The research on the dynamics analysis-based energy-saving technology is significant to reduce ship energy consumption and greenhouse gas emissions. The adoption of dynamics analysis theory and Computational Fluid Dynamics (CFD) approaches can achieve the optimal design and energy efficiency improvement of the ship. This research focuses on the ship energy efficiency improvement technology through CFD-based dynamics analysis, including the hull optimization design, drag reduction technology, navigation state optimization, efficient propulsion devices, energy-saving equipment, and the coupled dynamics analysis for comprehensive performance optimization. The current research and application status of ship performance optimization based on CFD approaches for energy-efficient shipping are systematically analyzed. On this basis, the challenges and problems in the application of the CFD-based energy-saving technology are discussed, and the future research works are proposed, aiming to provide references for the development of ship energy-saving technology based on CFD approaches. The analysis results show that the adoption of CFD-based dynamics analysis methods can effectively optimize the ship dynamics performance, thus reducing ship energy consumption and pollution gas emissions. In the future, the CFD-based coupled dynamics analysis should be further studied to achieve the overall performance optimization of the integrated ship-engine-propeller-appendages system under the influence of multiple complex factors, to continuously improve the ship energy efficiency, thus promoting the low-carbon development of the shipping industry.

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This study investigates the optimization of the operation and maintenance of offshore wind turbines based on condition monitoring data. Due to their increasingly remote and challenging location, a decision framework is proposed that optimizes the cost and risk of maintenance scheduling based on, dynamic Bayesian network based, iterative estimation of turbine lifetime. This allows for the combining of predictive and opportunistic maintenance strategies, scheduling preventative component replacements to minimize lost production, while maximizing lifetime and optimizing use of resources. Assessment of related literature and applications suggests the approach could lead to a reduction of maintenance costs that exceeds 30%. The proposed framework relies on effective fault detection and prognosis of wind turbine components, realised through the implementation of machine learning techniques on the turbine’s own SCADA system. The installing of additional sensors can potentially increase the capability of this system for more advanced diagnosis and localization of a fault.

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Submarine power cables in offshore wind farm operate within a complex multiphysics environment. Despite being designed to be both flexible and robust though, their mechanical characteristics are susceptible to variations of thermal field. Bending studies of submarine power cables present challenges rooted in geometry complexity, component contact, and material non-linearity, compounded by the intricate stick–slip mechanism. The difficulty is further intensified when incorporating the thermal impact on material and contact properties. This paper presents a three-dimensional Representative Volume Element (RVE) model for predicting the nonlinear bending stiffness of three-core submarine power cables. The RVE model, developed with constant curvature and periodic boundary conditions, incorporates dashpots to address the stick–slip challenges associated with cable bending. This modeling approach minimizes the required cable length for bending analysis, significantly reducing computational costs. Validation against the bending test of a three-core cable at room temperature, alongside comparison with a 3D full-scale finite element (FE) model, demonstrates the efficiency and accuracy of the proposed RVE approach. Furthermore, the study explores the thermal effect on cable bending, highlighting the capabilities of the proposed RVE model in facilitating thermal–mechanical coupled flexural analysis of submarine power cables. This research contributes to advancing understanding and optimization of submarine power cable design for offshore applications.

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Offshore wind energy is expected to be the most significant source of future electricity supply in Europe. Offshore wind farms are located far from the shores, requiring a fleet of various types of vessels to access sites when maintaining offshore wind turbines. The employment of the vessels is costly, accounting for the majority of the total O&M costs for offshore wind energy. Therefore, configuring the size and mix of the vessel fleet to support maintenance operations in a cost-effective manner is an issue of importance to enhance economics of offshore wind sector. In this paper, a discrete event simulation based model is proposed to present how a mixed vessel fleet with the specific configuration, including crew transfer vessels, field support vessels, and heavy lift vessels, performs maintenance for an offshore wind farm. The economic performance of the vessel fleet under a predetermined condition-based opportunistic maintenance strategy is investigated by using the model. A metaheuristic algorithm, simulated annealing, is employed to find the optimal fleet size and mix to make leasing decisions with the minimum costs. The performance of the developed approaches is evaluated by using a generic offshore wind farm in the North Sea. The sensitivity analysis is performed to investigate the most influential O&M factors.

