Position Start Date: Subject to availability
Application Deadline: Open until filled
Job Category: M.S. Project
Description:
Forested landscapes strongly influence the structure of the atmospheric boundary layer (ABL), modifying wind speed, turbulence intensity, and shear. These effects directly impact wind energy applications, where forest-induced drag and turbulence can alter available wind resources, turbine performance, and wake recovery. Understanding these interactions under real atmospheric conditions is essential to improve predictive models and optimize wind energy deployment in forested regions.
This M.S. project will focus on high-resolution field measurements using scanning Doppler lidars and instrumented UAVs. The aim is to characterize how forest structure and density affect flow patterns, turbulence, and the transport of momentum through and above forest canopies. Selected measurements may also capture interactions between forest-modified flows and wind turbines.
Your Responsibilities:
- Deploy and operate scanning Doppler lidars to capture wind velocity, turbulence intensity, and flow structure across forested terrain.
- Conduct UAV flights with fast-response sensors to measure three-dimensional wind components, turbulence, and vertical stratification within and above forest canopies.
- Collect and process large datasets, including lidar scans and UAV measurements, to reconstruct detailed spatial and temporal wind fields.
- Quantify forest effects such as drag, local speed-ups, turbulence intensity, and wake recovery in relation to canopy characteristics.
- Analyze the interaction between forest-modified flows and wind turbines, where feasible, to assess implications for energy production and turbine loading.
The project will provide hands-on experience in atmospheric field measurements, UAV operations, remote sensing, and data analysis, within the Wind Engineering and Renewable Energy Laboratory (WiRE) at EPFL. Students will gain skills applicable to renewable energy research, experimental fluid mechanics, and environmental wind studies.
Recommended Background:
Experience with experimental work and coding (e.g., Python, MATLAB) and solid foundation in fluid dynamics are highly recommended.
To express interest and obtain more information about the project, please contact:
|
Dr. Amr Khedr |
Prof. Fernando Porté-Agel |