Staff Profile

Biography

Dr. Asiri Umenga Weerasuriya has obtained BSc. (Hons) and MSc in Civil Engineering at the University of Moratuwa, Sri Lanka in 2008 and 2011 prior joing the Hong Kong University of Science and Technology (HKUST), Hong Kong in 2011. He has earned a PhD in Civil Engineering from HKUST in 2011. Dr. Asiri's research interests are broadly in Civil and Wind Engineering with special research areas of wind loading of buildings, pedestrian level wind environment, natural ventilation of buildings, air pollution dispersion in built-up areas, building design optimization, and expalinable machine learning in wind engineering applications. Dr. Asiri is an expert user in computational fluid dynamics (CFD) simualtions, experienced wind tunnel modeler, and  avid explorer in machine learning applications in wind engineering. 

Teaching Areas & Research Interests

• Pedestrian-level wind environment
• Air Ventilation Assessment
• Natural ventilation of buildings
• Air pollution dispersion in built-up areas
• Explainable machine learning

Academic & Professional Experience

Research Associate (2019 December - 2022 July),  The Hong Kong University of Science and Technology
Senior Research Associate (2018 December - 2019 November) - The University of Hong Kong

Postdoctoral Fellow (2016 Febriary - 2018 November) - The Hong Kong University of Science and Technology

Teaching Assistant (2011 September - 2015 August) - The Hong Kong University  of Science and Technology

Lecturer (2011  February - 2011 August) - The University of Moratuwa, Sri Lanka

Selected Publications

Journal Articles

• Tse, K. T., Weerasuriya, A. U.*, & Kwok, K. C. S. (2016). Simulation of twisted wind flows in a boundary layer wind tunnel for pedestrian-level wind tunnel tests. Journal of Wind Engineering and Industrial Aerodynamics, 159, 99-109 (https://doi.org/10.1016/j.jweia.2016.10.010).
• Weerasuriya, A. U., Tse, K. T., Zhang, X., & Kwok, K. C. S. (2018). Equivalent wind incidence angle method: A new technique to integrate the effects of twisted wind flows to AVA. Building and Environment, 139, 46-57 (https://doi.org/10.1016/j.buildenv.2018.05.017).
• Weerasuriya, A. U., Zhang, X., Lu, B., Tse, K. T., & Liu, C. H. (2020). Optimizing Lift-up Design to Maximize Pedestrian Wind and Thermal Comfort in 'Hot-Calm' and 'Cold-Windy' Climates. Sustainable Cities and Society, 102146 (https://doi.org/10.1016/j.scs.2020.102146).
• Weerasuriya, A. U., Zhang, X., Gan, V. J., & Tan, Y. (2019). A holistic framework to utilize natural ventilation to optimize energy performance of residential high-rise buildings. Building and Environment, 153, 218-232 (https://doi.org/10.1016/j.buildenv.2019.02.027)
• Weerasuriya, A. U., Zhang, X., Tse, K. T., Liu, C. H, & Kwok, K. C. S. (2022), RANS simulation of near-field dispersion of reactive air pollutants, Building and Environment, (https://doi.org/10.1016/j.buildenv.2021.108553).
• Meddage, D. P. P., Ekanayake, I. U., Weerasuriya, A. U.*, Lewangamage, C. S., Tse, K. T., Miyanawala, T. P., Ramanayaka, C. D. E. (2022). Explainable Machine Learning (XML) to Predict External Wind Pressure of a Low-Rise Building in Urban-Like Settings, Journal of Wind Engineering and Industrial Aerodynamics. 226. (https://doi.org/10.1016/j.jweia.2022.105027).

Further Information

Link to personal homepage

Link to Google Scholar page

Modified Date: 21 Jan, 2023
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