The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service inspections of mooring systems are the golden standard for monitoring their health status. This approach is often expensive, inefficient, and unsafe, and for this reason, researchers are focusing on developing tools for digital solutions for real-time monitoring. Floating offshore renewable energy devices are usually equipped with a wide range of sensors, some low-cost, low/zero maintenance, and easily deployable (e.g., accelerometers on the tower), contrary to others (e.g., direct tension mooring line measurements), producing real-time data streams. In this paper, we propose exploiting the data coming from the first type of sensors for mooring systems health status nowcasting. In particular, we will first rely on state-of-the-art supervised shallow and deep learning models for predicting the health status of the different sections of the mooring lines. Then, since these supervised models require types and amount of data that are seldom available, we will propose new shallow and deep weekly supervised models that require a very small amount of data regarding worn mooring lines. Results will show that these last models can potentially have practical applicability and impact for real-time monitoring of mooring systems in the near future. In order to support our statements, we will make use of data generated with a state-of-the-art digital twin of the mooring system, OrcaFlex, for a floating offshore wind turbine reproducing the physical mechanism of the mooring degradation under different loads and environmental conditions. Results will show errors around 1% in the simplest scenario and errors around 4% in the most challenging one, confirming the potentiality of the proposed approaches.

The global installed capacity of floating offshore wind turbines is projected to increase by at least 100 times over the next decades. Station-keeping of floating offshore renewable energy devices is achieved through the use of mooring systems. Mooring systems are exposed to a variety of environmental and operational conditions that cause corrosion, abrasion, and fatigue. Regular physical in-service inspections of mooring systems are the golden standard for monitoring their health status. This approach is often expensive, inefficient, and unsafe, and for this reason, researchers are focusing on developing tools for digital solutions for real-time monitoring. Floating offshore renewable energy devices are usually equipped with a wide range of sensors, some low-cost, low/zero maintenance, and easily deployable (e.g., accelerometers on the tower), contrary to others (e.g., direct tension mooring line measurements), producing real-time data streams. In this paper, we propose exploiting the data coming from the first type of sensors for mooring systems health status nowcasting. In particular, we will first rely on state-of-the-art supervised shallow and deep learning models for predicting the health status of the different sections of the mooring lines. Then, since these supervised models require types and amount of data that are seldom available, we will propose new shallow and deep weekly supervised models that require a very small amount of data regarding worn mooring lines. Results will show that these last models can potentially have practical applicability and impact for real-time monitoring of mooring systems in the near future. In order to support our statements, we will make use of data generated with a state-of-the-art digital twin of the mooring system, OrcaFlex, for a floating offshore wind turbine reproducing the physical mechanism of the mooring degradation under different loads and environmental conditions. Results will show errors around 1% in the simplest scenario and errors around 4% in the most challenging one, confirming the potentiality of the proposed approaches.

In the industry field, the increasingly stringent requirements of lightweight structures are exposing the ultimately nonlinear nature of mechanical systems. This is extremely true for systems with moving parts and loose fixtures which show piecewise stiffness behaviours. Nevertheless, the numerical solution of systems with ideal piecewise mathematical characteristics is associated with time-consuming procedures and a high computational burden. Smoothing functions can conveniently simplify the mathematical form of such systems, but little research has been carried out to evaluate their effect on the mechanical response of multi-degree-of-freedom systems. To investigate this problem, a slightly damped mechanical two-degree-of-freedom system with soft piecewise constraints is studied via numerical continuation and numerical integration procedures. Sigmoid functions are adopted to approximate the constraints, and the effect of such approximation is explored by comparing the results of the approximate system with the ones of the ideal piecewise counter-part. The numerical results show that the sigmoid functions can correctly catch the very complex dynamics of the proposed system when both the above-mentioned techniques are adopted. Moreover, a reduction in the computational burden, as well as an increase in numerical robustness, is observed in the approximate case.

This article gives an overview of challenges in primary distribution, protections, and power scalability for shipboard dc systems. Given that dc technology is in development, several aspects of shipboard systems have not yet been sufficiently devised to ensure the protection and efficiency demanded. Several issues in dc systems arise from the lack of complete relevant standardization from different regulation bodies. Unipolar and bipolar bus architectures have application-specific advantages that are discussed and compared. The placement of power electronics in dc systems creates opportunities for switchboard design, and this article compares the centralized and distributed approaches. Likewise, protection architectures for shipboard dc systems have challenges. Breaker-based protection utilizes slow fuses, mechanical circuit breakers, and solid-state circuit breakers. In addition, power-electronics-based protection embeds the protective circuit in the power converters, but its development lags. This article compares the state-of-the-art technologies, reviewing their main features. Finally, the power requirement of various applications and the low production rate of vessels force the designers to utilize commercial off-the-shelf converters to scale up power. The misuse of such converters, the modular topologies, and power electronics building blocks are exposed highlighting challenges and opportunities toward the mass adoption of dc systems onboard maritime vessels.

