Minimisation of the offshore wind and tidal turbine acoustic footprint on marine life

ERC (European Research Council)HORIZON-ERCID: 101086075
EC Contribution
€19,925
Consortium Size
1 orgs
Start Year
2023
Summary

For renewable energies to be sustainable in the future, their impact and harmful effects on the environment should be minimum. Recent evidences suggest that offshore wind and tidal turbines can have an acoustic damaging impact on marine life, due to the sustained generation of noise, which propagates very efficiently underwater.Off-coustics combines numerical simulations and experiments to provide insights into the physics governing the aero/hydro-acoustic generation and propagation for offshore wind and tidal farms. Control of these physics will enable the design of silent offshore farms enabling renewable energy with zero acoustic impact. First, I propose to develop a novel aero/hydro-acoustic solver, blending advanced high order numerical techniques through machine learning and trained with experiments, to simulate flow-acoustic signatures for wind and tidal turbines, in realistic offshore environments (including bathymetry, air-water surface, etc.). Second, an experimental campaign will generate aero/hydro-acoustic data for scaled turbines and farms to help elucidate the physics governing offshore acoustics and to guide/validate the flow-acoustic simulator. Third, simulations and experiments will be combined to characterise turbines in complex offshore environments and to develop physic-informed surrogate models. Fourth, using the developed surrogate models and optimisation, Off-coustics will propose new designs of silent farms that minimise the acoustic impact while ensuring energy production.Major advances in multidisciplinary aspects are expected, including fluid mechanics, numerical simulations, optimisation, experimental acoustics, aero/hydro-acoustics and offshore wind and tidal turbine physics.

Consortium (1)

Project Results (18)

Source: CORDIS, the EU research results database.

Publications (17)
Acoustic characterization of two atmospheric towing tanks: The case of the UPM-CEHINAV and INTA-CEHIPAR
Results in Engineering· 2026
Laura Botero-Bolívar; Adrian Portillo-Juan; Ángel Sanz Ortega; Guillermo Gomez Prada; Adelaida Garcia-Magariño Garcia; Esteban Ferrer
Mitigating underwater noise from offshore wind turbines via individual pitch control
Ocean Engineering· 2026
Martín de Frutos, Laura Botero-Bolívar, Esteban Ferrer
A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers
Results in Engineering· 2025DOI
David Huergo, Laura Alonso, Saumitra Joshi, Adrian Juanicotena, Gonzalo Rubio, Esteban Ferrer
Acoustic propagation/refraction through diffuse interface models
Journal of Computational Physics· 2025DOI
Abbas Ballout, Oscar A. Marino, Gerasimos Ntoukas, Gonzalo Rubio, Esteban Ferrer
An empirical wall-pressure spectrum model for aeroacoustic predictions based on symbolic regression
Applied Acoustics· 2025DOI
Laura Botero-Bolívar, David Huergo, Fernanda L. dos Santos, Cornelis H. Venner, Leandro D. de Santana, Esteban Ferrer
Enhancing Energy Generation While Mitigating Noise Emissions in Wind Turbines Through Multi‐Objective Optimization: A Deep Reinforcement Learning Approach
Wind Energy· 2025
Martín Frutos, Oscar A. Marino, David Huergo, Esteban Ferrer
Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers
Journal of Computational Physics· 2025
Huergo Perea, David; Frutos Muñoz, Martín de; Jané Soler, Eduardo; Mariño Sánchez, Oscar Ándres; Rubio Calzado, Gonzalo; Ferrer Vaccarezza, Esteban
Sound propagation analysis of a 10 MW wind turbine: Influence of the tower, operational states, and atmospheric conditions
Renewable Energy· 2025DOI
Zhenye Sun, Weijun Zhu, Eduardo Jané, Xukun Wang, Wen Zhong Shen, Esteban Ferrer
A comparison of h- and p-refinement to capture wind turbine wakes
Physics of Fluids· 2024DOI
Hatem Kessasra, Marta Cordero-Gracia, Mariola Gómez, Eusebio Valero, Gonzalo Rubio, Esteban Ferrer
A comparison of neural-network architectures to accelerate high-order h/p solvers
Physics of Fluids· 2024DOI
Oscar A. Marino; Adrian Juanicotena; Jon Errasti; David Mayoral; Fernando Manrique de Lara; Ricardo Vinuesa; Esteban Ferrer
A high-order immersed boundary method to approximate flow problems in domains with curved boundaries
Journal of Computational Physics· 2024DOI
Colombo, S.; Rubio, G.; Kou, J.; Valero, E.; Codina, R.; Ferrer, E.
Accelerating high order discontinuous Galerkin solvers through a clustering-based viscous/turbulent-inviscid domain decomposition
Engineering with Computers· 2024DOI
Otmani, Kheir-Eddine; Mateo Gabín, Andrés; Rubio Calzado, Gonzalo; Ferrer Vaccarezza, Esteban
Accelerating high order discontinuous Galerkin solvers using neural networks: Wall bounded flows
Journal of Physics: Conference Series, Volume 2753, 5th Madrid Turbulence Workshop 29/05/2023 - 30/06/2023 Madrid, Spain· 2024DOI
Oscar A. Mariño, David Mayoral, Adrián Juanicotena, Fernando Manrique De Lara and Esteban Ferrer
Hydro-acoustic optimization of propellers: A review of design methods
Applied Ocean Research· 2024DOI
Adrian Portillo-Juan, Simone Saettone, Poul Andersen, Esteban Ferrer
Low-cost wind turbine aeroacoustic predictions using actuator lines
Renewable Energy· 2024DOI
Laura Botero-Bolívar, Oscar A. Marino, Cornelis H. Venner, Leandro D. de Santana, Esteban Ferrer
Numerical underwater radiated noise prediction in multi-phase flow conditions
Ocean Engineering· 2024DOI
Adrian Portillo-Juan, Simone Saettone, Esteban Ferrer
Reinforcement learning to maximize wind turbine energy generation
Expert Systems with Applications· 2024DOI
Daniel Soler, Oscar Mariño, David Huergo, Martín de Frutos, Esteban Ferrer
Deliverables (1)
Data Management Plan