Brian Kaul

1.1k total citations
57 papers, 851 citations indexed

About

Brian Kaul is a scholar working on Fluid Flow and Transfer Processes, Computational Mechanics and Automotive Engineering. According to data from OpenAlex, Brian Kaul has authored 57 papers receiving a total of 851 indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Fluid Flow and Transfer Processes, 32 papers in Computational Mechanics and 17 papers in Automotive Engineering. Recurrent topics in Brian Kaul's work include Advanced Combustion Engine Technologies (43 papers), Combustion and flame dynamics (31 papers) and Vehicle emissions and performance (16 papers). Brian Kaul is often cited by papers focused on Advanced Combustion Engine Technologies (43 papers), Combustion and flame dynamics (31 papers) and Vehicle emissions and performance (16 papers). Brian Kaul collaborates with scholars based in United States, United Kingdom and Spain. Brian Kaul's co-authors include James P. Szybist, Derek Splitter, J. A. Drallmeier, Robert Wagner, S. Jagannathan, Charles Finney, Benjamin Lawler, Johney B. Green, K. Dean Edwards and C. Stuart Daw and has published in prestigious journals such as Applied Energy, Energy Conversion and Management and Fuel.

In The Last Decade

Brian Kaul

52 papers receiving 811 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Brian Kaul 545 356 298 208 115 57 851
Amir H. Shamekhi 591 1.1× 248 0.7× 412 1.4× 281 1.4× 162 1.4× 73 954
Yuichi Goto 806 1.5× 396 1.1× 561 1.9× 438 2.1× 240 2.1× 146 1.3k
Harry C. Watson 796 1.5× 541 1.5× 378 1.3× 252 1.2× 94 0.8× 64 1.0k
Michael Traver 809 1.5× 529 1.5× 421 1.4× 261 1.3× 90 0.8× 60 971
J. A. Drallmeier 356 0.7× 489 1.4× 165 0.6× 115 0.6× 25 0.2× 62 785
Hechun Wang 625 1.1× 217 0.6× 291 1.0× 242 1.2× 357 3.1× 63 928
Pinaki Pal 786 1.4× 738 2.1× 260 0.9× 144 0.7× 83 0.7× 81 1.1k
Zongyu Yue 701 1.3× 508 1.4× 281 0.9× 252 1.2× 167 1.5× 53 941
Francesco Mariani 420 0.8× 287 0.8× 167 0.6× 88 0.4× 63 0.5× 57 641
Timothy J. Callahan 806 1.5× 451 1.3× 310 1.0× 557 2.7× 113 1.0× 68 1.6k

Countries citing papers authored by Brian Kaul

Since Specialization
Citations

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

Fields of papers citing papers by Brian Kaul

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Brian Kaul

This figure shows the co-authorship network connecting the top 25 collaborators of Brian Kaul. A scholar is included among the top collaborators of Brian Kaul 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 Brian Kaul. Brian Kaul 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.
Curran, Scott, et al.. (2025). Mapping ammonia-diesel combustion on a single-cylinder 107 mm bore diesel engine retrofitted for ammonia port-fuel injection. International Journal of Engine Research. 26(12). 1951–1962.
2.
3.
Kaul, Brian, et al.. (2024). Reinforcement learning applied to dilute combustion control for increased fuel efficiency. International Journal of Engine Research. 25(6). 1157–1173. 2 indexed citations
4.
Curran, Scott, Angelo Onorati, Raúl Payri, et al.. (2023). The future of ship engines: Renewable fuels and enabling technologies for decarbonization. International Journal of Engine Research. 25(1). 85–110. 30 indexed citations
5.
Kaul, Brian, et al.. (2023). Analysis of Real-World Preignition Data Using Neural Networks. SAE technical papers on CD-ROM/SAE technical paper series. 1. 1 indexed citations
6.
Chuahy, Flavio Dal Forno, Charles Finney, Brian Kaul, & Michael D. Kass. (2022). Computational exploration of bio-oil blend effects on large two-stroke marine engines. Fuel. 322. 123977–123977. 11 indexed citations
7.
Curran, Scott, et al.. (2021). Fuel Stratification Effects on Gasoline Compression Ignition with a Regular-Grade Gasoline on a Single-Cylinder Medium-Duty Diesel Engine at Low Load. SAE International Journal of Advances and Current Practices in Mobility. 4(2). 488–501. 11 indexed citations
8.
Edwards, K. Dean, Scott Curran, Melanie Moses‐DeBusk, et al.. (2021). Exploring the potential benefits of high-efficiency dual-fuel combustion on a heavy-duty multi-cylinder engine for SuperTruck I. International Journal of Engine Research. 23(6). 1082–1099. 4 indexed citations
9.
Kaul, Brian, et al.. (2020). Advanced Intra-Cycle Detection of Pre-Ignition Events through Phase-Space Transforms of Cylinder Pressure Data. SAE International Journal of Advances and Current Practices in Mobility. 3(1). 215–222. 4 indexed citations
10.
Schuman, Catherine D., et al.. (2020). Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 1–8. 12 indexed citations
11.
Kass, Michael D., Beth L. Armstrong, Brian Kaul, et al.. (2020). Stability, Combustion, and Compatibility of High-Viscosity Heavy Fuel Oil Blends with a Fast Pyrolysis Bio-Oil. Energy & Fuels. 34(7). 8403–8413. 66 indexed citations
12.
Kaul, Brian, et al.. (2020). Control-Oriented Modeling of Cycle-to-Cycle Combustion Variability at the Misfire Limit in SI Engines. OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information). 8 indexed citations
13.
Szybist, James P., Josh A. Pihl, Shean Huff, & Brian Kaul. (2019). High Load Expansion of Catalytic EGR-Loop Reforming under Stoichiometric Conditions for Increased Efficiency in Spark Ignition Engines. SAE International Journal of Advances and Current Practices in Mobility. 1(2). 588–600. 5 indexed citations
14.
15.
Kaul, Brian, et al.. (2017). Determination of SI Combustion Sensitivity to Fuel Perturbations as a Cyclic Control Input for Highly Dilute Operation. SAE International Journal of Engines. 10(3). 1011–1018. 8 indexed citations
16.
Kaul, Brian, et al.. (2009). Reinforcement-Learning-Based Output-Feedback Control of Nonstrict Nonlinear Discrete-Time Systems With Application to Engine Emission Control. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics). 39(5). 1162–1179. 33 indexed citations
17.
Kaul, Brian, et al.. (2009). A method for predicting performance improvements with effective cycle-to-cycle control of highly dilute spark ignition engine combustion. Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering. 223(3). 423–438. 19 indexed citations
18.
Kaul, Brian. (2008). Addressing nonlinear combustion instabilities in highly dilute spark ignition engine operation. La Revue du praticien. 9(8). 845–8 passim. 1 indexed citations
19.
Kaul, Brian, et al.. (2008). Neuro emission controller for minimising cyclic dispersion in spark ignition engines with EGR levels1. International Journal of General Systems. 38(1). 45–72. 5 indexed citations
20.
Kaul, Brian, et al.. (2006). Neural Network Control of Spark Ignition Engines with High EGR Levels. The 2006 IEEE International Joint Conference on Neural Network Proceedings. 4978–4985. 13 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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