Floating Offshore Wind Turbines (FOWT) can harness the abundant offshore wind resource at reduced installation requirements. However, a further decrease in the development risks through higher confidence in the design and analysis methods is needed. The dynamic behaviour of FOWT systems is complex due to the strong interactions between the large translational and rotational motions and the diverse loads, which poses a challenge. While the methods to study the FOWT's general responses are well established, there are no methods to describe the highly complex time-dependent rotational motion patterns of FOWT. For a rigid body in general plane motion, an Instantaneous Centre of Rotation (ICR) can be identified as a point at which, at a given moment, the velocity is zero. However, it is common to assume a centre of rotation fixed in space and time, arbitrarily set at the centre of floatation or gravity. Identification of the ICR is crucial as it may lead to better motion reduction methods and can be leveraged to improve the designs. This includes better-informed fairlead placement and the reduction of aerodynamic load variability. In this paper, we propose a two-fold approach for the identification of the ICR: an analytical solution in the initial static equilibrium position, and a time-domain numerical approach for dynamic analysis in regular and irregular waves to understand the motion patterns and ICR sensitivity to environmental conditions. Results show that the ICR of FOWT depends on wave frequency and, at low frequencies, on wave height, due to the nonlinear viscous drag and mooring loads. An unexpected but interesting result is that the surge-heave-pitch coupling introduced by the mooring system leads to a dynamic phenomenon of signal distortion known as ”clipping” in the nonlinear audio signal processing area, which, through the introduction of higher harmonics, is responsible for the ICR sensitivity to motion amplitude.

As new offshore wind development sites move further from shore and existing sites enter their post-subsidy operating period, it is expected that operational expenditure (OpEx) will increase. In order to overcome these challenges, a more flexible and cost-effective maintenance solution is needed. One such solution is opportunistic maintenance (OM). This work provides an overview of the maintenance strategy used within other industries before providing an in-depth review of the work specific to offshore wind. The existing literature fails to agree on the specific definition of the term. This work proposes an all-encompassing definition of the term, reviewing maintenance ‘opportunities’ and their corresponding ‘action/response’. The review found that maintenance opportunities are either internal or weather-based, with each opportunity having a pre-determined trigger/response. This work proposes the introduction of a market-based opportunity, which has not been previously considered. As offshore wind farms now face increasing curtailment and negative pricing threats, this new OM framework, OM+, view these periods as maintenance opportunities. OM+ also provides a new definition for recording and reporting availability — moving from time/energy-based availability to market-based availability.

Seismic events in Italy and worldwide have highlighted the high vulnerability of unreinforced masonry (URM) structures in small historical centres. A key feature of these settlements is to be mostly composed of buildings in aggregate, i.e., interconnected by a more or less structurally effective connection. The seismic assessment of such buildings is quite debated in the literature and no shared tools procedures are currently available. The difficulty of standardization derives from the fact that structural units can be characterized by multiple features and configurations that determine a vast number of vulnerability factors, whose interdependency is not straightforward to be identified. The paper addresses this issue by combining evidence-based damage data with the potential offered by Machine Learning (ML) technique. Real data are used in combination with state-of-the-art ML algorithms carefully tuned via an advanced statistical procedure for two main purposes. The first one will be able to predict possible URM damages based on the vulnerability factor in both interpolation and extrapolation scenarios. The second purpose of the ML-based techniques will be to predict the most important vulnerability factors in making these predictions, namely to make the ML-based model explainable and informative about the underlying phenomena and not just predictive. The small historic centre of Casentino, hit by the 2009 L'Aquila earthquake, is adopted in the paper as the first test case study. A large amount of data was collected after the earthquake through in-situ surveys made by the Universities of Genova, Catania and Rome. Data include both geometric and structural factors, i.e., the input data supplied to the ML algorithm, as well as the actual seismic damage mechanisms, i.e., the output data expected to be predicted by the ML algorithm. As first application, ML techniques are applied only to data acquired on out-of-plane mechanisms.

The potential impact of underwater radiated noise from maritime operations on marine fauna has become an important issue. The most dominant noise source on a propeller-driven vessel is propeller cavitation, producing both structure-borne and radiated noise, with a broad spectrum that covers a wide range of frequencies. To ensure acceptable noise levels for sustainable shipping, accurate prediction of the noise signature is essential, and procedures able to provide a reliable estimate of propeller cavitation noise are becoming a fundamental tool of the design process. In this work, we investigate the potential of using computationally cheap methods for the prediction of underwater radiated noise from cavitating marine propellers. We compare computational and experimental results on a subset of the Meridian standard propeller series, behind different severities of axial wake, for a total of 432 experiments. The results indicate that the approaches employed can be a convenient solution for noise analysis during the design process.

