Wave Conditions Research Articles

Controls on the morphological evolution of embayed beaches: Morphometry versus external forcing

AbstractThe morphological evolution of embayed beaches on a microtidal coast is assumed to largely respond to the degree of exposure to wave conditions, decreasing the mobility with increasing beach indentation (and vice versa). However, the number of sediment arrivals at the beach or the impact of extreme storms can modify this relationship. Here, we present an analysis of 10 embayed beaches along the Catalan coast with different morphometric and sedimentary characteristics to identify the most relevant parameters controlling the morphological evolution of these embayed beaches at the inter‐annual and decadal scales. The study was mostly based on LiDAR topographic data collected from 2012 to 2017, aerial photographs from 1945 to 2021, sediment sampling and a long‐term series analysis of the forcing parameters (waves, sea level, precipitation and land‐use changes). The results show a net loss of volume on all the studied beaches at an inter‐annual scale and a general shoreline retreat during the last few decades, suggesting the influence of common processes on the evolution of the studied beaches. Smaller pocket beaches with medium‐to‐high indentations are more sensitive to changes induced by local factors and show higher variability in the volume of the emerged beach and shoreline position than larger beaches. The most relevant factors influencing the evolution of the studied beaches on a decadal scale were identified as changes in sea level and the reduction in sediment inputs provided by streams due to land‐use changes in the drainage basin. At the inter‐annual scale, the impact of extreme events is the main factor controlling beach behaviour. These general trends can be opposite locally for beaches that receive large amounts of sediment via longshore transport from adjacent beaches.

Experimental study of tendon failure analysis for a TLP floating offshore wind turbine

This paper describes an experimental study conducted on a multi-column tension leg platform (TLP) floating offshore wind turbine (FOWT). A prototype model of the TLP FOWT supporting the NREL 5-MW wind turbine with a scale ratio of 1:50 is tested under various wind and wave conditions at the State Key Laboratory of Coastal and Offshore Engineering at Dalian University of Technology, China. This work has particularly focused on the tendon failure and its impact on the dynamic response of the FOWT. Free decay tests, regular wave tests, wind-wave combined tests and tendon failure tests are conducted using different environmental parameters.The results suggest that natural periods, dynamic responses of the platform, and forces in the tendons satisfy the design requirements. The analysis indicates that the impact of tendon failure on the platform surge, heave and pitch responses are found to be insignificant. When one of the tendons is broken, the adjacent tendons experience a significant increase in tensile force; and, the maximum tensile force in the remaining tendon is found to increase by about 130%. The overstepping of the minimum breaking load as recommended by the design standard DNV GL is not reached, and this indicates that the safety of the system is ensured even during the harshest failure condition.

Synoptic dynamics of cold waves over north India: Underlying mechanisms of distinct cold wave conditions

The cold air outbreaks in the northern parts of India exert significant impacts on human health, energy, agriculture and transportation. In this study, we investigate the synoptic dynamics of cold waves and their linkages to large scale circulations for the winter period from 1982 to2020. Cold waves are classified into normal and intense (NCWs and ICWs) based on intensity and we examine the underlying mechanisms of formation and their atmospheric drivers. Notably, the spatial extent of the ICWs is almost double than that of normal ones thereby having the potential to affect a wider population. The NCWs are often influenced by western disturbances, leading to the inflow of cold air from Siberia (a region of shallow high). In contrast, ICWs are mostly linked to the presence of an omega block over the Ural-Siberian region. The downstream portion of the Ural block favours the inflow of cold northerlies into north India, leading to cold air advection and extreme cold wave conditions. The influence of Arctic warming for ICWs is further confirmed through a prominent Quasi-Resonant Amplification (QRA) fingerprint. Furthermore, La Niña condition seems to play a crucial role in triggering ICWs over north India. During La Niña, the prominent low level cyclonic anomaly helps in advecting the cold air from the higher latitudes into the country. The frequency as well as the duration of cold wave events are also found to be higher in La Niña years compared to El Niño and neutral years. The trend analysis of cold wave events over north India reveals a significant decrease in the frequency, duration and intensity during the analysis period due to a combination of various factors such as rising winter minimum temperatures (due to global warming), decreasing number of synoptic winter weather systems and Arctic amplification.

