Jung‐Il Choi

7.8k total citations · 3 hit papers
149 papers, 5.9k citations indexed

About

Jung‐Il Choi is a scholar working on Computational Mechanics, Electrical and Electronic Engineering and Aerospace Engineering. According to data from OpenAlex, Jung‐Il Choi has authored 149 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 75 papers in Computational Mechanics, 36 papers in Electrical and Electronic Engineering and 30 papers in Aerospace Engineering. Recurrent topics in Jung‐Il Choi's work include Fluid Dynamics and Turbulent Flows (48 papers), Computational Fluid Dynamics and Aerodynamics (26 papers) and Wind and Air Flow Studies (22 papers). Jung‐Il Choi is often cited by papers focused on Fluid Dynamics and Turbulent Flows (48 papers), Computational Fluid Dynamics and Aerodynamics (26 papers) and Wind and Air Flow Studies (22 papers). Jung‐Il Choi collaborates with scholars based in South Korea, United States and China. Jung‐Il Choi's co-authors include Mayank Jain, Sachin Katti, Kannan Srinivasan, Jack R. Edwards, Philip Levis, Siddharth Seth, Dinesh Bharadia, Taemin Kim, Prasun Sinha and Hyung Jin Sung and has published in prestigious journals such as Physical Review Letters, Environmental Science & Technology and ACS Nano.

In The Last Decade

Jung‐Il Choi

138 papers receiving 5.8k citations

Hit Papers

Achieving single channel,... 2010 2026 2015 2020 2010 2011 2014 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jung‐Il Choi South Korea 35 3.3k 1.7k 1.6k 1.3k 544 149 5.9k
Chih‐Yung Wen Hong Kong 41 831 0.3× 1.8k 1.1× 2.2k 1.3× 115 0.1× 1.1k 2.1× 299 5.4k
Gang Hu China 43 568 0.2× 1.6k 1.0× 1.1k 0.7× 1.5k 1.2× 1.4k 2.6× 258 6.3k
Jin Hu China 31 1.5k 0.5× 407 0.2× 522 0.3× 374 0.3× 298 0.5× 95 3.1k
Yogesh Jaluria United States 36 509 0.2× 2.9k 1.7× 575 0.4× 303 0.2× 651 1.2× 290 6.1k
Venkat Raman United States 35 179 0.1× 2.5k 1.5× 1.3k 0.8× 247 0.2× 291 0.5× 225 4.3k
M. Cross United Kingdom 38 614 0.2× 1.3k 0.8× 573 0.3× 391 0.3× 172 0.3× 179 4.8k
Baochang Shi China 45 4.4k 1.4× 9.5k 5.6× 1.1k 0.7× 66 0.1× 182 0.3× 232 10.4k
Lin Fu China 30 454 0.1× 1.9k 1.1× 372 0.2× 192 0.2× 240 0.4× 230 3.5k
Y. T. Chew Singapore 43 1.9k 0.6× 5.3k 3.1× 1.1k 0.7× 53 0.0× 682 1.3× 170 6.9k
Manfred Krafczyk Germany 39 1.6k 0.5× 3.6k 2.1× 507 0.3× 66 0.1× 420 0.8× 109 4.5k

Countries citing papers authored by Jung‐Il Choi

Since Specialization
Citations

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

Fields of papers citing papers by Jung‐Il Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jung‐Il Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Jung‐Il Choi. A scholar is included among the top collaborators of Jung‐Il 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 Jung‐Il Choi. Jung‐Il 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
2.
Kim, Seongyoon & Jung‐Il Choi. (2024). Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation. Journal of Energy Storage. 100. 113503–113503. 1 indexed citations
3.
Lee, Min‐Ho, et al.. (2024). Bilevel-optimized continual learning for predicting capacity degradation of lithium-ion batteries. Journal of Energy Storage. 86. 111187–111187. 4 indexed citations
4.
Choi, Jung‐Il, et al.. (2024). Hydrodynamic slip in nanoconfined flows: a review of experimental, computational, and theoretical progress. Nanoscale. 17(2). 635–660. 12 indexed citations
5.
Kang, Ji-Hoon, et al.. (2023). PaScaL_TCS: A versatile solver for large-scale turbulent convective heat transfer problems with temperature-dependent fluid properties. Computer Physics Communications. 290. 108779–108779. 3 indexed citations
6.
Choi, Jung‐Il, et al.. (2023). Non-Oberbeck–Boussinesq effects on a water-filled differentially heated vertical cavity. Physics of Fluids. 35(11). 6 indexed citations
7.
Kim, Seongyoon, et al.. (2023). Model-free reconstruction of capacity degradation trajectory of lithium-ion batteries using early cycle data. eTransportation. 17. 100243–100243. 33 indexed citations
9.
Choi, Jung‐Il, et al.. (2023). Improving the third-order WENO schemes by using exponential polynomial space with a locally optimized shape parameter. Computers & Mathematics with Applications. 149. 24–37.
10.
Kim, Seongyoon, et al.. (2023). Bayesian parameter identification in electrochemical model for lithium-ion batteries. Journal of Energy Storage. 71. 108129–108129. 19 indexed citations
11.
Choi, Yun Young, et al.. (2022). Two-layer hydrodynamic network model for redox flow battery stack with flow field design. International Journal of Heat and Mass Transfer. 201. 123626–123626. 10 indexed citations
12.
Kim, Seongyoon, Yun Young Choi, Ki Jae Kim, & Jung‐Il Choi. (2021). Forecasting state-of-health of lithium-ion batteries using variational long short-term memory with transfer learning. Journal of Energy Storage. 41. 102893–102893. 103 indexed citations
13.
Choi, Yun Young, et al.. (2021). Parameter identification and identifiability analysis of lithium‐ion batteries. Energy Science & Engineering. 10(2). 488–506. 10 indexed citations
14.
Kim, Seongyoon, Yun Young Choi, & Jung‐Il Choi. (2021). Impedance-based capacity estimation for lithium-ion batteries using generative adversarial network. Applied Energy. 308. 118317–118317. 56 indexed citations
15.
Hong, Hee‐Kyung, et al.. (2017). A Study on Influencing Factors of Continuous Use Intention by SNS Connection Type and User Psychology. Journal of Digital Contents Society. 18(5). 957–967. 2 indexed citations
16.
Jeon, Kiwan, et al.. (2015). A Reconstruction Method of Blood Flow Velocity in Left Ventricle Using Color Flow Ultrasound. Computational and Mathematical Methods in Medicine. 2015. 1–15. 8 indexed citations
17.
Lee, Jin, Jae Hwa Lee, Jung‐Il Choi, & Hyung Jin Sung. (2014). Spatial organization of large- and very-large-scale motions in a turbulent channel flow. Journal of Fluid Mechanics. 749. 818–840. 95 indexed citations
18.
Corcoran, Timothy E., R. Venkataramanan, M. Patricia George, et al.. (2012). Systemic Delivery of Atropine Sulfate by the MicroDose Dry-Powder Inhaler. Journal of Aerosol Medicine and Pulmonary Drug Delivery. 26(1). 46–55. 28 indexed citations
19.
Park, Hyun Wook, et al.. (2012). Near-Wall Modeling for Large Eddy Simulation of Convective Heat Transfer in Turbulent Boundary Layers. Bulletin of the American Physical Society. 1 indexed citations
20.
Choi, Jung‐Il & Jack R. Edwards. (2011). AN IMMERSED BOUNDARY METHOD. 6(1). 93–96. 8 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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