Metocean Research Articles

Design, deployment, and operation of an experimental offshore seaweed cultivation structure

Seaweed cultivation systems suitable for offshore and exposed locations have the potential to enable expansion of global seaweed production to levels capable of substantially supplementing or offsetting terrestrial agriculture. A demonstration scale, experimental seaweed cultivation system (farm) intended to withstand exposed ocean environments was designed, deployed, planted with kelp (a type of seaweed), monitored and decommissioned. Objectives for the field program included: [1] observation of the farm’s behavior and survivability in exposed ocean conditions, [2] demonstration of novel farm system design features and component technologies, and [3] evaluation of farm operability. Novel design features included a lattice mooring system geometry, multi-shaft helical anchors, and the use of fiberglass rod as a replacement for rope anchor lines and kelp growth substrate in order to mitigate the risk of marine animal entanglement. New hardware was developed for transmitting tension loads to and from fiberglass rods, enabling their use in the farm. A numerical model was used in the design stage to simulate the farm in ocean conditions. Analysis results were used to specify farm components, evaluate sensitivities to installation precision, identify potential issues with component interaction, consider operational constraints and investigate the implications of single point failures. The farm was installed at a Gulf of Maine, USA site and planted with Saccharina latissima (sugar kelp) in November 2021 through January 2022 and monitored for one growth season. Kelp was sampled 3 times and met-ocean conditions were measured throughout the season. Kelp was harvested in May 2022 and the structure was removed June 2022. Recovered components were inspected for wear. The farm survived a series of storm events with significant wave heights greater than 2 m and at least one storm with a maximum wave height of 5.9 m. The farm system experienced only minor corrosion, wear and damage. Farm operations revealed the need for improved accessibility, tension control and anchor placement accuracy. Observations and tests revealed relatively poor kelp holdfast attachment to the fiberglass rope replacement. Nonetheless, demonstration results suggest that the experimental farming system design was suitable for use in exposed settings.

Oil spill risk analysis for the NEOM shoreline

A risk analysis is conducted considering an array of release sources located around the NEOM shoreline. The sources are selected close to the coast and in neighboring regions of high marine traffic. The evolution of oil spills released by these sources is simulated using the MOHID model, driven by validated, high-resolution met-ocean fields of the Red Sea. For each source, simulations are conducted over a 4-week period, starting from first, tenth and twentieth days of each month, covering five consecutive years. A total of 180 simulations are thus conducted for each source location, adequately reflecting the variability of met-ocean conditions in the region. The risk associated with each source is described in terms of amount of oil beached, and by the time required for the spilled oil to reach the NEOM coast, extending from the Gulf of Aqaba in the North to Duba in the South. To further characterize the impact of individual sources, a finer analysis is performed by segmenting the NEOM shoreline, based on important coastal development and installation sites. For each subregion, source and release event considered, a histogram of the amount of volume beached is generated, also classifying individual events in terms of the corresponding arrival times. In addition, for each subregion considered, an inverse analysis is conducted to identify regions of dependence of the cumulative risk, estimated using the collection of all sources and events considered. The transport of oil around the NEOM shorelines is promoted by chaotic circulations and northwest winds in summer, and a dominant cyclonic eddy in winter. Hence, spills originating from release sources located close to the NEOM shorelines are characterized by large monthly variations in arrival times, ranging from less than a week to more than 2 weeks. Similarly, large variations in the volume fraction of beached oil, ranging from less then 50% to more than 80% are reported. The results of this study provide key information regarding the location of dominant oil spill risk sources, the severity of the potential release events, as well as the time frames within which mitigation actions may need to deployed.

Metocean conditions at two Norwegian sites for development of offshore wind farms

This paper examines metocean data from NORA3, a state-of-the-art wind and wave hindcast dataset for Northern Europe. Two offshore Norwegian areas, Utsira Nord (UN) and Sørlige Nordsjø II (SN2), are investigated. Both areas offer significant potential for the offshore wind sector. UN is situated in deep-sea water, suitable for floating offshore wind turbines. In contrast, SN2 lies in intermediate waters and ranks among the North Sea’s most promising regions allocated for offshore wind. Data from NORA3, originally on a 3-km resolution grid, are resampled into unstructured grids spanning from 1982 to 2022. This refined dataset offers a climatology time scale with superior spatial and temporal resolution compared to most other hindcast and reanalysis databases. The study examines mean wind speed and direction across seven levels, ranging from 10 m to 750 m above the surface. Analyses of extreme wind and wave conditions have been conducted. Results reveal that UN experiences higher extreme wave heights than SN2 while the extreme wind speeds may be substantially larger at SN2 than UN. Moreover, this study establishes joint distribution models that encompass several parameters, including mean wind speed, significant wave height, wave spectral peak period, and direction difference between wind and waves. Thus, this metocean data is valuable for designing and analyzing floating wind farms over their lifecycles.

