Yunfei

FU

傅雲飛

Ph.D. in Civil Engineering, The Hong Kong University of Science and Technology

Honors and Awards

•            第七届全国风工程研究生论坛优秀论文奖, 2023

•            参编中国工程建设标准化协会标准T/CECS 10378-2024《建筑用辐射制冷涂料》

Selected Publications

[1]         Fu, Y., Wang, Y. Yang, P., Li, Y., Liu, H., Tim, K.T., Li, C. Y., He, K., & Zhang, B. (2025). Gap flow dynamics and air pollutant dispersion mechanism behind building clusters. Physics of Fluids, 37, 035214. https://doi.org/10.1063/5.0255849

[2]         Lin, C., Wang, Y., Zhao, Z., Lin, X., Li, R., Zheng, Z., Shen, X., Lang, Z., Zhou, W., Wang, J., Yuan, D., Sun, Y., Liu, H., Tse, T. K. T., Li, C. Y., & Fu, Y. (2024). Comprehensive Investigation of Daytime Radiative Cooling Technology for Sustainable Grain Storage: A Combined Approach of Field Measurement and CFD Simulations. Building and Environment, 265, 111982. https://doi.org/10.1016/j.buildenv.2024.111982

[3]         Fu, Y., Li, C. Y., Zhao, Z., Zhang, B., Tse, T. K. T., Mak, C. M., Chen, Z., Feng, X., Lin, X., Li, W. & Lin, C. (2024) Energetic and dynamic characterization of pollutant dispersion in varied building layouts through an augmented analysis procedure. Physics of Fluids, 36(3), 035105. https://doi.org/10.1063/5.0190268

[4]         Lin, X., Fu, Y., Peng, Z., Liu, C. H., Chu, M., Chen, Z., Xue X., Hua, J., Yang, F., Tse, T. K. T., Li, C. Y. & Feng, X. (2023). CFD- and BPNN- based investigation and prediction of air pollutant dispersion in urban environment. Sustainable Cities and Society, 100, 105029. https://doi.org/10.1016/j.scs.2023.105029

[5]         Fu, Y., Lin, X., Li, L., Chu, M., Liu, C. H., Chen, Z., Li, C. Y. & Tse, T. K. T. (2023). The NOx-O3 photochemical reactive air pollutant dispersion around an isolated building—the role of turbulence model and building aspect ratio. Building and Environment, 245, 110906. https://doi.org/10.1016/j.buildenv.2023.110906

[6]         Li, C. Y., Chen, Z., Weerasuriya, A. U., Zhang, X., Lin, X., Zhou, L., Fu, Y. & Tse, T. K. T. (2023). Best practice guidelines for the dynamic mode decomposition from a wind engineering perspective. Journal of Wind Engineering and Industrial Aerodynamics, 241, 1105506. https://doi.org/10.1016/j.jweia.2023.105506

[7]         Wang, Y., Liu, H., Lei, M., Chen, Z., Tim, K. T., Li, C. Y., & Fu, Y. (2023). Aerodynamic analysis of an ultra–long and ultra–wide industrial building under wind loading: Insights into flow dynamics and pressure distribution characteristics. Journal of Building Engineering, 76, 107144. https://doi.org/10.1016/j.jobe.2023.107144

[8]         Fu, Y., Lin, X., Zheng, X., Wang, L., Liu, C. H., Zhang, X., Li, C. Y., & Tse, T. K. T. (2023). Physio-chemical modelling of the NOx-O3 photochemical cycle and the air pollutants’ reactive dispersion around an isolated building. Building Simulation, 16, 1735-1758. https://doi.org/10.1007/s12273-023-1042-0

[9]         Fu, Y., Lin, X., Li, L., Chu, Q., Liu, H., Zheng, X., Liu, C., & Li, C. Y. (2023). A POD-DMD augmented procedure to isolating dominant flow field features in a street canyon. Physics of Fluids, 35(2). https://doi.org/10.1063/5.0133375

[10]       Li, C. Y., Chen, Z., Lin, X., Weerasuriya, A. U., Zhang, X., Tse, T. K. T., & Fu, Y. (2022). The linear-time-invariance notion of the Koopman Analysis: the architecture, pedagogical rendering, and fluid-structure association. Physics of Fluids, 34(12), 125136. https://doi.org/10.1063/5.0124914                                                                                                                                             

[11]       Lin, C., Ma, W., Zhang, Y., LAW, M.-K., Li, C.Y., Li, Y., Chen, Z., Li, K., Li, M., Zheng, J., Fu, Y., Yan, X., Chi, C., Yang, J., Li, W., Yao, S. and Huang, B. (2023), A Highly Transparent Photo-Electro-Thermal Film with Broadband Selectivity for All-Day Anti-/De-Icing. Small, 19: 2301723. https://doi.org/10.1002/smll.202301723

[12]       Li, W., Mak, C. M., Fu, Y., Cai, C., Tse, K. T., & Niu, J. (2024). The impact of twisted wind on pedestrian comfort around two non-identical-height buildings in tandem arrangement: A wind tunnel study. Building and Environment, 262, 111847. https://doi.org/10.1016/j.buildenv.2024.111847

[13]       Zhang, B., Wen, L., Zhang, X., Fu, Y., Tim, K. T., & Mak, C. M. (2024). Enhanced modelling of vehicle-induced turbulence and pollutant dispersion in urban street canyon: Large-eddy simulation via dynamic overset mesh approach. Sustainable Cities and Society, 105939. https://doi.org/10.1016/j.scs.2024.105939