Achieving optimum power extraction of wave energy converters through tunable mechanical components

Wave energy converters (WECs) play a significant role in harnessing the vast potential of marine renewable energy. To enhance the power extraction of WECs, it is necessary but challenging to design the parameters of the WECs for the time-varying wave conditions. This research explores the utilization of tunable mechanical components within both the power take-offs (PTO) and wave capturing structures. We first briefly review the tunable mechanisms and then conduct a theoretical analysis of their applications for maximizing power extraction under regular and irregular wave conditions. The study investigates the impact of PTO stiffness and inertance on power extraction through an analytical approach. The results reveal that both parameters exhibit a monotonic effect within specific frequency ranges, and showed the optimum power extraction can be achieved through the tunable mechanical components. Furthermore, the research highlights the importance of designing the frequency tuning limit for two-body WECs to coincide with the desired wave conditions, as it enables all PTO parameters to achieve an optimal power output. Based on the analysis, the paper gives insightful suggestions on the design of PTO and WEC systems, including mass ratio between the two bodies, mooring stiffness, and other relevant parameters. It is found that the tuning through the mechanical component can be significant for the single-body WEC yet less effective for the two-body WEC especially at irregular wave conditions.

A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions

Growth and expansion in construction has increased recently and especially in coastal areas. In Alexandria, Egypt, mega projects such as El-Max Port Project (Middle Port), Port of ABU QIR (EG AKI), hotels, and restaurants were spread along the coastal lines, thus, it will need a high electrical energy. Although, the great economic benefits of such projects, it will have some negative impacts, such as overloading on the present grid. According to recommendations of COP 27, Egypt is one of the countries targeting to increase the dependency on green energy to minimize the production of greenhouse gases. This study is interested in wave energy as a renewable source of energy. Using a machine learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa-Matruh), this study will try to estimate the future amount of wave energy along Egypt's coast. Hourly measurements of the significant height and the mean wave period for the period 1979–2023 have been utilized for this. An extractor for wave energy can also be built on the Overtopping Breakwater for Energy Conversion (OBREC) in order to use this energy to fill the hole in the electric grid. The machine learning model was developed using hourly wave height and period data from three buoys, and as a result, the results have a root mean square error (RMSE) of 0.52. The amount of energy taken, wave power, and system efficiency at each place were then fully determined using a mathematical model for each of the three locations. The area along the coast of Alamein had the highest energy extraction rates, followed by Alexandria and Mersa-Matruh in that order. The results of the mathematical model indicate that the yearly power generation for Alamein, Alexandria, and Mersa-Matruh is 25287 MWhr, 14713 MWhr, and 4865 MWhr, respectively.

Satellite derived mixing ratio profile, a resourceful indicator of heat waves: INSAT-3DR sounder based analysis

India, being a tropical country, frequently experiences a natural hazard called heat waves especially during the hot summer season. During the summer months of 2022, India experienced moderate to severe heat waves spanning March to the third week of May. This adverse weather situation severely affects the northwest and the central Indian regions, causing loss of human lives, crop damage, and a scarcity of water. The present study is the first attempt to assess the meteorological situations favoring heat waves using Indian National Satellite (INSAT)-3DR sounder data. Such studies using satellite based sounder data have seldom happened. The study is conducted during March-May 2022 using the mixing ratio data derived from the INSAT-3DR sounder at 500 hPa, 620 hPa, 700 hPa, 850 hPa, and 950 hPa pressure levels of the atmosphere. From the present study, it is observed that the INSAT-3DR derived mixing ratio profile gives clear signatures of an impending heat wave condition. The weather signal can be observed as an apparent dryness representing the low mixing ratio values (<6 g/kg) percolating from upper layers (600 hPa) to lower levels (850 hPa). These percolations (seeping) happen prior to the surface layers actually show the effects of the heat wave. The study verifies the prior studies of heat wave strike over northwest frontiers of Indian land mass and its further expansion to eastern and southern regions. During strong heat wave conditions surface temperatures reaching maximum of 46 ͦ C at northwest regions, 41 ͦ C at eastern regions and 40 ͦ C at southern regions. The corresponding mixing ratio values at 950 hPa diminishes to 2–4 g/kg for northwest regions and 4–6 g/kg at eastern and southern regions. The study also gives hints to locate signals of heat waves regarding their origin, possible expanding directions, and geographical extent from the satellite-derived mixing ratio. The observations from the analysis give scope for further enhancing the study in the future to devise efficient forecasting methodologies to predict heat wave conditions using satellite derived mixing ratio profiles.