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Offshore wind farms are a promising source of renewable energy, but they face significant challenges in terms of operation and maintenance (O&M). Traditional scheduling models often overlook the potential of condition-based maintenance (CBM). Addressing this gap, this paper introduces a novel framework, incorporating principles of Model Predictive Control (MPC), to optimize the O&M scheduling of offshore wind farms using prognostic-driven maintenance. The framework integrates probabilistic remaining useful life (RUL) prognosis in a mixed-integer linear programming (MILP) optimization model with a rolling horizon approach, in alignment with MPC’s predictive and adaptive decision-making approach. The optimization model determines the optimal time to replace each component by minimizing the expected cost over the expected lifetime. This approach seeks to achieve the lowest expense while guaranteeing the highest utilization rate of each component. For the case study presented, the total O&M costs are reduced by up to 15% with respect to corrective maintenance strategies.@en

The complex structure and material property of a cable, particularly the stick-slip issue among its components pose the challenge for the bending analysis of submarine power cables. The calculation time and convergence problem of a full model makes the simulation unpractical during the design phase. This paper takes advantage of the peculiar structural property of helical components inside a cable, proposing a computational homogenization approach for analyzing the cable behavior under bending from global and local perspectives. This method assumes a macro model that is based on the theory of periodic beamlike structure, and a short-size micro model that is solved through a detailed finite element study. Results demonstrate the efficiency and capability of the proposed model that considers the structure nonlinearity and contact condition of a multi-layer cable with helical wires.

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Effective operation and maintenance (O&M) management is significant for enhancing the economic performance of offshore wind farms. Despite recent research progress in O&M, there remains a gap in integrating health prognostics and spare parts inventory into decision-making processes at the scale of offshore wind farms. To bridge this gap, this paper develops an optimisation framework integrating these aspects to establish cost-effective joint maintenance and inventory policies. In the framework, a maintenance policy is firstly developed to plan maintenance actions based on component health and maintenance opportunities. Meanwhile, in order to support maintenance implementation, a multi-echelon inventory network using (s, S) policies is proposed to store diverse units across distinct warehouses. A genetic algorithm (GA) is then employed to identify the optimal policy, aiming to minimise overall costs. Upon developing the optimisation framework, in order to illustrate the application of the proposed approach in practice, a numerical simulation of a generic offshore wind farm in the North Sea is performed. Results demonstrate that comprehensive O&M management considering interrelationship between maintenance and inventory policies reduces overall costs, showcasing its capacity in strengthening the economic performance. Finally, sensitivity analysis is performed to investigate the most influential O&M factors, providing actionable insights for O&M management.

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The structural complexity of submarine power cables (SPCs) presents significant challenges in their local mechanical analysis. This paper introduces an advanced modeling method developed for analyzing the mechanical behavior of SPCs under tension, emphasizing both accuracy and efficiency. The method’s accuracy is validated through a comparison of simulation results with tension tests conducted on a three-core SPC sample. Efficiency is demonstrated by the superior calculation speed of our model relative to traditional full-scale models. This improved performance is achieved by adopting periodic boundary conditions derived from the homogenization method applied to slender beam-like structures, and by employing a specialized combination of elements to model the helical metal components within the SPCs. The resulting model provides robust capabilities for the mechanical analysis of SPCs under tension and demonstrates significant possibility in accommodating various other loadings.@en

Numerical modeling of the floating offshore wind turbine (FOWT) dynamics plays a critical role at the design stage of a floating wind project. Still, there exist challenges for verification of efficient engineering models against experimental results. Recently, an experimental campaign was carried out for a 1:96 downscaled model of the OC4-DeepCWind semi-submersible platform with mooring lines made of fiber ropes and chains. Leveraging the results of this campaign, this paper focuses on the development and calibration of a numerical model for the semi-submersible platform with a focus on the dynamic responses under bichromatic waves. In the numerical model, the hydrodynamic loads are modeled based on the potential flow theory with Morison drag. The lumped mass method is applied to model the mooring system. Both free decay tests and bichromatic wave conditions are considered in the model calibration process, and key uncertain parameters (e.g., mooring line length) that affect the response have been identified and discussed. Using the proposed calibration procedure, we establish a reasonably good numerical model for prediction of the platform motion and mooring dynamics. The low-frequency responses of the platform under bichromatic waves are well-captured. These outcomes contribute to the development of efficient numerical FOWT models under experimental uncertainty.

