Seminar Series - Graduate Students - Dr. Meilin Yu's lab
Christiana Sasser and Md Badrul Hassan will present
Wind Power Prediction from Meteorological Characterization with Machine Learning Model
Christiana Sasser, NOAA EPP/Earth System Sciences and Remote Sensing Scholar, MS. Mechanical Engineering '20
To assess and improve upon the wind energy industry phenomenon known as the wind farm under performance bias, several power prediction methods are compared. These methods include, Manufacturer’s Power Curve (MPC), Rotor Equivalent Wind Speed (REWS) power estimate, and a machine learning model analyzing varying atmospheric criterion. The machine learning model is based on the decision tree technology. It analyzes wind speed, wind profile shear, wind profile shape, and environmental lapse rate as predictors to improve wind turbine power prediction certainty. Data from a Windcube 200s Scanning Doppler Lidar, Meteorological Tower, and 2MW turbine from the VERTical Enhanced miXing (VERTEX) campaign are used for analysis.
Christiana Sasser is a NOAA-EPP Earth System Sciences and Remote Sensing Scholar. She received her Bachelor of Science degree in Mechanical Engineering in 2018 and is currently pursuing her Master of Science in Mechanical Engineering, both at the University of Maryland, Baltimore County. Ms. Sasser is co-advised by Dr. Ruben Delgado at the Joint Center for Earth Systems Technology (JCET) and Dr. Meilin Yu at the Department of Mechanical Engineering. She has research interest in wind energy, remote sensing, and atmospheric relationships. Ms. Sasser has presented previous research at the 2017 American Meteorological Society Annual Meeting and the 2017 international Symposium for Tropospheric Profiling.
The Effects of Numerical Dissipation on Simulating Hurricane Intensification in a Realistic Regime
Md Badrul Hasan, Ph.D. candidate
The vortex response to heating in convective clouds, provided by warm ocean waters, is the fundamental physics controlling the energy input to tropical cyclones. Dissipation of energy occurs at the surface and through turbulent eddies of various scales most predominantly in the eyewall and boundary layer region. In numerical models, the dissipation of energy can also occur from the
dynamic core with recent work showing this effect can be significant in theoretical studies with the Weather Research and Forecasting (WRF) model showing anomalously high dissipation relative to research codes (Guimond et al. 2016).
In this work, we extend the results of the above study to a more realistic regime characterized by 4-D heating sources calculated from airborne Doppler radar observations. The initial condition is a balanced, tropical storm-like vortex with forcing from the observational heating. Two numerical models are examined: The Weather Research and Forecasting (WRF) model and the Nonhydrostatic Unified Model of the Atmosphere (NUMA). Simple, constant explicit diffusion for all prognostic variables and localized diffusion based on the heating are used to examine sensitivities.
The results show that the WRF wind field is significantly spread out and diffused relative to that in NUMA. Sensitivity tests show that this spreading of the wind field is not due to the default upwind biased advection scheme or the large/small timesteps.
After completing his undergraduate from Bangladesh University of Engineering and Technology (BUET) in 2017, Mr. Badrul Hasan joined the PhD program on Mechanical Engineering at UMBC in the 2019 Spring semester under the supervision of Dr. Meilin Yu. His research interests are in high-fidelity numerical simulation, machine/deep learning, and their applications to environmental fluid dynamics. From the 2020 Spring semester, he was co-supervised by Dr. Stephen Guimond at the Joint Center for Earth Systems Technology (JCET-UMBC) working on analyzing the effects of numerical dissipation from different numerical weather prediction models on hurricane intensification simulation.