Haecheon Choi

15.6k total citations · 6 hit papers
199 papers, 12.4k citations indexed

About

Haecheon Choi is a scholar working on Computational Mechanics, Aerospace Engineering and Environmental Engineering. According to data from OpenAlex, Haecheon Choi has authored 199 papers receiving a total of 12.4k indexed citations (citations by other indexed papers that have themselves been cited), including 175 papers in Computational Mechanics, 87 papers in Aerospace Engineering and 40 papers in Environmental Engineering. Recurrent topics in Haecheon Choi's work include Fluid Dynamics and Turbulent Flows (139 papers), Fluid Dynamics and Vibration Analysis (79 papers) and Wind and Air Flow Studies (40 papers). Haecheon Choi is often cited by papers focused on Fluid Dynamics and Turbulent Flows (139 papers), Fluid Dynamics and Vibration Analysis (79 papers) and Wind and Air Flow Studies (40 papers). Haecheon Choi collaborates with scholars based in South Korea, United States and United Kingdom. Haecheon Choi's co-authors include Parviz Moin, John Kim, Jungwoo Kim, Woo-Pyung Jeon, Jung Yul Yoo, Jin‐Sung Kim, Hyungmin Park, Sangmo Kang, Jungil Lee and Taegee Min and has published in prestigious journals such as PLoS ONE, Journal of Fluid Mechanics and Scientific Reports.

In The Last Decade

Haecheon Choi

194 papers receiving 11.9k citations

Hit Papers

An Immersed-Boundary Finite-Volume Method for Simulations... 1993 2026 2004 2015 2001 2008 2012 1993 1994 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Haecheon Choi South Korea 54 10.9k 4.5k 2.7k 2.0k 947 199 12.4k
Dan S. Henningson Sweden 59 12.3k 1.1× 4.7k 1.0× 2.9k 1.1× 1.8k 0.9× 561 0.6× 273 13.8k
Gianluca Iaccarino United States 45 7.6k 0.7× 2.8k 0.6× 1.9k 0.7× 912 0.5× 653 0.7× 280 10.4k
Milovan Perić Germany 26 7.9k 0.7× 2.3k 0.5× 1.6k 0.6× 2.3k 1.1× 1.7k 1.8× 77 12.2k
Rajat Mittal United States 61 11.1k 1.0× 6.6k 1.5× 1.2k 0.4× 1.5k 0.7× 1.3k 1.4× 342 15.9k
Philippe R. Spalart United States 53 18.0k 1.7× 11.9k 2.7× 6.8k 2.5× 2.4k 1.2× 840 0.9× 208 21.1k
Paul A. Durbin United States 47 7.6k 0.7× 3.5k 0.8× 2.7k 1.0× 2.4k 1.2× 407 0.4× 170 8.9k
Fulvio Scarano Netherlands 59 10.5k 1.0× 6.6k 1.5× 3.2k 1.2× 1.2k 0.6× 949 1.0× 276 13.2k
Peter A. Monkewitz Switzerland 35 6.3k 0.6× 2.4k 0.5× 1.8k 0.7× 768 0.4× 457 0.5× 95 7.0k
Markus Raffel Germany 32 6.8k 0.6× 4.1k 0.9× 1.5k 0.5× 1.3k 0.6× 1.1k 1.2× 179 10.3k
M. S. Chong Australia 34 6.1k 0.6× 1.6k 0.4× 2.5k 0.9× 1.1k 0.6× 262 0.3× 104 7.2k

