Contact Information
Email: fslien@uwaterloo.ca
Phone: 519-888-4567 x36528, 519-888-4567 x36528
Location: ERC 2024, CPH 2376C
Professor, Mechanical & Mechatronics Engineering
Prof. Lien has 30 years of experience developing and systematically applying Computational Fluid Dynamics (CFD) to a wide range of fluid mechanics and multi-physics problems, such as wind energy, urban flow and dispersion modeling, aerodynamics, aeroacoustics and aero-elasticity. His current research interests in wind energy include wind turbine wakes, wake-induced fatigue analysis, wind turbine pitch control, wind turbine noise prediction/reduction, micro-siting of a wind farm and AI-based wind power forecasting.
Professor Lien is a member of IMMERSE – The Research Network for Video Game Immersion. His expertise lies in using simulations in virtual worlds to train first responders in response to chemical, biological, radiological, nuclear and explosive (CBRNE) catastrophic events, and is interested in working on improving the learning experience by employing serious game technologies. This is a significant achievement because this modeling system provides a new, unique comprehensive operational capability in Canada to support CBRNE response planning and response for civilian operations (both domestically and internationally). Future customers of the web-based wind energy software WATWindTM, currently in development, include Prof. Lien’s connections and collaborations with wind power companies and related research institutes in Canada, China and Taiwan.
Prof. Lien has supervised 21 MASc students, 22 PhD students and 9 PDFs in the past 24 years, working on research in different disciplines. These include wind energy (aeroacoustics, fatigue, wake effect, wind power forecasting), parallel computing (on CPUs and GPUs), turbulence closure modeling using machine learning, multiphase flow (air pollution, icing wind tunnel design, noise in vehicle EVAP system, dense particulate flow under explosive dispersal), serious game for fire evacuation, and development of meshless (SPH) algorithm.
Selected Publications:
- R. McConkey, E. Yee, F.S. Lien, “A Curated Dataset for Data-driven Turbulence Modelling”, Scientific Data, 2021, 8:255.
- H. Meng, F.S. Lien, E. Yee. J. Shen, “Modeling of Anisotropic Beam for Rotating Composite Wind Turbine Blade by Using Finite-difference Time-domain (FDTD) Method”, Renewable Energy, 162, 2020, 2361-2379.
- J. Zhang, D. Infield, J. Yan, Y. Liu, F.S. Lien, “Short Term Forecasting and Uncertainty Analysis of Wind Turbine Power Based on Long Short Term Memory Network and Gaussian Mixture Model”, Applied Energy, 241, 2019, 229-244.