Mooring systems are required to keep floating wave energy converters (WECs) on station. The mooring concept might impact the performance of the WEC, its cost and its integrity. With the aim of clarifying the pros and cons of different mooring designs, we present the results from physical model experiments of three different mooring concepts in regular and irregular waves, including operational and survival conditions. The parameters investigated are the tension in the cables, the motions of the device in the different degrees of freedom and the seabed footprint in each case. We can see that the mooring system affects the performance of the wave energy converter, but the magnitude of the impact depends on the parameter analysed, on the mode of motion studied and on the conditions of the sea. Moreover, different configurations have similar performances in some situations and the choice of one over another might come down to factors such as the type of soil of the seabed, the spacing desired between devices, or environmental impacts. The results of our experiments provide information for a better selection of the mooring system for a wave energy converter when several constraints are taken into account (power production, maximum displacements, extreme tensions, etc).
A novel damping system is developed to address offshore wind turbine tower vibration exacerbated by global warming-induced coastal extreme weather. Through parametric optimization, it stabilizes nacelle displacement under normal loads and reduces responses in diverse wind conditions: 18.8% max bending stress reduction during gusts, 26.3% nacelle displacement mitigation under high turbulence, and 7.9% displacement standard deviation reductions in 50-year extreme winds. A Norwegian wind farm extends tower life by 44% at the tower top and 99.36% at the tower base. Under varying gust angles, it reduces nacelle displacement (4.3%) and bottom bending moment (3.2%), enhancing structural stability. These demonstrate their potential to cut maintenance costs and extend lifetime, which is crucial for offshore wind turbine development.
Mooring failures significantly threaten the stability of Floating Offshore Wind Turbines (FOWT) under extreme environmental conditions. This study presents an innovative shared damping mooring system incorporating Seaflex dampers to improve structural stability and operational reliability. Dynamic simulations under 1-year and 50-year return period sea states demonstrate the system’s effectiveness. Under Ultimate Limit State (ULS) conditions, the system reduces surge displacement by 59%, pitch angle by 47%, and mooring line tension by 72%. Under Accidental Limit State (ALS) conditions, it mitigates load spikes, reduces drift displacement by 60%, and improves safety factors by 50%. The comparison shows chain and wire rope configurations have better load reduction performance in the shared damping scheme. Lightweight and adaptable, the Seaflex dampers enhance broad-spectrum damping without affecting platform buoyancy. This study provides a robust solution for improving FOWT safety and durability in harsh marine environments, enabling large-scale offshore wind energy development.
This study proposes a novel tower damping system to enhance the structural performance of the NREL 5 MW semi-submersible wind turbine under operational and extreme load conditions. Environmental load data from the Norwegian MET center was analyzed to characterize the loading conditions for floating offshore wind turbines (FOWT). The probability density spectrum of sea state data was employed to identify operational load conditions. At the same time, the Inverse First-Order Reliability Method (IFORM) was used to derive the 50-year extreme sea state. Perform a fully coupled Aero-Hydro-Servo-Elastic simulation of the FOWT dynamic model with a damping system using OrcaFlex software. The results reveal that: Under operational sea states, the turbine tower-top displacement was reduced by 60–70%, and acceleration by 30–40%, enhancing tower-top stability. Under extreme loads, tower-top acceleration was reduced by 5–7%, and displacement by 6–8%. Cumulative damage assessments indicate a reduction in fatigue damage of up to 72%, with the effective fatigue life of the tower base extended by 136%. The proposed damping system significantly reduces vibration under fatigue and extreme load conditions.
This study proposes a novel tower damping system to enhance the structural performance of the NREL 5 MW semi-submersible wind turbine under operational and extreme load conditions. Environmental load data from the Norwegian MET center was analyzed to characterize the loading conditions for floating offshore wind turbines (FOWT). The probability density spectrum of sea state data was employed to identify operational load conditions. At the same time, the Inverse First-Order Reliability Method (IFORM) was utilized to derive the 50-year extreme sea state. Perform a fully coupled Aero-Hydro-Servo-Elastic simulation of the FOWT dynamic model with a damping system using OrcaFlex software. The results reveal that: Under operational sea states, the turbine tower-top displacement was reduced by 60–70%, and acceleration by 30–40%, enhancing tower-top stability. Under extreme loads, tower-top acceleration was reduced by 5–7%, and displacement by 6–8%. Cumulative damage assessments indicate a reduction in fatigue damage of up to 72%, with the effective fatigue life of the tower base extended by 136%. The proposed damping system significantly reduces vibration under fatigue and extreme load conditions.
