Yong-Su Na

1.3k total citations
89 papers, 524 citations indexed

About

Yong-Su Na is a scholar working on Nuclear and High Energy Physics, Aerospace Engineering and Astronomy and Astrophysics. According to data from OpenAlex, Yong-Su Na has authored 89 papers receiving a total of 524 indexed citations (citations by other indexed papers that have themselves been cited), including 83 papers in Nuclear and High Energy Physics, 32 papers in Aerospace Engineering and 30 papers in Astronomy and Astrophysics. Recurrent topics in Yong-Su Na's work include Magnetic confinement fusion research (80 papers), Ionosphere and magnetosphere dynamics (29 papers) and Particle accelerators and beam dynamics (25 papers). Yong-Su Na is often cited by papers focused on Magnetic confinement fusion research (80 papers), Ionosphere and magnetosphere dynamics (29 papers) and Particle accelerators and beam dynamics (25 papers). Yong-Su Na collaborates with scholars based in South Korea, United States and Germany. Yong-Su Na's co-authors include Y. S. Hwang, Jeongwon Lee, Chanyoung Lee, Jaemin Seo, Y.M. Jeon, Young-Gi Kim, T.S. Hahm, Min-Gu Yoo, Seong‐Jik Park and YoungHwa An and has published in prestigious journals such as Physical Review Letters, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Yong-Su Na

77 papers receiving 488 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yong-Su Na South Korea 14 444 156 152 139 137 89 524
M. Tsalas Germany 16 525 1.2× 234 1.5× 112 0.7× 149 1.1× 255 1.9× 47 583
J.M. Park United States 15 573 1.3× 176 1.1× 217 1.4× 209 1.5× 250 1.8× 30 635
A. Boboc United Kingdom 13 375 0.8× 188 1.2× 90 0.6× 78 0.6× 125 0.9× 53 487
S.H. Hahn South Korea 15 699 1.6× 243 1.6× 248 1.6× 276 2.0× 239 1.7× 93 761
F. Saint‐Laurent France 14 527 1.2× 131 0.8× 130 0.9× 143 1.0× 228 1.7× 30 585
K. Rahbarnia Germany 14 485 1.1× 332 2.1× 98 0.6× 70 0.5× 139 1.0× 60 618
V. Pericoli‐Ridolfini Italy 13 463 1.0× 173 1.1× 140 0.9× 158 1.1× 233 1.7× 32 530
R. Sweeney United States 13 348 0.8× 137 0.9× 93 0.6× 125 0.9× 143 1.0× 42 431
A. Meakins United Kingdom 13 420 0.9× 189 1.2× 87 0.6× 58 0.4× 137 1.0× 28 480
Zhoujun Yang China 14 640 1.4× 294 1.9× 172 1.1× 223 1.6× 164 1.2× 97 743

Countries citing papers authored by Yong-Su Na

Since Specialization
Citations

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

Fields of papers citing papers by Yong-Su Na

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yong-Su Na. 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 Yong-Su Na. The network helps show where Yong-Su Na may publish in the future.

