Summary
Tidal energy presents a promising solution for addressing the growing demand for sustainable energy. Extensive research efforts have been dedicated to refining individual turbine efficiency and optimizing tidal farms to maximize energy output. However, as tidal farms scale up, there's a consequential rise in noise emissions that can prove detrimental to underwater ecosystems. This issue necessitates a dedicated focus on the development of noise-reducing farms. While tidal turbine design has, to date, been focused on energy production, fatigue load and lifespan of the blades and paid little attention to the acoustic footprint and subsequent effects on the environment, it is urgent to design silent energy farms to reduce the noise impact on local fauna.
Farm-noise intends to: 1) characterise individual tidal turbines and construct simple models (surrogates) for their representation; 2) provide mechanisms for the acoustic control/minimisation. We will characterize tidal turbines and farms using computational fluid dynamics and large eddy simulations. Based on the simulations and the extracted physical insight, accurate surrogate models for turbines and associated acoustics will be developed to enable optimization of farms that minimize noise while ensuring energy production. Machine learning based reinforcement learning methodologies will be used to optimize and control the trade-off between energy production and noise emission. Compromises between energy and sound generation will finally be reached automatically for specific sites taking into account ambient conditions and local fauna.
The expected research results will not only provide theoretical and methodological support for the design and silent operation of large tidal farms but also promote the ecological sustainability of the tidal industry. The outcomes and impacts will be maximized and disseminated to various communities by peer-reviewed articles, conferences, workshops and outreach activities, etc.
Farm-noise intends to: 1) characterise individual tidal turbines and construct simple models (surrogates) for their representation; 2) provide mechanisms for the acoustic control/minimisation. We will characterize tidal turbines and farms using computational fluid dynamics and large eddy simulations. Based on the simulations and the extracted physical insight, accurate surrogate models for turbines and associated acoustics will be developed to enable optimization of farms that minimize noise while ensuring energy production. Machine learning based reinforcement learning methodologies will be used to optimize and control the trade-off between energy production and noise emission. Compromises between energy and sound generation will finally be reached automatically for specific sites taking into account ambient conditions and local fauna.
The expected research results will not only provide theoretical and methodological support for the design and silent operation of large tidal farms but also promote the ecological sustainability of the tidal industry. The outcomes and impacts will be maximized and disseminated to various communities by peer-reviewed articles, conferences, workshops and outreach activities, etc.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101149790 |
| Start date: | 17-02-2025 |
| End date: | 16-02-2027 |
| Total budget - Public funding: | - 181 152,00 Euro |
Cordis data
Original description
Tidal energy presents a promising solution for addressing the growing demand for sustainable energy. Extensive research efforts have been dedicated to refining individual turbine efficiency and optimizing tidal farms to maximize energy output. However, as tidal farms scale up, there's a consequential rise in noise emissions that can prove detrimental to underwater ecosystems. This issue necessitates a dedicated focus on the development of noise-reducing farms. While tidal turbine design has, to date, been focused on energy production, fatigue load and lifespan of the blades and paid little attention to the acoustic footprint and subsequent effects on the environment, it is urgent to design silent energy farms to reduce the noise impact on local fauna.Farm-noise intends to: 1) characterise individual tidal turbines and construct simple models (surrogates) for their representation; 2) provide mechanisms for the acoustic control/minimisation. We will characterize tidal turbines and farms using computational fluid dynamics and large eddy simulations. Based on the simulations and the extracted physical insight, accurate surrogate models for turbines and associated acoustics will be developed to enable optimization of farms that minimize noise while ensuring energy production. Machine learning based reinforcement learning methodologies will be used to optimize and control the trade-off between energy production and noise emission. Compromises between energy and sound generation will finally be reached automatically for specific sites taking into account ambient conditions and local fauna.
The expected research results will not only provide theoretical and methodological support for the design and silent operation of large tidal farms but also promote the ecological sustainability of the tidal industry. The outcomes and impacts will be maximized and disseminated to various communities by peer-reviewed articles, conferences, workshops and outreach activities, etc.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
04-10-2024
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