Evaluating and enhancing the performance of the K-Means clustering algorithm for annual coastal bed evolution applications

The prediction of the coastal bed evolution at an annual scale utilizing process-based models is usually a complex task requiring significant computational resources. To compensate for this, accelerating techniques aiming at reducing the amount of input parameters are often employed. In the framework of this research, a comprehensive evaluation of the capacity of the widely-used K-Means clustering algorithm as a method to obtain representative wave conditions was undertaken. Various enhancements to the algorithm were examined in order to improve model results. The examined tests were implemented in the sandy coastline adjacent to the port of Rethymno, Greece, utilizing an annual dataset of wave characteristics using the model MIKE21 Coupled Model FM. Model performance evaluation was carried out for each test simulation by comparing results to a “brute force” one, containing the bed level changes induced from the annual time series of hourly changing offshore sea state wave characteristics, deeming the results very satisfactory. The best-performing configurations were found to be related to the implementation of a filtering methodology to eliminate low-energy sea states from the dataset. Employment of clustering algorithms utilizing “smart” configurations to improve their performance could become a valuable tool for engineers desiring to obtain an accurate representation of annual bed level evolution, while simultaneously reducing the required computational effort.

Evolution of the European offshore renewable energy resource under multiple climate change scenarios and forecasting horizons via CMIP6

The design of the different offshore renewable energy (ORE) technologies depends on the characteristics of wind/wave resources. However, these characteristics are not stationary under climate change. In this study the evolution of European offshore wave/wind resources is assessed under mid- and high-emissions scenarios based on the Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and three forecasting horizons including the near (2023–2032), mid (2041–2050) and long term (2091–2100). The novelties lie in: (i) the concurrent analysis of wind and wave resources, (ii) the use of data with a 3-hour temporal resolution, and (iii) the local-scale statistical analysis. Results show significant variations along the 21st century, with an overall decline in average wind and wave conditions. Importantly, and somewhat counter-intuitively, much of the Atlantic coast of continental Europe experiences increasing extremes in the high-emissions scenario. For the local-scale statistical analysis, the focus is on five wind farm sites, commissioned or in planning. In the high-emissions scenario, the 99th and 99.99th percentiles decrease in three of them and increase in the other two. The combination of decreasing averages and increasing extremes presents the worst-case scenario for ORE technology developers.

Image-Based Measurement Method of Overtopping Flow Velocity and Layer Thickness in Irregular Wave Conditions

Image-Based Measurement Method of Overtopping Flow Velocity and Layer Thickness in Irregular Wave Conditions

Study of a Center Pipe Oscillating Column Wave Energy Converter Combined with a Triboelectric Nanogenerator Device

Wave energy is one of the most widely distributed and abundant energies in the ocean, and its conversion technology has been broadly researched. In this paper, a structure that combines a traditional center pipe oscillating water column and a triboelectric nanogenerator is proposed. Firstly, the structural characteristics and geometric parameters of the device are designed. The working process of the device is introduced, the motion equation of the device is established, and the power generation principle of the triboelectric nanogenerator is deduced and analyzed theoretically. Secondly, hydrodynamic modeling and simulation are carried out, the influence of the bottom shape of the main floating body and the structural parameters of the sag plate on the hydrodynamic force of the device is analyzed, and an electric field simulation of the generation process of the friction nanogenerator is carried out. Finally, experiments involving the wave water tank of the proposed device are conducted, including charging the capacitor of the device under different wave conditions and directly lighting the LED lamp. The performance of the proposed device under different wave conditions is discussed. According to the test results, the feasibility of the proposed device for wave energy conversion is confirmed.

