteroids in the Frog Brain. Journal of Neuroscience. 26(25). 6749–6760. 40 indexed citations
20.
Makarevich, Alexander V., et al.. (2004). The Role of Oxytocin, Protein Kinase A, and ERK-Related MAP-Kinase in the Control of Porcine Ovarian Follicle Functions. Experimental and Clinical Endocrinology & Diabetes. 112(2). 108–114. 18 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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