Response of the offshore jacket platform at its ultimate strength under operating and extreme loads: a Malaysian waters case study

ABSTRACT Before reassessing the reliability condition of an offshore jacket platform, it is essential to understand the behaviours that the offshore platform exhibits while it is at sea. This paper presents the response of the offshore jacket platform as its ultimate strength under operating and extreme loads. Malaysian waters’ metocean data from a 1-year return period (operating condition) and a 100-year return period (storm condition) are used to predict the four-legs jacket platform's behaviour by evaluating its ultimate strength. Also, two values of minimum design water depth (WD) of 105.9 m and maximum design WD of 115.4 m are chosen. The SACS software, a nonlinear finite element analysis software, is used to calculate the jacket platform's ultimate strength, called pushover analysis. For instance, the pushover analysis result will determine the Reserve Strength Ratio (RSR) as an approach to examining the offshore jacket platform's ultimate strength. Responses to the offshore jacket platform are base shear design, base shear ultimate, rotation, displacement, and moment, which illustrate the behaviour of the offshore jacket platform. There are eight combinations of environmental load directions are applied, to obtain the structure's weakest part. It is found that 45° is the critical direction for maximum design WD, while 90° is the critical direction for minimum design WD. The study's findings indicate that the response of the offshore jacket platform is very important for the safe design and operation of offshore jacket platforms.

Model-based comparisons of near-coincident TerraSAR-X and COSMO-SkyMed VV-polarized SAR measurements over sea surface with and without oil slicks

ABSTRACT This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar (SAR) missions. Two SAR scenes were acquired with a temporal difference of about 36 minutes, under similar met-ocean conditions, during the North Sea’s Gannet Alpha oil spill accident. The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model (AIEM) while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance (MLB) to include the damping effect of oil slicks. Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces. When dealing with oil-covered sea surfaces, the predicted backscattering reasonably agrees with TSX measurements, while it overestimates the CSK ones. This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.

Reliability Analysis of Offshore Wind Structures Considering the Korean Met-Ocean Conditions

Reliability Analysis of Offshore Wind Structures Considering the Korean Met-Ocean Conditions

Research on fatigue damage mitigation of offshore wind turbines by a bi-directional PSTMD under stochastic wind-wave actions

In view of an especial frequency tuning pattern, a prestressed tuned mass damper (PSTMD) with a larger mode damping has been proposed and explored its vibration control competence for offshore wind turbine (OWT). Unlike the vibration control research under a certainly given wind-wave actions, the fatigue damage of OWT structure is directly associated with the stochastic and complex environment correlation variables in real offshore site. This paper further develops the bi-directional PSTMD to mitigate the fatigue damage of OWT structure subjected to stochastic wind-wave actions. The mathematical models of OWT and bi-directional PSTMD are established to deduce the fatigue stress, and the aerodynamic and hydrodynamic loads from stochastic wind-wave actions. Some met-ocean data are used to study the stochastic probability distribution, and build the joint probabilistic density functions from various environmental factors. The Rain-flow Counting, S-N curve and Miner’s rule algorithms are used to comparatively explore the fatigue damage mitigation of bi-directional PSTMD under the stochastic wind-wave actions. The results show that this bi-directional PSTMD can mitigate the fatigue damage over 53.73% and 54.95% compared with the conventional pendulum TMD (PTMD) at the tower-base and seabed cross sections respectively.

Storm Franz: Societal and energy impacts in northwest Europe on 11–12 January 2007

Abstract. January 2007 was a bad storm month for much of central and northern Europe with a series of extratropical cyclones bringing high winds and precipitation to highly populated areas between Ireland and Russia. Although Storm Kyrill on 18–19 January 2007 was the most serious for its infrastructure damage and insurance costs, Storm Franz from the preceding week on 11–12 January 2007 was actually more serious for its maritime impacts in western Europe. This contribution takes a closer look at Storm Franz with an overview of its wind field and its impact on energy infrastructure, transportation networks and building damage. Maritime casualties are reviewed with respect to met-ocean conditions. The storm was notable for a series of wave-related accidents off southeast Ireland, the English Channel, and German Bight. An analysis is carried out on water level recorders around the North Sea to assess the storm surge and short period oscillations that may reveal harbour seiches or meteotsunamis. The results are compared with wave recorders, which had a fairly good coverage across the North Sea in 2007. The issue of wave damage to offshore infrastructure was highlighted in events associated with Storm Britta on 31 October–1 November 2006. Offshore wind energy in northwest Europe was in a growth phase during this time, and there were questions about the extreme met-ocean conditions that could be expected in the 20 year lifetime of an offshore wind turbine.

