Integration of CFD, Dimensional Analysis and Statistics in the Optimization of Hydrokinetic Turbines
Abstract
This communication explores the optimization of hydrokinetic turbines, key devices for sustainable
energy generation in isolated areas, as evidenced by projects in Scotland and France. To overcome high
experimental costs, the research focuses on Computational Fluid Dynamics (CFD) and numerical analysis
methodologies. The main objective is to analyze the integration of CFD, the Buckingham π Theorem, and
Statistics in the optimization of these turbines. The article reviews existing literature, establishing a theoretical
framework for future research. It highlights CFD's role in flow analysis and critical parameter determination,
and how the Buckingham π Theorem simplifies fluid equations and facilitates scaling. Statistics, in turn, are
fundamental for design, optimization, performance evaluation, and the development of predictive models.
Finally, the research emphasizes the growing integration of Artificial Intelligence (AI), including machine
learning and deep learning, as a novel approach to enhance the design, optimization, and control of
hydrokinetic turbines, opening new avenues for the advancement of this energy technology.
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