Coastal Data Information Program: advances in measuring and modeling wave activity, climate, and extremes

ABSTRACT The Coastal Data Information Program (CDIP) provides wave data to the public in near real-time, maintains an operational wave model for the California coast, and is engaged in ocean wave research on a global scale. CDIP’s array of moored wave buoy stations are instrumented with Datawell Waveriders. CDIP’s recent work to improve monitoring capabilities for directional surface wave spectra, surface current, and temperature is described. This includes data quality assurance and control, real-time alerts, telemetry and dissemination information technology infrastructure and methodology, data visualization tools, and mooring and instrumentation innovation. CDIP’s goal is to maximize data reliability, availability, accuracy, and precision. Validation of commonly used wave models – operational, hindcast, and forecast – with CDIP data is automated to provide assessments of relative skills at a variety of coastal locations and wave conditions. Wave heights measured by the buoys typically exceed modeled heights during the most energetic events, such as hurricanes, nor’easters, and bomb cyclones. In a practical validation example, the global wave model used to drive CDIP’s California coastal wave forecasts was recently changed, based on comparisons against buoy data. Forecast performance has improved for swell events, which are a challenge to model accurately in the Southern California Bight.

Hydrodynamic analysis of a heave-hinge wave energy converter combined with a floating breakwater

Research interest in breakwater design has increased recently due to the impetus to develop marine renewable energy systems, as breakwaters can be retrofitted to harness wave energy at the same time as attenuating it. This study investigates a novel system of attaching a hinge baffle under a floating breakwater. The floating breakwater itself acts as a heaving wave energy converter, and meanwhile the hinge rotation provides a second mechanism for wave energy harnessing. A computational model with multi-body dynamics was established to study this system, and a series of simulations were conducted in various wave conditions. Both wave attenuation performance and energy conversion ratio were studied, using an interdisciplinary approach considering both coastal engineering and renewable energy. In particular, the performance of the proposed system is compared with contemporary floating breakwater designs to demonstrate its advantage. Overall, a useful simulation framework with multi-body dynamics is presented and the simulation results provide valuable insights into the design of combined wave energy and breakwater systems.

Research on safety evaluation and weather routing optimization of ship based on roll dynamics and improved A* algorithm

With the steady growth of international trade, sea transport has become indispensable, leading to an increase in maritime accidents. The sinking of the Russian Black Sea Fleet flagship, the guided missile cruiser “Moskva,” underscores the critical need for enhanced maritime safety. However, the dynamics of ship stability, particularly under transverse wind and wave conditions, remain unclear. We aimed to explore ship navigation safety by using the Moskva incident as a case study. Specifically, our objective was to investigate the influence of wind and waves on ship stability, calculate the capsizing probability of the vessel, and devise a fast and safe return path to port. We conducted a comprehensive review of existing literature on ship stability and path-planning algorithms. Leveraging this knowledge, we established the dynamics equation under transverse wind and wave conditions and integrated risk and vulnerability indicators to obtain a quantitative sea-area risk zoning map. Additionally, we employed the A* algorithm to plan a path under International Maritime Organization rules restrictions. Our findings reveal insights into ship stability dynamics and the importance of considering environmental factors in navigation safety. We calculated the capsizing probability of Moskva and devised a fast and safe return path to port, highlighting the practical application of our methodology. This study contributes to the understanding of ship navigation safety by addressing the knowledge gap in ship stability dynamics. Our findings underscore the significance of considering environmental factors in navigation safety planning. However, limitations exist in terms of data availability and model accuracy. Future research should explore additional factors impacting ship stability and refine path-planning algorithms for enhanced safety.

