WavEC Offshore Renewables

As floating offshore wind farms move further offshore into more challenging environments, robust and scalable monitoring strategies become critical for maintaining operational safety and performance. This study investigates the use of Sentinel-2 satellite imagery to remotely track the positional offset of semi-submersible floating wind platforms and evaluate their dynamic response to varying metocean conditions. A convolutional neural network (U-Net) was developed to segment turbine platforms in optical imagery, enabling automated extraction of positional data. These positions were then correlated with wind and wave parameters using both linear and multivariate regression models. The results indicate a statistically significant but moderate global correlation between wind speed and platform horizontal displacement, which improves substantially when accounting for wind directionality, reaching <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> values of 0.73 for westerly winds. Horizontal displacement was found to be anisotropic and strongly dependent on the direction of environmental forcing, with both wind and wave contributions playing key roles. Clustering analysis further revealed four distinct offset regimes, each associated with characteristic wind patterns, confirming the potential for satellite-driven classification of operational states. Additionally, the method demonstrated value for anomaly detection, with deviations in turbine position highlighting possible mooring line failures. Independent in-situ position measurements (e.g., GNSS/INS, SCADA position channels, or mooring telemetry) were not available for the study period; therefore, we report uncertainty bounds and surrogate consistency evidence rather than a direct validation of absolute offsets. We demonstrate an end-to-end workflow for 1) extracting apparent platform offsets from Sentinel-2 imagery and 2) reporting descriptive associations between these offsets and farm-scale metocean forcing; any asset-health or degradation inference is presented only as an exploratory condition-indicator example and is not claimed as a validated diagnostic. These findings suggest that, despite limitations in spatial resolution, Sentinel-2 imagery can complement traditional in-situ monitoring systems by offering a cost-effective, wide-area surveillance layer. The approach supports not only real-time tracking but also the development of predictive maintenance tools, contributing to safer and more resilient offshore wind operations.

<strong class="journal-contentHeaderColor">Abstract.</strong> This work proposes a structural and economic optimisation framework applicable to floating semi-submersible platforms, demonstrated here for a 15-MW offshore wind design. A genetic algorithm was developed that can seek a multi-objective solution to minimise mass whilst respecting the constraints of loads acting upon the system. Statistical and machine learning methods are then employed to forecast near and far term costs of the platform under a range of scenarios. Finally, Levelized Cost of Energy is calculated to gauge the technical and economic viability. Results show that a mass reduction of 9 % is possible. With optimal costs predicted under the SARIMAX scenario of 4404.56 &euro;/t, 24.1 % under the average. The optimised platform results in an LCoE reduction of 2.08 %.

As Marine Renewable Energy (MRE), particularly wave energy, develops it is critical to understand and assess potential environmental interactions. Wave Energy Converters (WECs) and associated infrastructure are a source of underwater noise thus with potential to introduce acoustic disturbances that could affect marine species reliant on sound for communication, navigation, and foraging. This study presents the results of underwater noise monitoring conducted for the HiWave-5 wave energy project off the coast of Aguçadoura, Portugal. Ongoing monitoring began in 2022, during pre-operation, continuing through to 2023, covering its early operation phases. Underwater noise was evaluated across three sampling locations, through hydrophones, capturing seasonal variations during autumn/winter and spring/summer, located at increasing distances. Broadband Sound Pressure Level (SPL) and 1/3-octave bands were analysed, with a sampling frequency range of 10 Hz-288 kHz. The influence of season, wave height, wave period, temperature and WEC operation regime on acoustic emissions was analysed. During the commissioning phase, the WEC was tested in various operational states, including "Tuned" and "Derated" modes. Higher noise levels were observed under increased wave heights (&gt; 2 m), and significantly affected by wave period and temperature. Notably, the "Tuned" mode demonstrated reduced noise emissions at lower frequencies, highlighting potential pathways for reduced impacts with technological optimisation. Future monitoring phases will further characterise long-term acoustic impacts and refine mitigation strategies. This work was part of the SafeWAVE Project co-funded by the European Climate, Infrastructure and Environment Executive Agency (CINEA), Call for Proposals EMFF-2019-1.2.1.1 - Environmental monitoring of ocean energy devices.

The costs associated with maintenance activities in Offshore Wind Farms are significant, and particularly for preventive operations. These activities are calendarized according to the farm operator's strategy and may thus be optimized to maximize profit. However, calendarization depends directly on weather conditions, and thus on weather forecasts. The present abstract presents a digital twin application aimed at optimizing the scheduling of maintenance activities for offshore wind farms. The application incorporates the real-time weather forecast with the farm and vessel fleet characteristics and electricity price to schedule the optimal sequence of maintenance interventions. We present the development of the application, including the advances in the implementation of the optimization algorithm and additional functionalities, namely the employment a machine-learning technique for enhancing the weather forecast's accuracy and the extension to different geographies.