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This paper introduces a novel analytical approach aimed at predicting the growth of surface cracks in metallic pipes reinforced with Fibre-Reinforced Polymers (FRPs) subjected to cyclic bending and/or tension loads. The primary objective of this study is to develop a comprehensive analytical model that accounts for multiple factors influencing crack growth, namely stress reduction, crack-bridging effect, stiffness degradation, and fatigue damage of the FRP-to-metal interface simultaneously. By considering these simultaneous effects, our proposed approach enables accurate evaluations of the stress intensity factors (SIFs) at both the surface point and the deepest point of a surface crack. To facilitate practical implementation, we have developed an in-house program that automates crack growth rate and residual fatigue life predictions. The proposed analytical method has been validated through a series of comparisons with experimental data and finite element results, demonstrating its accuracy in estimating fatigue lives. The key novelties of this research lie in the holistic consideration of multiple dominating and influencing factors, the achievement of precise SIF evaluations, and the development of an automated prediction tool for practical applications. Overall, our findings confirm the suitability of the proposed analytical approach for predicting crack growth and provide valuable insights for guiding the design of FRP reinforcement in surface-cracked metallic pipes. This work contributes to advancing the understanding of crack growth behaviour in FRP-reinforced metallic pipes and opens new possibilities for the safe and efficient design of such structures.

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The determination of maintenance strategies is subject to complexity and uncertainty arising from variable offshore wind farm states and inaccuracies in model parameters. The most common method in the existing studies is to adopt an open-loop approach to optimize a maintenance strategy. However, this approach lacks the ability to capture periodic operational state of the wind farm and the awareness of eliminating uncertainty. Consequently, the determined strategy is inadequate to instruct maintenance activities, inducing excessive revenue losses. In this paper, a closed-loop maintenance strategy optimization method is proposed for decision-makers to identify a more profitable manner of wind farm maintenance management. The life-cycle maintenance optimization problem is decomposed into a sequence of sub-optimization problems covering multiple time periods by using a rolling-horizon approach. Each sub-optimization problem is intentionally designed based on the monitored state of the wind farm and the available reliability, availability, and maintainability (RAM) database. Meanwhile, the decision maker consciously mitigates the parameter uncertainty in the maintenance model gradually by updating the current database. Compared to conventional strategies covering the entire lifetime of wind farms, the proposed maintenance strategy is periodically adjusted to provide a series of sub-strategies. The proposed approach was applied in a simulation experiment, a generic small-scale offshore wind farm, to assess its performance. Computational results show that adapting maintenance strategies based on the current state of the wind farm can reduce revenue losses in comparison to conventional open-loop strategies. In addition, the benefits of updating the RAM database in decreasing revenue losses is revealed.

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Predicting the bending behaviours of a submarine power cable (SPC) is always a tough task due to its complex geometry and inner layer contact, not to mention the stick–slip mechanism. A full-scale finite element model is cumbersome during the early design stage and a more efficient model for practical use is required. Therefore, in this paper, a repeated unit cell (RUC) technique-based FE model is developed, which simplifies the bending analysis of SPCs using a short-length representative cell with periodic conditions. The verification of this RUC model is conducted from cable and component levels, respectively. The cable overall response is validated by the curvature-moment relationships from our cable bending tests regarding four cable samples whose material properties are obtained through a set of material tests. As for the component level, the behaviours of particular components are studied and compared with the results from a full-scale numerical model. Discrepancy is observed between the RUC model and the test, which can be explained by the distinctions of boundary conditions between these two methods. The proposed Cable-RUC model has been found robust and computationally efficient for studying SPCs under bending.