The Roles of Bathymetry and Waves in Rip‐Channel Dynamics

AbstractThe behavior and predictability of rip currents (strong, wave‐driven offshore‐directed surfzone currents) have been studied for decades. However, few studies have examined the effects of rip channel morphology on the rip generation or have compared morphodynamic models with observations. Here, simulations conducted with the numerical morphodynamic model MIKE21 reproduce observed trends in flows and bathymetric evolution for two channels dredged across a nearshore sandbar and terrace on an ocean beach near Duck, NC, USA. Channel dimensions, wave conditions, and flows differed between the two cases. In one case, a strong rip current was driven by moderate height, near‐normally incident waves over an approximately 1‐m deep channel with relatively little bathymetric evolution. In the other case, no rip was generated by the large, near‐normally incident waves over the shallower (∼0.5 m) channel, and the channel migrated in the direction of the mean flow and eventually filled in. The model simulated the flow directions, the generation (or not) of rip currents, and the morphological evolution of the channels reasonably well. Model simulations were then conducted for different combinations of the two channel geometries and two wave conditions to examine the relative importance of the waves and morphology to the rip current evolution. The different bathymetries were the dominant factor controlling the flow, whereas both the initial morphology and wave conditions were important for channel evolution. In addition, channel dimensions affected the spatial distribution of rip current forcings and the relative importance of terms.

Autopilot System for Depth and Pitch Control in Underwater Vehicles: Navigating Near-Surface Waves and Disturbances

This paper introduces a framework for depth and pitch control of underwater vehicles in near-surface wave conditions. By effectively managing tail, sail plane angles and hover tank operations utilizing a Linear Quadratic Regulator controller and ℒ1 Adaptive Autopilot augmentation, the system ensures balanced control input distribution and significantly attenuates wave disturbances. This development in underwater vehicle control systems offers potential for improved functionality across a range of marine applications. The proposed framework is demonstrated to be robust in wave conditions, enabling more precise navigation and improved safety in operational scenario. The effectiveness of this control strategy is validated through extensive simulations using the Joubert BB2 model.

Modeling the trajectories of floating and non-floating microplastic particles in the water column

The distribution and movement of microparticles of plastic (MP) in freshwater and marine environments are determined by the characteristics of both MP and the surrounding flow. The most common plastic polymers in the aquatic environment, such as polyethylene and polypropylene, have a density lower than the density of water. Therefore, based only on buoyancy, it is expected that most MP will be present in the surface layers of the aquatic environment. In both freshwater and marine environments, turbulence-induced mixing depends on factors such as velocity gradient and convective flow. Consequently, the surface wind, the deep temperature gradient and the breaking of waves in the surf zone can lead to the formation of turbulence and deep vertical mixing. In such conditions, in addition to gravity and buoyancy, the motion caused by turbulent mixing can affect the vertical transport and distribution of particles. The paper presents the results of modeling Lagrangian trajectories for floating particles and non-floating particles, under wave conditions they correspond to regular waves, while the particle sizes range from 10 microns to 5 mm, and the density ranges from 0.88 to 2.80 g/cm3, which is within some of the most common densities of microplastic particles.

Optimizing wave energy converter benchmarking with a fuzzy-based decision-making approach.

The quest for sustainable energy solutions has intensified interest in marine renewables, particularly wave energy. This study addresses the crucial need for an objective assessment of Wave Energy Converter (WEC) technologies, which are instrumental in harnessing ocean waves for electricity generation. To benchmark WEC technologies, we employed an integrated approach combining the MEthod based on the Removal Effects of Criteria (MEREC) and the Spherical Fuzzy Combine Compromise Solution (SF-CoCoSo). MEREC provided a systematic way to determine the importance of various benchmarking criteria, while SF-CoCoSo facilitated the synthesis of complex decision-making data into a coherent evaluation score for each technology. The results of the study offer a definitive ranking of WEC technologies, with findings emphasizing the importance of grid connectivity and adaptability to various wave conditions as pivotal to the technologies' success. While the study makes significant strides in the evaluation of WECs, it also recognizes limitations, including the potential for evolving market dynamics to influence criteria weightings and the assumption that the MCDM methods capture all decision-making complexities. Future work should expand the evaluative criteria and explore additional MCDM methods to validate and refine the benchmarking process further.