Data Informed Model Test Design With Machine Learning – an Example in Nonlinear Wave Load on a Vertical Cylinder

Abstract Model testing is common in coastal and offshore engineering. The design of such model tests is important such that the maximal information of the underlying physics can be extrapolated with a limited amount of test cases. The optimal design of experiments also requires considering the previous similar experimental results and the typical sea-states of the ocean environments. In this study, we develop a model test design strategy based on Bayesian sampling for a classic problem in ocean engineering -- nonlinear wave loading on a vertical cylinder. The new experimental design strategy is achieved through a GP-based surrogate model, which considers the previous experimental data as the prior information. The metocean data are further incorporated into the experimental design through a modified acquisition function. We perform a new experiment, which is mainly designed by data-driven methods including several critical parameters such as the size of the cylinder and all the wave conditions. We examine the performance of such a method when compared to traditional experimental design based on manual decisions. This method is a step forward to a more systematic way of approaching test designs with marginally better performance in capturing the higher-order force coefficients. The current surrogate model also made several ‘interpretable’ decisions which can be explained with physical insights.

What future for marine renewable energy in Portugal and Spain up to 2030? Forecasting plausible scenarios using general morphological analysis and clustering techniques

Marine renewables – which include mainly wave, tidal and current energy – have been hailed, for the past decades, as a potential solution to support the decarbonization of the society. Portugal and Spain have been traditionally avid for the testing and demonstration of such technologies, but the implementation of marine capacity is yet marginal, and there are many uncertainties regarding the future of the sector in the region. The main objective of this article is to show a future projection of marine renewable energies in both Iberian and Macaronesian regions for 2030 to research and technological development communities. To obtain this future projection, General Morphological Analysis and advanced clustering techniques have been used. The results are divided into five groups of potential scenarios, which vary significantly due to different political, social and technological parameters. The influence of variables such as innovation speed, infrastructure implementation, and comprehensive metocean data availability emerges as pivotal determinants shaping the sector's course. The knowledge from this systematization is expected to be used by researchers, technicians, governments or by any other agency involved in marine renewable energies in Spain and Portugal, as a guidance for their new projects and research lines.

Bias correction techniques for uncertainty reduction of long-term metocean data for ocean renewable energy systems

The design of effective and reliable solutions for Offshore Renewable Energy (ORE) technologies greatly relies on accurate metocean data. Uncertainties can significantly impact the design process. This paper presents a thorough evaluation of different bias-correction (BC) techniques applied to re-analysis datasets from diverse locations along the Spanish coast, presenting a multi-level evaluation procedure including novel, more appropriate statistical metrics beyond the traditional assessment techniques. First, the quality of raw ERA5 datasets is demonstrated to be rather poor, especially under extreme events, confirming the need for BC. Once the need is identified, results show that only the most sophisticated distribution-mapping BC techniques present the capacity to significantly improve the quality of the datasets, with the linearly-spaced Quantile-mapping (QM) showing the best overall performance for power production conditions (PP), reducing the bias by over an order of magnitude. In contrast, the Gumble Quantile-Mapping (GQM) outperforms the QM in survivability conditions (Surv). However, when computing overall performance of BC techniques, the predominance of PP hinders the relevance of Surv. Hence, adapted metrics with a more realistic balance between the PP and Surv regions are suggested, which show a better suitability of GQM providing more accurate estimations of the average power density and variability.

A study of the towing characteristics of a semi-submersible floating offshore wind platform

A robust pipeline of floating wind energy has emerged with a general trend of projects becoming larger, further from shore, and placed in increasingly energetic seas. The installation process for these farms involves the pre-assembly of components onshore or in sheltered waters before towing the platform to the operational location using tugs. It can be expected that such marine operations will be repeated in reverse at the time of decommissioning. The cost and safety of these operations will be influenced by the tugs used, towing speed, the local metocean conditions, the platform/turbine characteristics and other factors. This paper investigates the hydrodynamic characteristics of a large semi-submersible floating offshore wind turbine (FOWT) under tow. The motions of the FOWT are analysed using a numerical tool and validated using a towing test. A framework is proposed for the assessment of FOWT towing operations. Various limiting factors have been identified and the hydrodynamic performance of the system has been evaluated using the framework.