The completeness and high predictability of hazardous scenarios by hazard identification methods are issues in risk analyses. A way to the improvement is to carry out both an exhaustive - to the extent possible - post-accident and predictive accident analysis. Currently, Natural Language Processing (NLP) allows quick processing of many accident reports. In combination with graphical tools, it is now even possible to automatically output causal diagrammatic models of accidents and visualize them on a multi-scenario accident diagram. A step forward is the application of NLP to support predictive analysis. Predictive accident analysis focuses on identifying deviations from expected or normal conditions, the subsequent events following these deviations, and their interactions leading to an accident. The expected or normal conditions are typically outlined in specifications and procedures. This paper demonstrates how NLP can assist hazard identification and predictive accident analysis during lifting operations on ships and offshore platforms.
The report focuses on analysing on-deck accidents in offshore environments using data from the Health and Safety Executive (HSE) which covers the period 1980-2005. It applies the Accident Anatomy (AA) method, which maps accident causes and consequences using fault trees and cause-consequence diagrams (CCDs). Unlike previous analyses, this report aims to extract deeper insights into accident patterns beyond general statistics.
For this report, on-deck operations involve material handling, tool use, and equipment operation in offshore environments.
The study analysed 10,846 records that cover accident events on both fixed and floating offshore units. The report focuses on cases where injuries or fatalities occurred. The analysis described in the report mapped 77 accident-prone operations and provides a detailed causal understanding of offshore accidents.
Despite the exhaustiveness of the analysis, there are limitations related to the used data. The HSE records primarily document physical and operational aspects of the accidents, leaving out design-related or organisational factors. Next, probabilities for the accident events considered in the analysis are not provided. This is due to the incomplete nature of the sources and the lack of information on the number of opportunities for accidents. The computation of probabilities will be feasible if data on the frequency of use of relevant components, machines, personnel, and workplaces has been also collected.
Subsea power cables are crucial for transmitting electrical power between offshore installations, islands, and onshore infrastructure. The demand for these cables has surged with the expansion of offshore wind farms. Despite mechanisation, divers are still needed for tasks such as installation, inspection, and remedial work, facing hazards like entanglement, equipment damage, and those to the environment. Therefore, analyzing accidents in diving operations during subsea cable installation is essential to develop safety measures that protect divers and ensure successful installations. This document reports an analysis of the hazards and accident events linked to diving operations during subsea cable installation. Few risk assessments of these operations have been made publicly available.
Various methods can be used to analyze diving accidents, but this document reports on the use of the Accident Anatomy (AA) method. The AA method combines fault trees and cause-consequence diagrams to map accident causes and consequences. In the AA method, evidence-based (post-accident) analysis is used jointly with predictive analysis to identify deviations from normal conditions that could lead to accidents.
To exhaust the identification of hazards, the AA method is additionally powered by an error mode classification checklist, which classifies errors that produce similar effects on a system. Analysts used this checklist to identify hazards for each basic diving operation task identified.
As a data source, 163 documents were analyzed, including accident records, regulations, manuals, and scientific papers. Basic tasks associated with diving operations are identified, along with hazards for each task. Predictive analysis identifies potential events and unwanted consequences when normal conditions (specified in safety procedures and specifications) deviate. The unwanted consequences that were found include delays, technical problems, injuries, and fatalities. Ultimately, safety measures are identified for each basic task to reduce the effects of hazards.
Life depends on healthy oceans that provide ecosystem services (ES) to humans, including provisioning, regulating, supporting, and cultural ES (Kovalenko et al., 2023). However, biodiversity, habitats, and the delivery of marine ES and resources are increasingly threatened by growing human activities in the oceans (Worm et al., 2006). Blue-growth activities, such as shipping and energy, eutrophication, and climate change represent major pressures that affect marine ecosystems (Halpern et al., 2008; Ehlers, 2016). Over the past two decades, increasing scientific attention has focused on the need to preserve and restore healthy marine waters and their role in adapting to climate change (Santos et al., 2020). This challenge calls for holistic approaches that advance our knowledge. Within the contributions to this Research Topic (see Figure 1), three themes are central to driving further research to expand our understanding in this interdisciplinary field.
In a number of experiments and field tests of point absorbers, snap loads have been identified to cause damage on the mooring cables. Snap loads are basically propagating shock waves, which require special care in the numerical modeling of the mooring cable dynamics. In this paper we present a mooring cable model based on a conservative formulation, discretized using the Runge-Kutta discontinuous Galerkin method. The numerical model is thus well suited for correctly capturing snap loads. The numerical model is verified and validated using analytic and experimental data and the computed results are satisfactory.