Nonlinear Transient Switching Filter for Automatic Buffer Window Adjustment in Short-term Ship Response Prediction

Online ship response prediction is one of the emerging interests in seakeeping due to the extensive range of applications for autonomous control of marine vehicles. In particular, the short-term prediction and online updates of ship response have received special attention. Despite a body of studies on different predictors, the asymptotic properties of estimators given the time series sample size have not been addressed specifically, so the predictors only have been analyzed for a fixed observation window regardless of the intrinsic statistical characteristics in the time series. To this end, the current research has explored the performance of two nonlinear and linear based regressors: support vector regression (SVR) and the adaptive Seasonal Auto-Regressive and Integrated Moving Average (SARIMA) model on the time series data obtained from a simulated semisubmersible platform in different sea states. The experiment demonstrated that the prediction accuracy depends significantly on the observation window length statistical properties with respect to the ship responses in various wave conditions. Therefore, an innovative filter using the weighted average of a proposed function has been introduced to adaptively adjust the buffer window based on the signal statistical characteristics. The encouraging outcomes underscore that the introduced filter not only holds the potential to enhance various predictors employed in autonomous onboard decision-making, guidance, and stabilization systems for both existing and forthcoming intelligent onboard control systems but also offers an avenue to bolster the autonomy of dynamic systems. More precisely, the proposed mechanism could extend its applicability to enhance cognitive systems and enable deterministic decision-making in diverse fields beyond the scope of present contribution.

Incident component extraction from disturbed waves around large fixed cylindrical structures

The parameters of incident waves are critical for real-time wave load estimation of structures in service. Nonetheless, it is challenging to characterize incident waves accurately using the measured wave surface elevation around large fixed cylindrical structures due to the interaction with the structure in the wave field. To provide a better understanding of incident waves, which are usually buried in directly measured waves, a new time-domain method for the extraction of first-order and second-order incident waves around large fixed cylindrical structures is proposed. In contrast to most existing separation methods that are suitable for structures with equal reflection coefficients, the amplitude and phase changes of near-field waves around cylindrical structures can be determined by considering the significant diffraction effect, and then the time-frequency characteristic of the wavelet transform is employed, which enables the extraction of incident waves in the time domain. The accuracy of the proposed method is studied using several examples with known incident waves which are generated with the OpenFOAM. The numerical results show that the deviations between the exact and extracted incident waves change from 6.16% to 16.77% for different wave conditions. To further investigate the performance of the proposed method, an experimental study on waves around a mono-pile offshore wind turbine (OWT) is conducted in the laboratory of the Ocean University of China. The predicted results basically agree well with the target waves in terms of amplitude and phase. The deviations between predicted waves using the proposed method and target waves are 110% smaller than those between directly experimental measured waves and target waves for all tested conditions. Finally, 48 h of measured wave data were obtained during calm and typhoon periods around a mono-pile OWT located near Rudong County, Jiangsu Province, in the Yellow Sea of China. There are almost 150% and 30% differences between the extraction results and measured data in the time series and statistical wave heights, respectively, which means that employing disturbed wave data as the input for calculating real-time wave loads leads to deviations that cannot be ignored.

Object Manipulation in Marine Environments using Reinforcement Learning

Performing intervention tasks in the maritime domain is crucial for safety and operational efficiency. The unpredictable and dynamic marine environment makes the intervention tasks such as object manipulation extremely challenging. This study proposes a robust solution for object manipulation from a dock in the presence of disturbances caused by sea waves. To tackle this challenging problem, we apply a deep reinforcement learning (DRL) based algorithm called Soft. Actor-Critic (SAC). SAC employs an actor-critic framework; the actors learn a policy that minimizes an objective function while the critic evaluates the learned policy and provides feedback to guide the actor-learning process. We trained the agent using the PyBullet dynamic simulator and tested it in a realistic simulation environment called MBZIRC maritime simulator. This simulator allows the simulation of different wave conditions according to the World Meteorological Organization (WMO) sea state code. Simulation results demonstrate a high success rate in retrieving the objects from the dock. The trained agent achieved an 80 percent success rate when applied in the simulation environment in the presence of waves characterized by sea state 2, according to the WMO sea state code.