Overview of the potential of floating wind in Europe based on met-ocean data derived from the ERA5-dataset

This paper provides an overview of the potential of floating wind in Europe. Wind and wave data from the ERA5 dataset as well as bathymetry data are analysed to identify regions suitable for floating wind deployment. The most promising areas are quantified according to different characteristics (wind resource, bathymetry, distance to the coast). The wind resource is quantified in terms of averaged wind velocity and wind availability at 100 m height above sea level. The results show the great potential of Ireland, Iceland, Norway and the United Kingdom. Indeed, a large part of the seas of these countries is located in water depths between 60 and 1000 meters and has a very high wind resource despite stronger wave conditions than in other countries. In addition, Spain, France, Sweden, Finland and Greece all exhibit good potential.

Monitoring sediment transport pathways from an artificial nearshore berm, South Padre Island, Texas, USA, August 2018 to November 2019—Implications for coastal management

During August 2018 – November 2019, the transport pathways of dredge material from a specially constructed nearshore feeder berm were investigated as part of a collaborative study by the City of South Padre Island, U.S. Army Corps of Engineers–Galveston District, U.S. Geological Survey, Partrac GeoMarine Inc., and Texas A&M University, into the efficacy of beneficial use dredge material (BUDM) as a method of replenishing the beach profile and shoreface at South Padre Island, Texas with sediment. Dual-signature (fluorescent and ferrimagnetic) tracer particles, designed to be hydraulically equivalent to the dredge material, were placed on the berm, and a sampling program was initiated to monitor the spatiotemporal movement of tracer particles under the influence of the prevailing hydrodynamic regime. Wave and current data were collected and were used together with available metocean data to identify the forcing mechanisms of sediment transport; improved understanding garnered from the consideration of multiple datasets can be used to inform future coastal management decisions.Tracer analysis results indicated low magnitude, shoreward transport of dredge material from the berm and along shore transport in both northerly and southerly directions. Small amounts of tracer detected in beach face samples demonstrated connectivity between the constructed feeder berm and the shoreface. However, the generally low tracer concentration within beach face samples indicated low rates of sediment transport for berm sediments, and a low magnitude sediment transport pathway from the berm directly to the beach face, with possible storage of sediment in the nearshore bar system.Given that the berm was at or beyond the closure depth and considering the relatively weak prevailing near bottom hydrodynamic conditions, the potential for sediment to be mobilized and transported during the study period was generally low. Periods of higher energy waves, driven by north-northwest winds, were identified as a key driver of sediment transport but due to the infrequent nature of these events the sediment transport regime across the area of interest was temporally limited. Longshore movement of sediment was both north and south, mainly dependent on prevailing wind directions and resulting longshore currents.We discuss implications for coastal zone management and the use of feeder berms as an artificial beach nourishment mechanism to replenish sediments lost by coastal erosional processes. The insight into nearshore emplacement of material can be used by local governments and coastal managers to optimize the efficacy of berm emplacement and implement such schemes as a cost-effective nourishment option, or when onshore placement of material is not feasible.

Enhancing the accuracy of metocean hindcasts with machine learning models

To improve the quality of wind and wave forecasts post-processing algorithms are developed to bias-correct the variables of interest using machine learning models trained with buoy data. Two types of deep neural networks are developed and studied to improve the accuracy of ERA5 hourly data of 10-m wind speed (wsp) and significant wave height (swh) at two locations with distinct metocean conditions. The algorithms are the multilayer perceptron (MLP) and the long short-term memory (LSTM) neural networks. The latter is built based on a range of previous time steps included in the input, evaluating the optimal value. Several metrics such as bias, scatter index, root mean square error and correlation coefficient are used to evaluate model performances. After extensive examination and experimentation regarding feature selection, filtering windows, and model architecture, the results show better improvements for swh than wsp, and that LSTM outperforms MLP significantly. The original correlation coefficients between ERA5 and observations for swh have been improved and the ERA5 RMSE has been reduced with the LSTM post-processing model.

