Researchers from the Technical University of Denmark, including Frédéric Blondel, Erwan Jézéquel, Helen Schottenhamml, and Majid Bastankhah, have developed a new physics-based model to improve the accuracy of wind farm flow predictions. Their work, published in the Journal of Fluid Mechanics, focuses on better understanding and estimating the turbulence created by wind turbines, which is crucial for optimizing wind farm performance and energy yield.
Wind turbines generate wakes—regions of disturbed airflow downstream—that can significantly impact the performance of nearby turbines. These wakes are both influenced by and contribute to turbulence, making accurate modeling of turbulence intensity essential for predicting wind farm efficiency. Existing models often rely on empirical data or oversimplified assumptions, which can lead to inaccuracies.
The researchers’ new model is based on the analysis of turbulent kinetic energy and the streamwise Reynolds stress budget, incorporating classical Reynolds-Averaged Navier-Stokes (RANS) modeling assumptions and far-wake approximations. This approach provides a more physically consistent and predictive tool for estimating wake-added turbulence intensity. The model has been validated against large-eddy simulations (LES) and wind tunnel measurements, demonstrating strong agreement and improved accuracy over previous methods.
For the energy industry, this research offers practical applications in wind farm design and optimization. By providing more accurate predictions of turbulence and wake interactions, the model can help engineers better arrange wind turbines to maximize energy output and minimize mechanical stress on the turbines. This can lead to more efficient wind farm layouts, reduced maintenance costs, and ultimately, increased renewable energy production.
This article is based on research available at arXiv.