The Iliad Digital Twins of the Ocean is a European Green Deal Project which aims at the development of an architecture and set of components, tools and services for the creation of digital twins of the ocean. The approach aims to support the emerging European Digital Twins of the Ocean (EDITO) initative with associated projects like EDITO Infra and EDITO Model lab and the overall Destination Earth (DestinE) initiative and also taking advantage of the evolving European Common Data Spaces including the Green Deal Data Space, the Copernicus Data Space and the EOSC cross domain Data Space. The paper presents the final version of the Iliad digital twin interoperability architecture based on four steps of a digital twin pipeline from Data Acquisition/Collection to Digital Twin Data Representation to Digital Twin Hybrid and Cognitive/AI Analytics Models and further to Digital Twin Visualisation and Control, which are presented together with associated Digital twin components and services.

Abstract The identification and quantification of marine mammals is crucial for understanding their abundance, ecology and supporting their conservation efforts. Traditional methods for detecting cetaceans, however, are often labor-intensive and limited in their accuracy. To overcome these challenges, this work explores the use of convolutional neural networks (CNNs) as a tool for automating the detection of cetaceans through aerial images from unmanned aerial vehicles (UAVs). Additionally, the study proposes the use of Long-Short-Term-Memory (LSTM)-based models for video detection using a CNN-LSTM architecture. Models were trained on a selected dataset of dolphin examples acquired from 138 online videos with the aim of testing methods that hold potential for practical field monitoring. The approach was effectively validated on field data, suggesting that the method shows potential for further applications for operational settings. The results show that image-based detection methods are effective in the detection of dolphins from aerial UAV images, with the best-performing model, based on a ConvNext architecture, achieving high accuracy and f1-score values of 83.9% and 82.0%, respectively, within field observations conducted. However, video-based methods showed more difficulties in the detection task, as LSTM-based models struggled with generalization beyond their training environments, achieving a top accuracy of 68%. By reducing the labor required for cetacean detection, thus improving monitoring efficiency, this research provides a scalable approach that can support ongoing conservation efforts by enabling more robust data collection on cetacean populations.

RESUMO: O processo crescente de urbanização intensifica a impermeabilização das superfícies das cidades resultando em impactos no balanço hídrico e elevação das ocorrências de cheias urbanas. Este fenômeno está associado ao aumento do volume de escoamento superficial e redução do tempo de concentração, que resulta em picos mais elevados de vazão na bacia de contribuição. Os sistemas de drenagem na fonte visam reestabelecer ou preservar o balanço hídrico nas áreas edificadas, promovendo a retenção ou detenção da pluviometria incidente. Assim, utilizando um loteamento hipotético foram avaliados 4 cenários de implantação do loteamento considerando diferentes implantações de poços de infiltração de águas pluviais nas áreas edificadas e cenários de referência (terreno natural e urbanizado sem drenagem na fonte). Os poços de infiltração foram implantados individualmente e coletivamente e comparou-se a redução de pico de vazão e de aumento de tempo de concentração notando-se que a implantação coletiva teve maior eficiência de infiltração do escoamento, diminuindo o volume escoado, e que os poços de infiltração individuais foram os que mais diminuíram a vazão de pico.

Floating offshore wind and wave energy are both promising marine renewable energy technologies that are still at an early stage of development. This paper reviews synergies between the two technologies that can be mutually beneficial and facilitate the path to commercial exploitation. The analysis focuses on three different types of combined wave and wind systems: Co-located arrays, hybrid systems and island systems. The decarbonisation potential of co-located systems is discussed. Existing carbon emissions from marine technologies are compared and how synergies can achieve these reductions are explored. A case study is presented to calculate the impact of multi-purpose platforms on the Portuguese energy system. Portugal was chosen because it has an excellent climate for marine resources, deep waters, positive political support for renewable marine energy and previous success in deploying first technology demonstrators for floating wind and wave energy systems.

Abstract The presence of complex fluids in nature and industrial applications combined with the rapid growth of computer power over the past decades has led to an increasing number of numerical studies of non-Newtonian flows. In most cases, non-Newtonian models can be implemented in existing Newtonian solvers by relatively simple modifications of the viscosity. However, due to the scarcity of analytical solutions for non-Newtonian fluid flows and the widespread use of regularization methods, performing rigorous code verification is a challenging task. The method of manufactured solutions (MMS) is a powerful tool to generate analytical solutions for code verification. In this article, we present and discuss the results of three verification exercises based on MMS: (i) steady single-phase flow; (ii) unsteady two-phase flow with a smooth interface; (iii) unsteady two-phase flow with a free surface. The first and second exercises showed that rigorous verification of non-Newtonian fluid solvers is possible both on single- and two-phase flows. The third exercise revealed that “spurious velocities” typical of free-surface calculations with the Volume-of-Fluid model lead to “spurious viscosities” in the non-Newtonian fluid. The procedure is illustrated herein on a second-order finite volume flow solver, using the regularized Herschel-Bulkley fluid model as an example. The same methodology is however applicable to any flow solver and to all the rheological models falling under the class of generalized Newtonian fluid models.