Balaji Mohan

2.4k total citations
83 papers, 2.0k citations indexed

About

Balaji Mohan is a scholar working on Fluid Flow and Transfer Processes, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, Balaji Mohan has authored 83 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Fluid Flow and Transfer Processes, 46 papers in Computational Mechanics and 17 papers in Aerospace Engineering. Recurrent topics in Balaji Mohan's work include Advanced Combustion Engine Technologies (60 papers), Combustion and flame dynamics (41 papers) and Biodiesel Production and Applications (14 papers). Balaji Mohan is often cited by papers focused on Advanced Combustion Engine Technologies (60 papers), Combustion and flame dynamics (41 papers) and Biodiesel Production and Applications (14 papers). Balaji Mohan collaborates with scholars based in Saudi Arabia, Singapore and United States. Balaji Mohan's co-authors include Wenming Yang, S.K. Chou, Wenbin Yu, Kun Lin Tay, Feiyang Zhao, K.J. Chua, Jihad Badra, Hong G. Im, R. Vallinayagam and Vedharaj Sivasankaralingam and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Applied Energy and International Journal of Heat and Mass Transfer.

In The Last Decade

Balaji Mohan

77 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Balaji Mohan Saudi Arabia 27 1.5k 895 788 441 413 83 2.0k
Yuqiang Li China 23 1.0k 0.7× 448 0.5× 969 1.2× 459 1.0× 431 1.0× 77 1.9k
Selahaddin Orhan Akansu Türkiye 17 1.1k 0.8× 487 0.5× 720 0.9× 488 1.1× 606 1.5× 56 1.7k
Kihyung Lee South Korea 25 1.5k 1.0× 865 1.0× 652 0.8× 474 1.1× 798 1.9× 184 2.3k
Long Liu China 21 972 0.7× 491 0.5× 439 0.6× 573 1.3× 378 0.9× 84 1.7k
Ali M.A. Attia Egypt 20 1.1k 0.7× 424 0.5× 1.2k 1.5× 349 0.8× 166 0.4× 36 1.7k
Fanhua Ma China 28 1.7k 1.1× 764 0.9× 657 0.8× 545 1.2× 946 2.3× 79 2.1k
Amin Paykani Iran 20 1.1k 0.7× 602 0.7× 519 0.7× 395 0.9× 622 1.5× 51 1.5k
Jiaying Pan China 33 2.1k 1.4× 1.5k 1.7× 463 0.6× 706 1.6× 534 1.3× 122 2.7k
Scott Curran United States 24 1.3k 0.9× 710 0.8× 549 0.7× 465 1.1× 869 2.1× 70 1.7k
Amir-Hasan Kakaee Iran 18 802 0.5× 448 0.5× 398 0.5× 263 0.6× 538 1.3× 41 1.3k

Countries citing papers authored by Balaji Mohan

Since Specialization
Citations

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

Fields of papers citing papers by Balaji Mohan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Balaji Mohan

This figure shows the co-authorship network connecting the top 25 collaborators of Balaji Mohan. A scholar is included among the top collaborators of Balaji Mohan 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 Balaji Mohan. Balaji Mohan 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.
Im, Hong G., Xinlei Liu, Moez Ben Houidi, et al.. (2025). Toward H 2 ICE: Experimental and computational characterization of hydrogen injection, mixing, and combustion. International Journal of Engine Research.
2.
Liu, Xinlei, Mickael Silva, Emre Cenker, et al.. (2025). Assessment of combustion models in hydrogen engine simulations using optical measurements. Fuel. 392. 134871–134871. 6 indexed citations
3.
4.
5.
Liu, Xinlei, Balaji Mohan, Mickael Silva, et al.. (2025). Assessment of piston and injector cap designs on the performance of a hydrogen direct-injection spark-ignition engine. Applied Thermal Engineering. 271. 126372–126372. 7 indexed citations
6.
Yalamanchi, Kiran K., Pinaki Pal, Balaji Mohan, et al.. (2025). A Variational Autoencoder Model Toward Molecular Structure Representation Learning of Fuels. 1(5).
7.
Mohan, Balaji & Abdullah S. AlRamadan. (2024). Estimation of cetane number using machine learning. Fuel. 381. 133462–133462. 3 indexed citations
8.
Nagaraja, Shashank S., S. Mani Sarathy, Balaji Mohan, & Junseok Chang. (2024). Machine learning-driven screening of fuel additives for increased spark-ignition engine efficiency. Proceedings of the Combustion Institute. 40(1-4). 105658–105658. 4 indexed citations
9.
Mohan, Balaji, et al.. (2024). Experimental Investigation on Performance Characteristics o f Diesel Engine Using Various Blended Biodiesels. Indian Journal of Science and Technology. 17(37). 3865–3870. 1 indexed citations
10.
Liu, Xinlei, Mickael Silva, Balaji Mohan, et al.. (2023). Computational optimization of the performance of a heavy-duty natural gas pre-chamber engine. Fuel. 352. 129075–129075. 24 indexed citations
11.
Ahmad, Nabeel, et al.. (2023). Predicting physical properties of oxygenated gasoline and diesel range fuels using machine learning. Alexandria Engineering Journal. 76. 193–219. 17 indexed citations
12.
AlRamadan, Abdullah S., et al.. (2022). Machine Learning Model for Spark-Assisted Gasoline Compression Ignition Engine. SAE International Journal of Advances and Current Practices in Mobility. 5(2). 509–516. 4 indexed citations
13.
Liu, Xinlei, et al.. (2022). Investigation of the Cryogenic Nitrogen and Non-Cryogenic N-Dodecane and Ammonia Injections using a Real-Fluid Modelling Approach. SAE International Journal of Advances and Current Practices in Mobility. 5(3). 1129–1141. 1 indexed citations
14.
Silva, Mickael, Moez Ben Houidi, Xinlei Liu, et al.. (2022). Comparative Study of Spark-Ignited and Pre-Chamber Hydrogen-Fueled Engine: A Computational Approach. Energies. 15(23). 8951–8951. 27 indexed citations
15.
Mohan, Balaji, Jihad Badra, Jaeheon Sim, & Hong G. Im. (2020). Coupled in-nozzle flow and spray simulation of Engine Combustion Network Spray-G injector. International Journal of Engine Research. 22(9). 2982–2996. 18 indexed citations
16.
Du, Jianguo, Balaji Mohan, Jaeheon Sim, & William L. Roberts. (2019). Experimental study on the non-reacting spray characterization of gasoline compression ignition fuel. Energy Procedia. 158. 1968–1973. 6 indexed citations
17.
Ridgway, Harry, Balaji Mohan, Xin Cui, K.J. Chua, & M.R. Islam. (2017). Molecular dynamics simulation of gas-phase ozone reactions with sabinene and benzene. Journal of Molecular Graphics and Modelling. 74. 241–250. 10 indexed citations
18.
Cui, Xin, Balaji Mohan, M.R. Islam, & K.J. Chua. (2017). Investigating the energy performance of an air treatment incorporated cooling system for hot and humid climate. Energy and Buildings. 151. 217–227. 10 indexed citations
19.
Mohan, Balaji, Shaligram Tiwari, & M.P. Maiya. (2014). Experimental investigations on performance of liquid desiccant-vapor compression hybrid air conditioner. Applied Thermal Engineering. 77. 153–162. 26 indexed citations
20.
Mohan, Balaji, Wenming Yang, & S.K. Chou. (2013). Fuel injection strategies for performance improvement and emissions reduction in compression ignition engines—A review. Renewable and Sustainable Energy Reviews. 28. 664–676. 258 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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