[14]       Li, W., Mak, C. M., Fu, Y., Cai, C., Tse, K. T., Niu, J., & Wong, S. H. Y. (2024). Pedestrian-level wind environment surrounding two tandem non-identical height elevated buildings under the influence of twisted wind flows. Sustainable Cities and Society, 112, 105641. https://doi.org/10.1016/j.scs.2024.105641

[15]       Li, C. Y., Zhang, L., Li, S., Zhang, X., Chen, Z., Fu, Y., Lin, X., Peng, D. Z., Wang, Y., Zhang, B., Zhou, L., Wang, Y., Liu, H., Weerasuriya, A. U., Tse, T. K. T. & Yang, Q. (2024) Koopman-inspired data-driven quantification of fluid-structure energy transfers. Physics of Fluids, 36, 095102. https://doi.org/10.1063/5.0219635

[16]       Chen, Z., Guan, T., Zhang, L., Li, S., Kim, B., Xu, Y., Fu, Y. & Li, C. Y. (2024) The flow interference investigation of multi-square prisms under fluid-structure interaction. II. Flow field phenomenology of side-by-side square prisms. Physics of Fluids, 36, 075138. https://doi.org/10.1063/5.0210021

[17]       Chen, Z., Guan, T., Zhang, L., Li, S., Kim, B., Fu, Y., Li, C. Y. & Zhang, X. (2024) The flow interference investigation of multi-square prisms under fluid-structure interaction. I. Proximal wake characteristics of tandem square prisms. Physics of Fluids, 36, 075137. https://doi.org/10.1063/5.0201581

[18]       Li, W., Mak, C. M., Cai, C., Fu, Y., Tse, K. T., & Niu, J. (2024). Wind tunnel measurement of pedestrian-level gust wind flow and comfort around irregular lift-up buildings within simplified urban arrays. Building and Environment, 256, 111487. https://doi.org/10.1016/j.buildenv.2024.111487

[19]       Chen, Z., Li, D., Li, S., Bai, J., Li, C. Y.*, Wang, H., Fu, Y. & Tse, T. K. T. (2023). Experimental and numerical investigation on the aerodynamics of isolated high-rise building and phenomenology of twisted wind field. Engineering Applications of Computational Fluid Mechanics, 17(1), 2264351. https://doi.org/10.1080/19942060.2023.2264351

[20]       Xu, Y., Li, C. Y., Chen, Z., Tse, K. T., Huang, L., Xue, X., Hua, J., & Fu, X. (2023). Isolation, decomposition, and mechanisms of the aerodynamic nonlinearity and flow field phenomenology of structure-motion-induced dynamics in fluid-structure interactions. Physics of Fluids, 35(4), 47125. https://doi.org/10.1063/5.0147851

[21]       Li, C. Y., Chen, Z., Tse, T. K. T., Weerasuriya, A. U., Zhang, X., Fu, Y., & Lin, X. (2023). The Linear-Time-Invariance Notion of the Koopman Analysis - Part 2: Physical Interpretations of Invariant Koopman Modes and Phenomenological Revelations. Journal of Fluid Mechanics, 959, A15. https://doi.org/10.1017/jfm.2023.36

[22]       Brimblecombe, P., Chu, M., Liu, C. H., Fu, Y., Wei, P., & Ning, Z. (2023). Roadside NO2/NOx and primary NO2 from individual vehicles. Atmospheric Environment, 295, 119562. https://doi.org/10.1016/j.atmosenv.2022.119562

[23]       Li, L., Fu, Y., Fung, J. C., Tse, K. T., & Lau, A. K. (2022). Development of a back-propagation neural network combined with an adaptive multi-objective particle swarm optimizer algorithm for predicting and optimizing indoor CO2 and PM2. 5 concentrations. Journal of Building Engineering, 54, 104600. https://doi.org/10.1016/j.jobe.2022.104600

[24]       Li, C. Y., Chen, Z., Tse, T. K. T., Weerasuriya, A. U., Zhang, X., Fu, Y., & Lin, X. (2022). A Parametric and Feasibility Study for Data Sampling of the Dynamic Mode Decomposition: Spectral Insights and Further Explorations. Physics of Fluids, 34(3), 035102. https://doi.org/10.1063/5.0082640

[25]       Li, C. Y., Chen, Z., Tse, T. K. T., Weerasuriya, A. U., Zhang, X., Fu, Y., & Lin, X. (2022). A parametric and feasibility study for data sampling of the dynamic mode decomposition: range, resolution, and universal convergence states. Nonlinear Dynamics, 107(4), 3683-3707. https://doi.org/10.1007/s11071-021-07167-8

[26]       Li, C. Y., Chen, Z., Tse, T. K. T., Weerasuriya, A. U., Zhang, X., Fu, Y., & Lin, X. (2021). Establishing direct phenomenological connections between fluid and structure by the Koopman-Linearly Time-Invariant analysis. Physics of Fluids, 33(12), 121707. https://doi.org/10.1063/5.0075664

[27]       Li, L., Fu, Y., Fung, J. C., Qu, H., & Lau, A. K. (2021). Development of a back-propagation neural network and adaptive grey wolf optimizer algorithm for thermal comfort and energy consumption prediction and optimization. Energy and Buildings, 253, 111439. https://doi.org/10.1016/j.enbuild.2021.111439

[28]       Gan, W., He, Y., Hu, P., Fu, Y., Yin, Y., & Feng, C. (2025). Predicting the surface temperature of radiative cooling coatings with time-series forecasting: Validation of the small-batch training dataset and implementation of a hyperparameter optimization strategy. Renewable Energy, 123351. https://doi.org/10.1016/j.renene.2025.123351