Ultimate limit state (ULS) assessment examines the maximum load-carrying capacity of structures considering inelastic buckling failure. Contrary to the traditional allowable stress principle which is mainly based on experiences, the ULS assessment focuses on explicitly evaluating the structural safety margin and thus enables a consistent level of safety/risk between conventional and novel structural designs. Modern structures are usually designed as a network of plates and stiffeners (e.g., ship structures, offshore and onshore wind turbine, and land-based bridge) joined by welding which induces a residual stress field. Hence, predicting the ultimate strength reduction of stiffened panels caused by welding residual stress is a crucial problem addressed by many scholars with different approaches, among which the Nonlinear Finite Element Method (NLFEM) is the prevailing approach within the community of structural engineering. Unfortunately, the NLFEM has a high computational requirement which prevents its use in the design, appraisal, and optimisation phases of stiffened panels. To well approximate the nonlinear finite element method, a data-driven method is proposed in this paper, with a functional which is computationally expensive to build but computationally inexpensive to use allowing its application at design stage. Results obtained in different (i.e., interpolation and extrapolation) scenarios using data generated by a state-of-the-art NLFEM on a series of stiffened panels will support the proposed method.

The development of novel energy technologies to meet net zero carbon emission is essential in the provision of solutions to realize an increasing worldwide demand for renewable energy. Floating Offshore Wind Turbine (FOWT) is one of the emerging technologies to exploit the vast wind resources available in deep waters within the offshore wind sector. However, as a result of the complexity of a FOWT system, bringing FOWT technology up to speed requires a detailed understanding of the different disciplines within the system and the relationship between the FOWT system and the dynamics of the marine environment; hence, the need for Multidisciplinary Design Analysis and Optimization (MDAO) of the system. This paper reviews the MDAO of FOWT substructures/platforms proposed in the literature. This review covers an overview of floating offshore wind turbine substructures’ concepts, the design using geometric shape parameterization techniques and the analysis approaches (time and frequency domain) for response assessment of the FOWT system. It also provides a comprehensive review of MDAO frameworks for FOWT substructures. Regarding the optimization aspect, a review of some optimization algorithms used for floating offshore wind turbine substructure is provided, i.e., from the global search heuristic and meta-heuristics algorithms to the local search gradient-based optimization algorithms. This work further identifies the research gaps in MDAO for FOWT substructures. The main proposed future research areas to address these gaps are: increasing design space richness by adopting more advanced parametrization techniques to represent the platform geometry (and other characteristics), utilize surrogate/meta models to replace the most computationally expensive high-fidelity models needed for quick sensitivity studies before detailed analyses on selected models are conducted, and exploring the upscaling of the geometric design parameters of an optimal shape parameterized FOWT platforms derived from existing designs which can be coupled with new generation highly rated and heavier turbines.

Monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance is a crucial problem that attracts the attention of both academy and industry. Effective and reliable tools to address this would allow a timely cleaning procedure able to trade off costs, efficiency, and environmental impacts. In this paper, the authors carry out a critical review, accompanied with summary tables, of the biofouling problem with a particular focus on the shipping industry and the state-of-the-art techniques for monitoring and evaluating the biofouling state and its effects on the vessel's hull and propeller performance. In particular, different techniques are grouped according to the three main families of numerical models that have been designed and exploited in the literature: Physical Models (i.e., models relying on the mechanistic knowledge of the phenomena), Data-Driven Models (i.e., models relying on historical data about the phenomena together with Artificial Intelligence), and Hybrid Models (i.e., a hybridisation between Physical and Data-Driven Models). A conclusion from the performed review, open problems, and future direction of this field of research is detailed at the end of the review.

In this paper the authors investigate the problems of predicting the fuel consumption and of providing the best value for the trim of a vessel in real operations based on data measured by the onboard automation systems. Three different approaches for the prediction of the fuel consumption are compared: White, Black and Gray Box Models. White Box Models (WBM) are based on the knowledge of the physical underling processes. Black Box Models (BBMs) build upon statistical inference procedures based on the historical data collection. Finally, the authors propose two different Gray Box Model (GBM) which are able to exploit both mechanistic knowledge of the underlying physical principles and available measurements. Based on these predictive models of the fuel consumption a new strategy for the optimisation of the trim of a vessel is proposed. Results on real world operational data show that the BBM is able to remarkably improve a state-of-the-art WBM, while the GBM is able to encapsulate the a-priory knowledge of the WBM into the BBM so to achieve the same performance of the latter but requiring less historical data. Moreover, results show that the GBM can be used as an effective tool for optimising the trim of a vessel in real operational conditions.