Wind tracing to resolve the wind cooling effect in geometrically complex solar energy systems
As the price-per-watt of solar cells keeps sinking, the applications of solar photovoltaic (PV) systems become increasingly diverse. PV systems no longer need to consist of radiatively and operationally optimized arrays to amortize the initial investment within the system’s operating life. Today, any unused surface in the natural or built environment can be considered as a potential source of economically viable PV power generation. This trend is further reinforced by various sustainability targets set for companies and public entities to reduce carbon emissions.
The ever-wider range of PV applications makes the performance simulation of PV systems more challenging. The models need to be flexible to correctly capture the effects of ambient factors in various system geometries. Ray tracing provides this flexibility for the estimation of in-plane irradiance, which is by far the most important ambient factor of PV performance. The method is not limited to any specific geometry but can be readily applied to any point cloud comprising a PV system and its surroundings. Due to their link to solar cell temperature, wind speed and direction have also been shown to correlate with PV performance. Analogously with making irradiance estimation more flexible with ray tracing, the objective of this Ph.D. project is to improve cell temperature estimation in an arbitrary system geometry by more accurately simulating the wind flow within the system. The actual wind velocity influencing the wind-forced convective cooling of each cell in a PV array is a complex function of the speed and direction of the external wind flow, the system geometry obstructing the flow, and the cell’s location in the array.
In practice, the candidate will identify, test, and implement computationally efficient methods to perform computational fluid dynamics (CFD) as part of imec’s existing simulation framework. The work involves experimental analysis of wind flow and its cooling effect under both laboratory and field conditions. The model’s level of detail should be dynamically changed depending on the available computation resources and the required simulation interval.
Required background: Physicist, Mechanical Engineer, or Computer Scientist with a background in computational fluid dynamics or high-performance computing
Type of work: 15% literature review, 20% model development, 40% experimental work, 15% dissemination of results, 10% software implementation
Supervisor: Jef Poortmans
Co-supervisor: Michael Daenen
Daily advisor: Arttu Tuomiranta