Minimisation of the offshore wind and tidal turbine acoustic footprint on marine life
Lead by Prof. Esteban Ferrer (ETSIAE-School of Aeronautics)
Universidad Politécnica de Madrid
ERC Consolidator Grant (ERC-2022-COG)
Summary of the project
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, we propose to develop a novel aero/hydro-acoustic solver, blending advanced high order numerical techniques through machine learning, to simulate flow-acoustic signatures for wind and tidal turbines, in offshore environments. Second, experiments will generate aero/hydro-acoustic data 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, Off-coustics will propose new designs of silent farms that minimise the acoustic impact while ensuring energy production.
List of Publications
2026
– L Botero-Bolívar, A Portillo, A Sanz, G Gomez, A Garcia-Magariño, C Soriano, A Navarra, E Ferrer, “Acoustic Characterization of Two Atmospheric Towing Tanks: The Case of the UPM-CEHINAV and INTA-CEHIPAR”, Accepted Results in Engineering
– A Portillo, L Botero-Bolivar, S Saettone, L González, E Ferrer, “Experimental Assessment of Marine Propeller Noise Under Ventilation Conditions in a Towing Tank”, Applied Acoustics, Vol 245, 111208, 2026
– A Ballout, O Marino, G Rubio, E Ferrer, “Acoustic Propagation/Refraction Through Diffuse Interface Models” Journal of Computational Physics, Vol 545 (15), 2026 (available online)
2025
– Z Sun, E Jané, W Zhu, X Wang, W Z Shen, E Ferrer, “Sound Propagation Analysis of a 10 MW Wind Turbine: Influence of the Tower, Operational States, and Atmospheric Conditions”, Renewable Energy, Vol 255, 123842, 2025 (available online)
– M de Frutos, O Mariño, D Huergo, E Ferrer, “Enhancing Energy Generation while Mitigating Noise Emissions in Wind Turbines through Multi-Objective Optimization: A Deep Reinforcement Learning Approach”, Wind Energy, Vol 28 (8), 2025 (available online)
– D Huergo, M de Frutos, E Jané, G Rubio, E Ferrer, “Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers”, Journal of Computational Physics, Vol 536, 114080, 2025 (available online)
– Z Sun, Z Sun, Y Xia, W Shen, W Zhu, E Ferrer, “Aeroelastic study of Downwind and Upwind configurations under different power level of wind turbines”, Machines, 13(7), 599, 2025 (available online)
– L Botero-Bolívar, D Huergo, F dos Santos, C Venner, L de Santana, E Ferrer, “An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression”, Applied Acoustics, Vol 240, 110876, 2025 (available online)
– G Ntoukas, G Rubio, O Marino, F Bottone, J Hoessler, E Ferrer, “A comparative study of explicit and implicit Large Eddy Simulations using a high-order discontinuous Galerkin solver: application to a Formula 1 front wing”, Results in Engineering, Vol 25, 104425, 2025 (available online)
– S Colombo, G Rubio, J Kou, E Valero, R Codina, E Ferrer, “A high order immersed boundary method to approximate flow problems in domains with curved boundaries”, Journal of Computational Physics, Vol 528, 113807, 2025 (available online)
– A Mateo-Gabín, K Tlales, E Valero, E Ferrer, G Rubio, “Unsupervised machine learning shock capturing for High-Order CFD solvers”, Expert Systems With Applications,Vol 270, 126352, 2025 (available online)
2024
– H Kessasra, M Cordero-Gracia, M Gómez, E Valero, G Rubio, E Ferrer, “A comparison of h- and p-refinement to capture wind turbine wakes”, Physics of Fluids 36, 125125, 2024 (available online)
– OA Marino, A Juanicotena, J Errasti, D Mayoral, F Manrique de Lara, R Vinuesa, E Ferrer, “A comparison of Neural–Network architectures to accelerate high–order h/p solvers”, Physics of Fluids, Vol 36, 107132, 2024 (available online)
– D Huergo, L Alonso, S Joshi, A Juanicotena, G Rubio, E Ferrer, “A reinforcement learning strategy to automate and accelerate h/p-multigrid solvers”, Results in Engineering, Vol 24, 102949, 2024 (available online)
– KE Otmani, A Mateo-Gabín, G Rubio, E Ferrer, “Accelerating high order discontinuous Galerkin solvers through a clustering-based viscous/turbulent-inviscid domain decomposition”, Engineering with Computers, Vol 41, 2025 (available online)
– A Portillo, S Saettone, P Andersen, E Ferrer, “Hydro-Acoustic Optimization of Propellers: A Review of Design Methods”, Applied Ocean Research, Vol 151, 104158, 2024 (available online)
– A Portillo, S Saettone, E Ferrer, “Numerical Underwater Radiated Noise prediction in muli-phase flow conditions”, Ocean Engineering, Vol 308, 118324, 2024 (available online)
– L Botero-Bolívar, O Mariño, C Venner, L de Santana, E Ferrer, “Low-cost wind turbine aeroacoustic predictions using actuator lines”, Renewable Energy, Vol 227, 120476, 2024 (available online)
– D Soler, O Marino, D Huergo, M de Frutos, E Ferrer, “Reinforcement learning to maximise wind turbine energy generation”, Expert Systems with Applications, Vol 249, Part A, 123502, 2024 (available online)
Acknowledgments
This research has received funding from the European Union (ERC, Off-coustics, project number 101086075). Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
