arXiv subjects

Martín de Frutos

Publications and source records attributed to Martín de Frutos.

6 recordsLinked to original sources

arXiv2026

Vibroacoustic Underwater Noise from Fixed and Floating Offshore Wind Turbines

Anthropogenic underwater noise from offshore wind turbines is a growing environmental concern, particularly with the large-scale deployment of bottom-fixed and floating devices. This study presents a physics-based vibroacoustic framework to predict operational underwater noise emissions from offshore wind turbines and compares monopile-supported and floating configurations for a 10 MW turbine. The methodology combines time-domain aero-hydro-servo-elastic simulations with a frequency-domain acoustic formulation based on equivalent dipole sources and Green's function solutions, accounting for underwater confinement between the free surface and seabed through the method of images. Results show that floating configurations exhibit enhanced low-frequency acoustic emissions, producing up to 15% higher OASPL than the monopile structures under equivalent water depths for frequencies below 10 Hz due to additional rigid-body motions, while monopile structures radiate more efficiently at higher frequencies associated with drivetrain excitations. Significant differences in the spatial distribution and directivity of the radiated sound field are also observed, with floating platforms displaying more complex three-dimensional radiation patterns and stronger direction-dependent variations, reaching approximately 20-25 dB in the 100-1000 Hz band, compared with the smoother and nearly axisymmetric response of monopile configurations. Water depth strongly influences propagation regimes and overall sound levels, with shallow-water floating configurations showing variations of up to 7% in OASPL relative to deep-water cases. The proposed framework enables quantification of vibro-acoustic noise and provides a predictive tool for assessing underwater acoustic impacts during the design phase, supporting environmentally informed offshore wind turbine design and future regulatory and monitoring strategies.

physics.flu-dyn↗

arXiv2025

Mitigating Underwater Noise from Offshore Wind Turbines via Individual Pitch Control

This paper proposes a pitch control strategy to mitigate the underwater acoustic footprint of offshore wind turbines, a measure that will soon become necessary to minimize impacts on marine life, which rely on sound for communication, navigation, and survival. First, we quantify the underwater acoustic signature of blade-generated aerodynamic noise from three reference turbines, the NREL 5 MW, DTU 10 MW, and IEA 22 MW, using coupling blade element momentum and coupled air-water acoustic propagation modeling. Second, we propose and implement an open-loop individual pitch control (IPC) strategy that modulates the pitch of the blade at the blade passing frequency to attenuate the overall sound pressure level (OSPL) and the amplitude modulation (AM) of the transmitted noise. Third, we benchmark IPC performance against conventional pitch schemes. The results indicate that up to 5 dB reductions in OSPL and a decrease in AM depth 20% can be achieved with a pitch variation of $Δθ\approx 5^\circ$, with small losses (5-10%) in energy capture. These findings highlight a previously underappreciated noise pathway and demonstrate that targeted blade-pitch modulation can mitigate its impact.

eess.SY↗

arXiv2025

On the prediction of underwater aerodynamic noise of offshore wind turbines

The growing demand for offshore wind energy has led to a significant increase in wind turbine size and to the development of large-scale wind farms, often comprising 100 to 150 turbines. However, the environmental impact of underwater noise emissions remains largely unaddressed. This paper quantifies, for the first time, the underwater aerodynamic noise footprint of three large offshore turbines (5 MW, 10 MW, and 22 MW) and wind farms composed of these turbines. We propose a novel methodology that integrates validated wind turbine noise prediction techniques with plane wave propagation theory in different media, enabling turbine designers to predict and mitigate underwater noise emissions. Our results confirm that aerodynamic noise from offshore wind farms presents a potential environmental challenge, with negative effects on marine life. Addressing this issue is crucial to ensuring the sustainable expansion of offshore wind energy.

physics.ao-ph↗

arXiv2024

Reinforcement learning for anisotropic p-adaptation and error estimation in high-order solvers

We present a novel approach to automate and optimize anisotropic p-adaptation in high-order h/p solvers using Reinforcement Learning (RL). The dynamic RL adaptation uses the evolving solution to adjust the high-order polynomials. We develop an offline training approach, decoupled from the main solver, which shows minimal overcost when performing simulations. In addition, we derive an inexpensive RL-based error estimation approach that enables the quantification of local discretization errors. The proposed methodology is agnostic to both the computational mesh and the partial differential equation to be solved. The application of RL to mesh adaptation offers several benefits. It enables automated and adaptive mesh refinement, reducing the need for manual intervention. It optimizes computational resources by dynamically allocating high-order polynomials where necessary and minimizing refinement in stable regions. This leads to computational cost savings while maintaining the accuracy of the solution. Furthermore, RL allows for the exploration of unconventional mesh adaptations, potentially enhancing the accuracy and robustness of simulations. This work extends our original research, offering a more robust, reproducible, and generalizable approach applicable to complex three-dimensional problems. We provide validation for laminar and turbulent cases: circular cylinders, Taylor Green Vortex and a 10MW wind turbine to illustrate the flexibility of the proposed approach.

physics.flu-dyn↗

arXiv2024

Deep Reinforcement Learning for Multi-Objective Optimization: Enhancing Wind Turbine Energy Generation while Mitigating Noise Emissions

We develop a torque-pitch control framework using deep reinforcement learning for wind turbines to optimize the generation of wind turbine energy while minimizing operational noise. We employ a double deep Q-learning, coupled to a blade element momentum solver, to enable precise control over wind turbine parameters. In addition to the blade element momentum, we use the wind turbine acoustic model of Brooks Pope and Marcolini. Through training with simple winds, the agent learns optimal control policies that allow efficient control for complex turbulent winds. Our experiments demonstrate that the reinforcement learning is able to find optima at the Pareto front, when maximizing energy while minimizing noise. In addition, the adaptability of the reinforcement learning agent to changing turbulent wind conditions, underscores its efficacy for real-world applications. We validate the methodology using a SWT2.3-93 wind turbine with a rated power of 2.3 MW. We compare the reinforcement learning control to classic controls to show that they are comparable when not taking into account noise emissions. When including a maximum limit of 45 dB to the noise produced (100 meters downwind of the turbine), the extracted yearly energy decreases by 22%. The methodology is flexible and allows for easy tuning of the objectives and constraints through the reward definitions, resulting in a flexible multi-objective optimization framework for wind turbine control. Overall, our findings highlight the potential of RL-based control strategies to improve wind turbine efficiency while mitigating noise pollution, thus advancing sustainable energy generation technologies

eess.SY↗

arXiv2024

Reinforcement learning to maximise wind turbine energy generation

We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent is coupled with a blade element momentum model and is trained to allow control for changing winds. The agent is trained to decide the best control (speed, yaw, pitch) for simple steady winds and is subsequently challenged with real dynamic turbulent winds, showing good performance. The double deep Q- learning is compared with a classic value iteration reinforcement learning control and both strategies outperform a classic PID control in all environments. Furthermore, the reinforcement learning approach is well suited to changing environments including turbulent/gusty winds, showing great adaptability. Finally, we compare all control strategies with real winds and compute the annual energy production. In this case, the double deep Q-learning algorithm also outperforms classic methodologies.

cs.LG↗