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Helical wires are a type of structure winding around on an underneath layer in flexible structures. They constitute a structure layer that provides mechanical protection and sufficient flexibility. The cross section of a helical wire could differ from round to rectangular. The curvature increments of a helical wire on a bent flexible structure can be influenced by the cross section shape due to the contact restriction from the neighbouring layer. The shape effect causes a different mechanical behaviour of the wire itself. However, this effect is not fully understood up till now. This paper mainly investigates the curvature increments of helical wires with rectangular and round cross section by using numerical method. This model takes advantage of the properties belonging to solid elements and beam elements by embedding the latter into the former. The solid element is able to retain the geometry detail of the cross section as much as possible in order to get deep insight of the shape effect. The beam element based on Timoshenko beam theory with well defined Frenet-Serret frames can accurately output the curvature increments in three local directions. Then widly-used analytical models are presented and deployed to verify the numerical results. The research results show the application condition of the analytical expressions and benefit the cable/umbilical/flexible pipe designers.@en

Optimization of ship energy efficiency is an efficient measure to decrease fuel usage and emissions in the shipping industry. The accurate prediction model of ship energy usage is the basis to achieve optimization of ship energy efficiency. This study investigates the sequential properties of the actual voyage data from a VLOC. On this basis, a model for predicting ship energy consumption is established by adopting a LSTM neural network that has better prediction performance for sequential datasets. To further enhance the performance of the established LSTM-based model, the network structures and hyperparameters are optimized by using Genetic Algorithm. Lastly, the application analysis is conducted to validate the established GA-LSTM-based model for ship fuel usage prediction. The established model for ship energy usage shows a significant improvement in prediction accuracy, compared to the original LSTM-based model. Meanwhile, the developed prediction model is more accurate than the existing BP, SVR, and ARIMA-based energy consumption models. The prediction errors for the ship's operational energy efficiency adopting the established GA-LSTM-based model can reach as low as 0.29%. Therefore, the established model can effectively predict the ship fuel usage under different conditions, which is essential for the optimization and improvement of ship energy efficiency.

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Submarine power cables are considered lifelines in wind farm engineering, playing a key role in transporting electric current produced by wind turbines or wave converters. The bending behaviour of a power cable is a complex issue due to both its various materials and multi-layer structures. The copper conductor in the middle of a power cable is especially complex as it is composed of numerous helical copper wires. The influence of the copper conductor on the overall bending behaviour is still not clear due to rare studies regarding this topic. This paper focuses on the pure bending test of a naked copper conductor from a real cable, then theoretical and numerical methods are used to generate the bending stiffness in order to compare it with each other. The pros and cons of each method are clarified and the obtained conclusions will pave the way for the study of the overall power cable.

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While offshore wind energy is showing enormous potential, effective approaches to enhance its economics are being sought at the same time. The design of maintenance strategy is a type of strategic decision-making for offshore wind farms, aiming to improve energy production and reduce maintenance expenses. As a complicated and challenging task, the maintenance decision-making is confronted with various types of uncertainty in the model. The presence of uncertainty affects the estimation of maintenance performance, and renders the determined maintenance decisions sub-optimal or even inappropriate. In this paper, the authors propose an integrated decision-making framework incorporating i) a maintenance model which is applied to estimate maintenance performance, including maintenance costs and production losses, ii) a probabilistic uncertainty modelling approach which is used to characterize different types of uncertainty and a Monte Carlo method is adopted to generate stochastic scenarios, and iii) a multi-objective optimization method used to find the optimal decisions in the presence of conflict between multiple objectives. The uncertainties considered in the model include the stochastic attributes of time to failure, deviation between real and predicted failure times of components, and uncertain maintenance consequences. The proposed framework was applied in a generic 150MW-offshore wind farm located in the North sea. Results demonstrate that the deterministic scenario underestimates the maintenance costs and production losses, leading to the consequence that the developed maintenance strategy becomes unsatisfactory. A new series of solutions including priority solutions and trade-offs is provided for decision-makers to satisfy different goals while involving uncertainty. In addition, the influence of different uncertainties on the maintenance performance is quantified to assess the significance. The proposed optimization framework constitutes a useful decision-making tool to instruct the long-term maintenance strategy for offshore wind farms in a practical environment involving a high degree of uncertainty.