Grid value of co-located offshore renewable energy

The paper investigates the added grid value of co-locating wave energy converters with offshore wind turbine generators and floating photovoltaic solar panels. Grid value is evaluated in terms of power generation variability and electrical infrastructure utilization. Using meteorological reanalysis data for the region of the North Sea, wave, solar and wind energy converters are modelled on an hourly basis to form the power profile of the joint operation. Necessary power ratings of the transmission system are provided both for stand-alone installations and the hybrid parks and is shown to be of higher utilization for the latter case. Suitable locations of hybrid parks are also analyzed and provided in terms of water depth, solar irradiation and favorable wave and wind climate. The joint operation is found to smoothen the power profile on all time scales but influences long term variations most heavily. Added grid value strongly depends on the park configuration as well as metocean conditions. It is shown that by co-locating wave, solar and wind energy converters the utilization of the electrical transmission system is increased compared to stand-alone installations.

On the Definition of a Comprehensive Technology-Informed Accessibility Metric for Offshore Renewable Energy Site Selection

Despite the important role of offshore renewable energies (OREs) in the energy transition, the economical viability is still unclear. Therefore, an appropriate site selection is crucial. Besides the energy potential, the impact of operation and maintenance (O&M) aspects on the location can be critical. Accessibility is one of the most relevant aspects for O&M, assessing the feasibility to access the ORE farm. However, traditional accessibility assessment metrics do not allow a comprehensive evaluation. Therefore, the present paper suggests a novel, technology-informed metric, incorporating the overall set of most critical aspects, i.e., metocean conditions, visibility due to sunlight and sea fog, system failures, and O&M logistics. Among the different aspects, limited visibility is shown to be highly relevant with a reduction of up to 60% in accessibility. The study assesses accessibility in five different locations across Europe. On the one hand, accessibility is shown to be less sensitive to long-term resource variations with a reduction of 5% in the last six decades. On the other hand, accessibility is overall shown to be inversely proportional to the energy potential, meaning that as the energy potential increases, accessibility is reduced, increasing downtime, reducing the energy generation and increasing the cost of energy. As a consequence, site selection should combine the energy potential and accessibility assessments, which is enabled by the technology-informed metric presented here.

Global sensitivity analysis of a semi-submersible floating wind turbine using a neural network fitting method

The stochastic platform motions of a floating offshore wind turbine (FOWT) significantly affect its load characteristics and power production. It is vital to understand the influence of various input parameters on the global performance of an FOWT to ensure structural safety and power efficiency. This study investigates the influence of key design parameters related to metocean conditions and natural periods of platform motions on wind turbine performance. A global sensitivity analysis (GSA) framework is proposed to map the input-output relation of a 4-MW semi-submersible FOWT. This framework uses fully-coupled numerical analysis and neural network fitting (NNF). Several important parameters are selected, including natural periods of the platform motions, mean wind speed, turbulence intensity, wave spectral peak period and significant wave height. Results demonstrate that natural periods of platform motions and wave conditions have a significant influence on the dynamic responses of the FOWT. Furthermore, statistical analysis reveals that severe sea states and low natural periods of platform motions can increase the operation cycles of the blade pitch system by 20%. The use of NNF method effectively increases the efficiency of the GSA by at least forty times. The proposed framework can be applied to other areas of FOWTs.

Variance-based sensitivity analysis of oil spill predictions in the Red Sea region

To support accidental spill rapid response efforts, oil spill simulations may generally need to account for uncertainties concerning the nature and properties of the spill, which compound those inherent in model parameterizations. A full detailed account of these sources of uncertainty would however require prohibitive resources needed to sample a large dimensional space. In this work, a variance-based sensitivity analysis is conducted to explore the possibility of restricting a priori the set of uncertain parameters, at least in the context of realistic simulations of oil spills in the Red Sea region spanning a two-week period following the oil release. The evolution of the spill is described using the simulation capabilities of Modelo Hidrodinâmico, driven by high-resolution metocean fields of the Red Sea (RS) was adopted to simulate accidental oil spills in the RS. Eight spill scenarios are considered in the analysis, which are carefully selected to account for the diversity of metocean conditions in the region. Polynomial chaos expansions are employed to propagate parametric uncertainties and efficiently estimate variance-based sensitivities. Attention is focused on integral quantities characterizing the transport, deformation, evaporation and dispersion of the spill. The analysis indicates that variability in these quantities may be suitably captured by restricting the set of uncertain inputs parameters, namely the wind coefficient, interfacial tension, API gravity, and viscosity. Thus, forecast variability and confidence intervals may be reasonably estimated in the corresponding four-dimensional input space.

Deriving spatial wave data from a network of buoys and ships

The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.