J.L. Barr

626 total citations
36 papers, 261 citations indexed

About

J.L. Barr is a scholar working on Nuclear and High Energy Physics, Materials Chemistry and Biomedical Engineering. According to data from OpenAlex, J.L. Barr has authored 36 papers receiving a total of 261 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Nuclear and High Energy Physics, 15 papers in Materials Chemistry and 12 papers in Biomedical Engineering. Recurrent topics in J.L. Barr's work include Magnetic confinement fusion research (34 papers), Fusion materials and technologies (15 papers) and Superconducting Materials and Applications (12 papers). J.L. Barr is often cited by papers focused on Magnetic confinement fusion research (34 papers), Fusion materials and technologies (15 papers) and Superconducting Materials and Applications (12 papers). J.L. Barr collaborates with scholars based in United States, China and United Kingdom. J.L. Barr's co-authors include David Humphreys, M.W. Bongard, M.G. Burke, B. Sammuli, K. E. Thome, J.A. Reusch, D. J. Schlossberg, R. J. Fonck, N.W. Eidietis and D. M. Kriete and has published in prestigious journals such as Physical Review Letters, Physics of Plasmas and Nuclear Fusion.

In The Last Decade

J.L. Barr

34 papers receiving 250 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J.L. Barr United States 11 223 108 84 70 69 36 261
Hyunsun Han South Korea 10 256 1.1× 81 0.8× 60 0.7× 63 0.9× 122 1.8× 52 289
O. Kudláček Germany 9 252 1.1× 139 1.3× 92 1.1× 86 1.2× 61 0.9× 40 272
G. Harrer Germany 10 260 1.2× 121 1.1× 75 0.9× 60 0.9× 101 1.5× 22 294
W. Yan China 10 233 1.0× 91 0.8× 66 0.8× 63 0.9× 93 1.3× 51 275
Ruihai Tong China 10 287 1.3× 107 1.0× 80 1.0× 68 1.0× 121 1.8× 55 329
Z.Y. Cui China 7 198 0.9× 83 0.8× 44 0.5× 38 0.5× 89 1.3× 15 238
J. Havlíček Czechia 11 295 1.3× 117 1.1× 85 1.0× 114 1.6× 104 1.5× 51 336
Erik Olofsson United States 11 241 1.1× 56 0.5× 73 0.9× 88 1.3× 112 1.6× 29 264
N. Walkden United Kingdom 12 300 1.3× 143 1.3× 47 0.6× 66 0.9× 165 2.4× 25 349
T. Ravensbergen France 8 183 0.8× 120 1.1× 52 0.6× 52 0.7× 26 0.4× 19 219

Countries citing papers authored by J.L. Barr

Since Specialization
Citations

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

Fields of papers citing papers by J.L. Barr

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.L. Barr. 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 J.L. Barr. The network helps show where J.L. Barr may publish in the future.

