Ye Tian

2.4k total citations
127 papers, 1.9k citations indexed

About

Ye Tian is a scholar working on Computational Mechanics, Aerospace Engineering and Applied Mathematics. According to data from OpenAlex, Ye Tian has authored 127 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 109 papers in Computational Mechanics, 89 papers in Aerospace Engineering and 10 papers in Applied Mathematics. Recurrent topics in Ye Tian's work include Computational Fluid Dynamics and Aerodynamics (88 papers), Combustion and flame dynamics (59 papers) and Rocket and propulsion systems research (31 papers). Ye Tian is often cited by papers focused on Computational Fluid Dynamics and Aerodynamics (88 papers), Combustion and flame dynamics (59 papers) and Rocket and propulsion systems research (31 papers). Ye Tian collaborates with scholars based in China, United States and Russia. Ye Tian's co-authors include Jialing Le, Fuyu Zhong, Shunhua Yang, Mingming Guo, Wen Shi, Spyros A. Kinnas, Shunping Zhang, Hua Zhang, Xiyao Wang and Ran Wei and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Hydrogen Energy and Expert Systems with Applications.

In The Last Decade

Ye Tian

118 papers receiving 1.8k citations

Peers

Ye Tian

FFTP

Jialing Le China

Jiajian Zhu China

Zun Cai China

David G. MacManus United Kingdom

Joseph Oefelein United States

Hongbo Wang China

Li Yan China

David Greenblatt Israel

Ananias Tomboulides Greece

Venkateswaran Sankaran United States

Jialing Le China

Ye Tian

1.5k ×0.9

1.3k ×1.1

155 ×0.9

FFTP

137 ×1.1

148 ×1.2

Citations per year, relative to Ye Tian Ye Tian (= 1×) peers Jialing Le

Countries citing papers authored by Ye Tian

Since Specialization

Citations

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

Fields of papers citing papers by Ye Tian

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ye Tian

This figure shows the co-authorship network connecting the top 25 collaborators of Ye Tian. A scholar is included among the top collaborators of Ye Tian 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 Ye Tian. Ye Tian is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Ying, et al.. (2025). Fluid modeling of the atmospheric pressure helium direct current microdischarge coupled with the two-term Boltzmann equation. Chinese Journal of Physics. 95. 298–305.

Tian, Ye, et al.. (2025). Study on atomization and evaporation characteristics of liquid jets under shock wave action in supersonic flow. International Journal of Heat and Fluid Flow. 112. 109762–109762. 1 indexed citations

Tian, Ye, et al.. (2025). Artificial intelligence in scramjet research: Applications and future outlook. Chinese Journal of Aeronautics. 39(4). 103695–103695. 1 indexed citations

Tian, Ye, et al.. (2025). Multi-knowledge reinforcement transfer framework: Solving supersonic combustion flow in extreme environments. Knowledge-Based Systems. 328. 114262–114262.

Guo, Mingming, Ye Tian, Yi Zhang, et al.. (2025). Fast prediction of flow field in scramjet combustor based on physical information neural network under wide Mach number. Chinese Journal of Aeronautics. 38(7). 103482–103482. 2 indexed citations

Tian, Ye, et al.. (2025). Research on the effects of shock waves induced by a ramp on combustion oscillation in a scramjet combustor. Acta Astronautica. 229. 616–626. 3 indexed citations

Tian, Ye, et al.. (2025). Supersonic combustion flow field reconstruction in a scramjet based on deep learning method. Aerospace Science and Technology. 161. 110169–110169. 1 indexed citations

Tian, Ye, et al.. (2025). Research on integrated design method of wide-range hypersonic vehicle/engine based on dynamic multi-objective optimization. Aerospace Science and Technology. 159. 110031–110031. 4 indexed citations

Tian, Ye, et al.. (2025). An intelligent prediction method for supersonic flow field in scramjet isolator enhanced by feature details. Aerospace Science and Technology. 161. 110116–110116. 1 indexed citations

10.

Tian, Ye, et al.. (2025). Investigation of self-circulation controlled mechanism on combustion characteristics in a scramjet combustor. Applied Thermal Engineering. 280. 128207–128207. 1 indexed citations

11.

Le, Jialing, et al.. (2024). Progress and prospects of artificial intelligence development and applications in supersonic flow and combustion. Progress in Aerospace Sciences. 151. 101046–101046. 11 indexed citations

12.

Guo, Mingming, Ye Tian, Linjing Li, et al.. (2024). Supersonic combustion flow field reconstruction based on multi-view domain adaptation generative network in scramjet combustor. Engineering Applications of Artificial Intelligence. 136. 108981–108981. 4 indexed citations

13.

Guo, Mingming, et al.. (2024). Supersonic combustion field evolution prediction in scramjet engine using a deblurring multi-scale attention network. Expert Systems with Applications. 252. 124290–124290. 5 indexed citations

14.

Guo, Mingming, et al.. (2024). Hypersonic inlet flow field reconstruction dominated by shock wave and boundary layer based on small sample physics-informed neural networks. Aerospace Science and Technology. 150. 109205–109205. 12 indexed citations

15.

Chen, Chen, et al.. (2024). Effects of pulse injection on the flow field structure and combustion performance in a kerosene-fueled supersonic combustor. Acta Astronautica. 226. 479–493. 1 indexed citations

16.

Guo, Mingming, et al.. (2024). Evolution prediction of flame structure in a hydrogen-fueled scramjet combustor based on lightweight deformable convolutional residual neural network. Engineering Applications of Artificial Intelligence. 138. 109435–109435. 4 indexed citations

17.

Tian, Ye, et al.. (2023). Intelligent reconstruction algorithm of hydrogen-fueled scramjet combustor flow based on knowledge distillation model compression. International Journal of Hydrogen Energy. 49. 1278–1291. 15 indexed citations

18.

Guo, Mingming, et al.. (2023). Uncertainty quantification and identification of SST turbulence model parameters based on Bayesian optimization algorithm in supersonic flow. International Journal for Numerical Methods in Fluids. 96(3). 277–296. 7 indexed citations

19.

Tian, Ye, et al.. (2023). Experimental study on the combustion process of a kerosene-fueled scramjet with strut injection. Aerospace Science and Technology. 137. 108300–108300. 17 indexed citations

20.

Wang, Ping, et al.. (2023). Large eddy simulation of a dual-mode scramjet with direct injection: Flame dynamics under flow separation induced by heat-release. Aerospace Science and Technology. 139. 108401–108401. 12 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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