Countries citing papers authored by Haecheon Choi

Since Specialization
Citations

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

Fields of papers citing papers by Haecheon Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Haecheon Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Haecheon Choi. A scholar is included among the top collaborators of Haecheon Choi 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 Haecheon Choi. Haecheon Choi 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.
Choi, Haecheon, et al.. (2025). Perspective on machine-learning-based large-eddy simulation. Physical Review Fluids. 10(11).
2.
Choi, Haecheon, et al.. (2024). A recursive neural-network-based subgrid-scale model for large eddy simulation: application to homogeneous isotropic turbulence. Journal of Fluid Mechanics. 1000. 4 indexed citations
3.
Kim, Myung‐Hwa, Jong Hwan Park, & Haecheon Choi. (2024). Large eddy simulation of flow over a circular cylinder with a neural-network-based subgrid-scale model. Journal of Fluid Mechanics. 984. 12 indexed citations
4.
Kim, Kiyoung & Haecheon Choi. (2024). Characteristics of turbulent core–annular flow with water-lubricated high viscosity oil in a horizontal pipe. Journal of Fluid Mechanics. 986. 3 indexed citations
5.
Chang, J.S., Kiyoung Kim, & Haecheon Choi. (2024). A locally redistributed level-set method for numerical simulation of thin interface structures. Computers & Fluids. 278. 106315–106315. 1 indexed citations
6.
Choi, Haecheon, et al.. (2024). Effect of the yaw angle on the riblet performance. Physics of Fluids. 36(6).
7.
Kim, Kiyoung, et al.. (2024). A numerical study on the turbulence characteristics in an air–water upward bubbly pipe flow. Journal of Fluid Mechanics. 994. 1 indexed citations
8.
Choi, Haecheon, et al.. (2023). Role of the deviation motion on the aerodynamic performance of a mosquito wing in hover. Computers & Fluids. 270. 106146–106146. 2 indexed citations
9.
Choi, Haecheon, et al.. (2023). Leading-edge vortex and aerodynamic performance scaling in a simplified vertical-axis wind turbine. Physics of Fluids. 35(10). 3 indexed citations
10.
Oh, Seungtae, et al.. (2023). Effect of superhydrophilic surface on the cavitation behaviors of rotating blades. Physics of Fluids. 35(11). 7 indexed citations
11.
Kim, Woo Jin, et al.. (2022). Vertical and torsional vibrations before the collapse of the Tacoma Narrows Bridge in 1940. Journal of Fluid Mechanics. 949. 9 indexed citations
12.
Choi, Haecheon, et al.. (2021). Modification of flow behind a circular cylinder by steady and time-periodic blowing. Physics of Fluids. 33(11). 16 indexed citations
13.
Choi, Haecheon, et al.. (2021). A predictive model of the drag coefficient of a circular cylinder. Physics of Fluids. 33(11). 6 indexed citations
14.
Choi, Haecheon, et al.. (2019). Predictions of the effective slip length and drag reduction with a lubricated micro-groove surface in a turbulent channel flow. Journal of Fluid Mechanics. 874. 797–820. 46 indexed citations
15.
Choi, Haecheon, et al.. (2018). A predictive model of the drag coefficient for a revolving wing at low Reynolds number. Bioinspiration & Biomimetics. 13(5). 54001–54001. 1 indexed citations
16.
Cho, Minjung, Yongyun Hwang, & Haecheon Choi. (2018). Scale interactions and spectral energy transfer in turbulent channel flow. Journal of Fluid Mechanics. 854. 474–504. 88 indexed citations
17.
Choi, Haecheon, et al.. (2018). Flow structure modifications by leading-edge tubercles on a 3D wing. Bioinspiration & Biomimetics. 13(6). 66011–66011. 25 indexed citations
18.
Kim, Kiyoung & Haecheon Choi. (2018). Direct numerical simulation of a turbulent core-annular flow with water-lubricated high viscosity oil in a vertical pipe. Journal of Fluid Mechanics. 849. 419–447. 19 indexed citations
19.
Choi, Haecheon, et al.. (2016). Effects of the air layer of an idealized superhydrophobic surface on the slip length and skin-friction drag. Journal of Fluid Mechanics. 790. 42 indexed citations
20.
Choi, Jin Woo, et al.. (2006). Characteristics of flow around a sphere with a surface trip.. Bulletin of the American Physical Society. 59. 1 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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