Co-authorship network of co-authors of J.L. Barr

This figure shows the co-authorship network connecting the top 25 collaborators of J.L. Barr. A scholar is included among the top collaborators of J.L. Barr 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 J.L. Barr. J.L. Barr 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.
Lvovskiy, A., H. Anand, A.S. Welander, et al.. (2025). Framework for assessment of magnetic equilibrium controller performance on the MAST upgrade spherical tokamak. Plasma Physics and Controlled Fusion. 67(7). 75003–75003.
2.
Anand, H., W Wehner, D. Eldon, et al.. (2024). Real-time plasma equilibrium reconstruction and shape control for the MAST Upgrade tokamak. Nuclear Fusion. 64(8). 86051–86051. 5 indexed citations
3.
Penaflor, B.G., B. Sammuli, D.A. Piglowski, et al.. (2024). Recent Advancements in the DIII-D Plasma Control System. IEEE Transactions on Plasma Science. 52(9). 3535–3541.
4.
Hu, Qiming, N.C. Logan, C. Paz-Soldan, et al.. (2024). Non-disruptive error field measurement in DIII-D low safety factor plasmas and projection to ITER. Nuclear Fusion. 65(1). 16006–16006. 3 indexed citations
5.
Tang, W. M., Ge Dong, J.L. Barr, et al.. (2023). Implementation of AI/DEEP learning disruption predictor into a plasma control system. Contributions to Plasma Physics. 63(5-6). 2 indexed citations
6.
Sammuli, B., et al.. (2022). Neural Network-Based Confinement Mode Prediction for Real-Time Disruption Avoidance. IEEE Transactions on Plasma Science. 50(11). 4157–4164. 3 indexed citations
7.
Eldon, D., H. Anand, J.L. Barr, et al.. (2022). Enhancement of detachment control with simplified real-time modelling on the KSTAR tokamak. Plasma Physics and Controlled Fusion. 64(7). 75002–75002. 9 indexed citations
8.
Wu, Kai, Qiping Yuan, Guosheng Xu, et al.. (2021). The achievement of the T e,div feedback control by CD 4 seeding on EAST. Plasma Physics and Controlled Fusion. 63(10). 105004–105004. 4 indexed citations
9.
Barr, J.L., B. Sammuli, David Humphreys, et al.. (2021). Development and experimental qualification of novel disruption prevention techniques on DIII-D. Nuclear Fusion. 61(12). 126019–126019. 21 indexed citations
10.
Anand, H., D. Eldon, David Humphreys, et al.. (2021). Real-time estimation and control of divertor surface heat flux on the DIII-D tokamak. Fusion Engineering and Design. 171. 112560–112560. 3 indexed citations
11.
Rea, Cristina, Kevin Montes, Wenhui Hu, et al.. (2020). Interpretable data-driven disruption predictors to trigger avoidance and mitigation actuators on different tokamaks. APS Division of Plasma Physics Meeting Abstracts. 2020. 1 indexed citations
12.
Barr, J.L., N.W. Eidietis, & David Humphreys. (2020). Control Solutions Supporting Disruption Free Operation on DIIID and EAST. 1 indexed citations
13.
Yuan, Qiping, Kai Wu, J.C. Xu, et al.. (2020). The first implementation of active detachment feedback control in EAST PCS. Fusion Engineering and Design. 154. 111557–111557. 12 indexed citations
14.
Wehner, W, Eugenio Schuster, N.W. Eidietis, et al.. (2019). Integrated current profile, normalized beta and NTM control in DIII-D. Fusion Engineering and Design. 146. 559–562. 4 indexed citations
15.
Barr, J.L., et al.. (2018). A power-balance model for local helicity injection startup in a spherical tokamak. Nuclear Fusion. 58(7). 76011–76011. 9 indexed citations
16.
Sammuli, B., J.L. Barr, N.W. Eidietis, et al.. (2018). TokSearch: A search engine for fusion experimental data. Fusion Engineering and Design. 129. 12–15. 9 indexed citations
17.
Eidietis, N.W., J.L. Barr, S.H. Hahn, et al.. (2017). Control advances for achieving the ITER baseline scenario on KSTAR. APS. 2017. 1 indexed citations
18.
Thome, K. E., M.W. Bongard, J.L. Barr, et al.. (2016). High Confinement Mode and Edge Localized Mode Characteristics in a Near-Unity Aspect Ratio Tokamak. Physical Review Letters. 116(17). 175001–175001. 16 indexed citations
19.
Bongard, M.W., J.L. Barr, R. J. Fonck, J.A. Reusch, & K. E. Thome. (2016). Public Data Set: On Virial Analysis at Low Aspect Ratio. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 3 indexed citations
20.
Thome, K. E., M.W. Bongard, J.L. Barr, et al.. (2016). H-mode plasmas at very low aspect ratio on the Pegasus Toroidal Experiment. Nuclear Fusion. 57(2). 22018–22018. 15 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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