Co-authorship network of co-authors of Yong-Su Na

This figure shows the co-authorship network connecting the top 25 collaborators of Yong-Su Na. A scholar is included among the top collaborators of Yong-Su Na 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 Yong-Su Na. Yong-Su Na 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.
Park, Sangjin, G. J. Choi, Eun‐jin Kim, et al.. (2025). Experimental identification of I-mode characteristics at the edge of FIRE mode in KSTAR. Nuclear Fusion. 65(3). 36003–36003. 1 indexed citations
2.
Woo, M.H., S.H. Hahn, Y.S. Park, et al.. (2025). First application of minimum island width growth rate seeking feedback controller for neoclassical tearing mode stabilization. Fusion Engineering and Design. 212. 114850–114850.
3.
Chung, Hyun Kyung, Chanyoung Lee, G. J. Choi, et al.. (2025). Development of a data-driven neural network model for electron thermal transport in NSTX. Nuclear Fusion. 65(8). 86028–86028. 1 indexed citations
4.
Yoo, Min-Gu, et al.. (2025). Modelling of electron cyclotron energy gain in the tokamak pre-ionization phase. Nuclear Fusion. 65(5). 56038–56038.
5.
Jalalvand, Azarakhsh, S.K. Kim, Jaemin Seo, et al.. (2025). Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas. Nature Communications. 16(1). 8506–8506.
6.
Park, Sangjin, Chanyoung Lee, Hyung Jin Shim, et al.. (2024). Evaluation of the impact of plasma operation scenarios on the fusion reactor blanket design using an integrated numerical plasma and neutronics analysis suite. Fusion Engineering and Design. 202. 114350–114350.
7.
Jeong, Junhyung, et al.. (2024). The impact of stray magnetic fields on the KSTAR NBI performance. Fusion Engineering and Design. 207. 114646–114646. 1 indexed citations
8.
Aleynikov, P., P.C. de Vries, Hyun-Tae Kim, et al.. (2024). Binary Nature of Collisions Facilitates Runaway Electron Generation in Weakly Ionized Plasmas. Physical Review Letters. 133(17). 175102–175102. 1 indexed citations
9.
Hu, Di, M. Lehnen, E. Nardon, et al.. (2024). Nonlinear MHD modeling of neon doped shattered pellet injection with JOREK and its comparison to experiments in KSTAR. Nuclear Fusion. 64(10). 106042–106042. 2 indexed citations
10.
Yang, S.M., Jong-Kyu Park, Y.M. Jeon, et al.. (2024). Tailoring tokamak error fields to control plasma instabilities and transport. Nature Communications. 15(1). 1275–1275. 11 indexed citations
11.
Park, Jong-Kyu, et al.. (2024). Effect of parallel flow on resonant layer responses in high beta plasmas. Nuclear Fusion. 64(10). 106058–106058. 1 indexed citations
12.
Vries, P.C. de, P. Aleynikov, Yoonseok Lee, et al.. (2023). Kinetic modelling of start-up runaway electrons in KSTAR and ITER. Nuclear Fusion. 63(10). 106011–106011. 3 indexed citations
13.
Kim, S.K., Min‐Sik Park, M. Choi, et al.. (2023). Effect of coherent edge-localized mode on transition to high-performance hybrid scenarios in KSTAR. Nuclear Fusion. 63(12). 126032–126032. 7 indexed citations
14.
Na, Yong-Su. (2023). Advanced operation modes relying on core plasma turbulence stabilization in tokamak fusion devices. SHILAP Revista de lepidopterología. 33(1).
15.
Lee, Chanyoung, Seong‐Cheol Kim, Young-Gi Kim, et al.. (2022). Investigation of the effect of pre-fill gas in VEST discharges by predictive transport simulations. Journal of the Korean Physical Society. 81(2). 126–132. 1 indexed citations
16.
Seo, Jaemin, et al.. (2021). Feedforward beta control in the KSTAR tokamak by deep reinforcement learning. Nuclear Fusion. 61(10). 106010–106010. 41 indexed citations
17.
Yoo, Min-Gu, Jeongwon Lee, Young-Gi Kim, et al.. (2018). Evidence of a turbulent ExB mixing avalanche mechanism of gas breakdown in strongly magnetized systems. Nature Communications. 9(1). 3523–3523. 17 indexed citations
18.
Kim, Seong‐Cheol, Jeongwon Lee, J. Yang, et al.. (2017). Development of diverted plasma discharge and plan for advanced divertor study in VEST. Fusion Engineering and Design. 123. 584–587. 3 indexed citations
19.
Kim, Kyungjin, Yong-Su Na, Hyun-Seok Kim, et al.. (2016). Modeling of neoclassical tearing mode stabilization by electron cyclotron heating and current drive in tokamak plasmas. Current Applied Physics. 16(8). 867–875. 4 indexed citations
20.
Kim, Hyun-Seok, et al.. (2011). L- to H-mode power threshold and confinement characteristics of H-modes in KSTAR. Bulletin of the American Physical Society. 53. 2 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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