Phd on Meso–micro high-fidelity wind-farm emulation

Candidates have a master degree in one of the following or related fields: fluid mechanics, aerospace engineering, mathematical engineering, mechanical engineering, or computational physics. They should have a good background or interest in wind energy, fluid mechanics, simulation, and programming (Fortran, C/C++, Python, …). Proficiency in English is a requirement. The position adheres to the European policy of balanced ethnicity, age and gender. Persons of all origins and gender are encouraged to apply.BACKGROUND
Today’s modern wind farms require multi-billion euro investments in which several parties are involved (operator, OEM, banks, regulators, insurers, …). As a result, testing of novel wind-farm control ideas in the field is simply not possible without building strong evidence that the new method is safe and effective. Wind tunnel experiments of new concepts may be considered, but because of scaling, the involved physical time scales typically decrease by two orders of magnitude, so that testing of control algorithms in a wind tunnel environment requires algorithms that are a significantly faster than they need to be in the field. Instead, wind-farm emulators using fine-grid large-eddy simulations in combination with aeroelastic multi-body representations of the wind turbines have been considered in recent years, requiring extensive use of supercomputing. However, these type of simulations have been typically driven by idealized boundary conditions that do not represent the complexity and variety of atmospheric inflows that exist in the field.

PHD PROJECT DESCRIPTION
Research aims at developing a wind-farm control emulator by coupling large-eddy simulations and aeroelastic turbine models with meso-scale weather models to run realistic wind-farm control scenario’s. The focus of the research is on developing efficient coupling techniques that cover the wide range of time scales involved (hours down to milliseconds). These are used to test and compare wind-farm control strategies over a wide range of relevant atmospheric conditions. Furthermore, the virtual environment is used to develop/prepare field-campaign strategies that are effective in quantifying control gains in full-scale experiments, given atmospheric uncertainty, and the lack of an uncontrolled baseline available for the exact same conditions.A PhD positions is available, supervised by Prof. Johan Meyers and hosted in the Turbulent Flow Simulation and Optimization (TFSO) research group at the department of Mechanical Engineering of KU Leuven, and co-supervised by Prof. Nicole van Lipzig of the department of Earth and Environmental Sciences. The research is part of the ERC Advanced Grant “Real-time optimal control of wind-farm atmosphere interaction (REALTOWIND)” led by Prof. Johan Meyers. The research is also integrated into the broader wind-farm control research of the TFSO group at KU Leuven, which is one of the leading research groups on wind farms worldwide. ERC is the premier European funding organization for excellent frontier research. It’s mission is to encourage the highest quality research in Europe through competitive funding and to support investigator-driven frontier research across all fields, based on scientific excellenceImmediate start is possible. The PhD position lasts for the duration of four years, and is carried out at theUniversity of Leuven. The candidate also takes up a limited amount (approx. 10% of the time) of teaching activities. The remuneration is generous and is in line with the standard KU Leuven rates. It consists of a net monthly salary of about 2400 Euro (in case of dependent children or spouse, the amount can be somewhat higher); social security is also included. Following Belgian law, the salary is automatically adjusted for inflation based on the smoothed health index.