Project description
Minimising tidal turbine noise to protect marine wildlife
Tidal energy is a promising renewable, but the harmful noise created by expanding tidal farms can have a detrimental effect on underwater ecosystems. There is a need to develop quieter turbines. With the support of the Marie Skłodowska-Curie Actions programme, the Farm-noise project aims to minimise noise during operation. It will use computational fluid dynamics and large-eddy simulations to create models of turbine acoustics, optimising tidal farms to reduce noise while maintaining energy production. By using machine learning and reinforcement learning, it will balance energy output and noise emissions, customising solutions for specific sites based on local conditions and wildlife. It will support the design and quiet operation of tidal farms.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning reinforcement learning
- natural sciences physical sciences acoustics
- natural sciences physical sciences classical mechanics fluid mechanics fluid dynamics computational fluid dynamics
- engineering and technology environmental engineering energy and fuels renewable energy hydroelectricity marine energy tidal energy
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2023-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
28040 Madrid
Spain
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