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Wing-diesel engine-powered hybrid ships can effectively reduce fuel consumption and CO₂ emissions by using wind energy as the auxiliary driving power. The energy optimization management of the hybrid system can further improve the ship's energy efficiency. To achieve this purpose, it is significant to establish an effective energy consumption model for the energy optimization management of the hybrid system. Therefore, an energy consumption model is established based on the energy conversion analysis of the hybrid power system in this paper. This model can effectively describe the energy consumption of the hybrid ship under different navigational environmental conditions. Then, a joint optimization method of the wing attack angle and of the sailing speed for the hybrid ship is proposed by adopting a swarm intelligence optimization algorithm, in order to reduce energy consumption and CO₂ emissions of the hybrid ship under different navigational environmental conditions. Finally, the energy consumption optimization potentials by adopting the hybrid power system and the proposed joint optimization method are analyzed. The results show that the energy consumption and CO₂ emissions along a typical route can be reduced by about 4.5%. This study provides an important basis for future practical operations of wing-diesel engine-powered hybrid ships.

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This paper conducts a numerical analysis on the external surface cracked steel pipes reinforced with Composite Repair System. A three-dimensional finite element (FE) model is developed to calculate the Stress Intensity Factor (SIF) of the surface crack, and the crack growth process is evaluated by the Paris’ law. The effect of FRP-to-steel interfacial bond condition on the SIF evaluation has been considered by incorporating the cohesive zone modelling. Then the FE model is validated by the experimental results. Thereafter, major issues including “interfacial bond condition” and “reinforcement effectiveness and influential parameters” have been discussed. The results indicate the reinforcement effectiveness on reducing the SIF owes to the decreasing of stress magnitude and the crack-bridging effect. Because of the crack-bridging effect, composite reinforcement performs more efficiently on reducing the SIF at the surface point than at the deepest point of the surface crack. The negative influence of the FRP-to-steel bond condition on the surface crack growth is not as significant as on reinforcing through-thickness cracks. However, since the interfacial stiffness is sensitive to the adhesive thickness, choosing an ideal adhesive thickness to acquire a good reinforcement effectiveness and to avoid potential interfacial bond failures is recommended.

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Ship energy consumption and emission prediction are critical for ship energy efficiency management and pollution gas emission control, both of which are major concerns for the shipping industry and hence continue to attract global attention and research interest. This article examined the energy efficiency data sources, big data analysis for energy efficiency, and analyzed the ship energy consumption and emission prediction models. The ship energy consumption and pollution gas emission prediction models are comprehensively summarized based on the modeling method and principles. The theoretical analysis and artificial intelligence-based ship energy consumption model, as well as the top-down and bottom-up ship emission prediction models, are thoroughly examined in terms of influencing factors, model accuracy, data sources, and practical applications. On this basis, the challenges of ship energy consumption and emission prediction are discussed, and future research suggestions are proposed, providing a foundation for the development of ship energy consumption and emission prediction technologies. The analysis results show that the principles, parameters of concern, and data quality all have a significant impact on the performance of the prediction models. Consequently, the prediction model's accuracy can be improved by combining intelligent algorithms and machine learning. In the future, high precision, self-adapting, ship fuel consumption and emission prediction models based on artificial intelligence technology should be further studied, in order to improve their prediction performance, and thus providing solid foundations for the optimization management and control of the ship energy consumption and emissions.

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A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines, especially those served for a long time. Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion. However, it is time-consuming for finite-element method and there is a limited application range by using empirical formulas. In order to improve the prediction of strength, this paper investigates the burst pressure of line pipelines with a single corrosion defect subjected to internal pressure based on data-driven methods. Three supervised ML (machine learning) algorithms, including the ANN (artificial neural network), the SVM (support vector machine) and the LR (linear regression), are deployed to train models based on experimental data. Data analysis is first conducted to determine proper pipe features for training. Hyperparameter tuning to control the learning process is then performed to fit the best strength models for corroded pipelines. Among all the proposed data-driven models, the ANN model with three neural layers has the highest training accuracy, but also presents the largest variance. The SVM model provides both high training accuracy and high validation accuracy. The LR model has the best performance in terms of generalization ability. These models can be served as surrogate models by transfer learning with new coming data in future research, facilitating a sustainable and intelligent decision-making of corroded pipelines.

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