Publications by authors named "Fares M'zoughi"

The operational efficiency and lifespan of Floating Offshore Wind Turbines (FOWTs) are adversely impacted by the inherent platform motions and undesired vibrations induced by wind and wave loads. To effectively address these effects, the control of specific structural motions is of utmost importance, with platform pitch and yaw identified as the primary Degrees Of Freedom (DOF) that require attention. This study proposes a novel utilization of Oscillating Water Columns (OWCs) as a reliable and viable solution to mitigate platform pitch and yaw motions, thereby significantly enhancing the efficiency and reducing fatigue in wind turbines.

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Offshore wind energy is getting increasing attention as a clean alternative to the currently scarce fossil fuels mainly used in Europe's electricity supply. The further development and implementation of this kind of technology will help fighting global warming, allowing a more sustainable and decarbonized power generation. In this sense, the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) devices arise as a promising solution for hybrid renewable energy production.

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Oscillating water column (OWC) plants face power generation limitations due to the stalling phenomenon. This behavior can be avoided by an airflow control strategy that can anticipate the incoming peak waves and reduce its airflow velocity within the turbine duct. In this sense, this work aims to use the power of artificial neural networks (ANN) to recognize the different incoming waves in order to distinguish the strong waves that provoke the stalling behavior and generate a suitable airflow speed reference for the airflow control scheme.

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