Binghua Li, PhD in Aerospace Engineering and Fluid Mechanics, is currently an interim associate professor at the Hangzhou International Innovation Institute of Beihang University.

The main research directions are data science (machine learning algorithms, reduced-order models and modal analysis) and turbulent flow control (turbulence numerical simulation, drag reduction mechanism analysis and flow control technology), forming the featured research direction of AI4Science. He has published a total of 8 SCI papers in the Journal of Computational Physics, Physics of Fluids, Chinese Journal of Aeronautics and other journals, with a total of more than 100 citations. Published 4 EI papers, accepted more than ten patents, and 1 software book. As a core writer, he participated in writing the Chinese academic monograph "Turbulent Flow Control Drag Reduction Technology".

He has participated in 6 basic research and enterprise horizontal projects such as China-EU International Cooperation Project, Ministry of Industry and Information Technology Special Project, National Natural Science Foundation General Project, Youth Fund Project, etc. He has rich overseas study and exchange experience. He was awarded the Outstanding Doctoral Graduate and the highest honor (Sobresaliente cum laude) from the Polytechnic University of Madrid. He currently serves as a reviewer for top SCI journals such as Physics of Fluids and Ocean Engineering.

The team sincerely invite postdoctoral and master's students in fluids, machinery, computers, and transportation to join (enough positions). Undergraduate students are also very welcome to participate in and jointly conquer open competitions. For details, please refer to: ResearchGate Homepage, Team Homepage.

Main scientific publications：

[C3] Li, S.C., Zhang, L.L., Han, D.Y., Li, B.*, Wang H.W., & Kang, R. (2024). Capacity Prediction of Lithium-ion Batteries with Regeneration Phenomena Based on Hierarchical Modal Analysis. ICSRS 2024. (EI indexed)

[J5] Li, B., Garicano-Mena, J.*, & Valero, E. (2022). A dynamic mode decomposition technique for the analysis of non–uniformly sampled flow data. Journal of Computational Physics, 468, 111495. (Top SCI)

[J4] Li, B., Garicano-Mena, J.*, Zheng, Y., & Valero, E. (2020). Dynamic mode decomposition analysis of spatially agglomerated flow databases. Energies, 13(9), 2134. (SCI Q1)

[C2] Li, B.*, Garicano-mena, J., Zheng, Y., Valero, E. (2020). Feature Extraction from Turbulent Channel Flow of Moderate Reynolds Number via Composite DMD Analysis. Journal of Physics: Conference Series, 1600, 012028. (EI indexed)

[C1] Li, B.*, Garicano-Mena, J. & Valero, E. (2020). Feature Extraction from Turbulent Channel Flow Databases via Composite DMD Analysis. Journal of Physics: Conference Series, 1522, 012008. (EI indexed)

[J2] Garicano-Mena, J., Li, B.*, Ferrer E., and Valero E. (2019). A composite dynamic mode decomposition analysis of turbulent channel flows. Physics of Fluids, 31, no. 1: 115102. (Top SCI)

[J1] Li, B.*, Huang, X. W., Zheng, Y., Xie, F. F., Wang, J., & Zou, J. F. (2018). Performance of flapping airfoil propulsion with LBM method and DMD analysis. Modern Physics Letters B, 32(12n13), 1840024. (SCI Q2)