Applied Ocean Research最新文献

{"title":"Flume test on mechanical responses of wind-wave cyclic loaded offshore wind turbine supported by jacket foundation considering time-varied local scour effects","authors":"Hao Zhang ,&nbsp;Hanbo Zheng ,&nbsp;Fayun Liang ,&nbsp;Lin Li","doi":"10.1016/j.apor.2025.104565","DOIUrl":"10.1016/j.apor.2025.104565","url":null,"abstract":"<div><div>Large diameter pile (LDP) jacket foundations have recently seen increased application in offshore wind energy engineering due to their ability to avoid the complexities of rock-socketed pile construction. Offshore wind turbines (OWTs) supported by LDP-jacket foundations are subjected to long-term wind-wave loading and local scour caused by ocean currents after installation, posing challenges to their structural service performance. This paper presents an experimental study by using two compact cyclic loading devices to simulate long-term wind and wave cyclic loading with different characteristics at various heights. Additionally, stable unidirectional flow is generated in the test area by the flume to achieve the combined effects of long-term cyclic loading and time-varying scour processes. The experimental study investigates the mechanical responses of the LDP-jacket foundation supported OWT system under these combined effects, including the cumulative deformation of the structure, the ultimate bearing capacity, and the migration of the natural frequency of the OWT system. Comparisons are made with scenarios considering only solely environmental factors. The results of this study can be used to predict the service performance evolution of newly installed LDP-jacket supported OWTs more accurately.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104565"},"PeriodicalIF":4.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regional frequency analysis of extreme significant wave heights with long return periods based on complete distribution characteristics","authors":"Jiaqian Li ,&nbsp;Zhuxiao Shao ,&nbsp;Bingchen Liang ,&nbsp;Shengtao Du ,&nbsp;Huijun Gao","doi":"10.1016/j.apor.2025.104566","DOIUrl":"10.1016/j.apor.2025.104566","url":null,"abstract":"<div><div>Reliable assessment of extreme significant wave heights is crucial to the design and operation of ocean structures. However, due to the limited duration of available wave data, the uncertainty of return significant wave heights based on at-site extreme wave analysis may be large, especially for long return periods. In this study, a clustering method for identifying homogeneous regions is proposed, which directly diagnoses the frequency distribution of extreme samples to fully detect the distribution characteristics, rather than using statistical parameters of extreme samples to partially characterize this distribution. By standardizing extreme samples, measuring distribution differences, and iteratively calculating clustering centers, three homogeneous regions are identified in the study region. The diagnostic results of goodness-of-fit test show that the generalized extreme value distribution generally performs well in these regions. In each homogeneous region, the regional quantile is constructed based on all extreme samples within this region, and the site-dependent scale factor is used to extrapolate the quantile for each site. Compared with the traditional clustering method, the fitting performance of the model quantile based on the proposed method is generally improved, especially near the boundary of the homogeneous region. Compared with the at-site extreme wave analysis, the uncertainty of the return significant wave height extrapolated by the regional frequency analysis is generally reduced, especially for long return periods. For example, the width of the 95 % confidence interval for the 200-year return level is reduced by approximately 2 times at all study sites. In the homogeneous region, the distribution characteristics of extreme samples are similar due to the influence of some factors, such as driving weather. Sample information from all sites in this region can be used to describe the common distribution characteristics to construct a stable regional quantile, which is essential for extrapolation with long return periods.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104566"},"PeriodicalIF":4.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"End-to-end underwater acoustic target classification with frequency separation strategy","authors":"Ning Tang ,&nbsp;Hao Zhang ,&nbsp;Fei Zhou ,&nbsp;Wei Huang ,&nbsp;Pengfei Wu","doi":"10.1016/j.apor.2025.104554","DOIUrl":"10.1016/j.apor.2025.104554","url":null,"abstract":"<div><div>Most underwater target classification methods require data preprocessing to convert waveforms into spectrograms. Recent advancements have led to the emergence of waveform-based approaches that directly extract discriminative features from raw acoustic signals, eliminating the need for preprocessing. However, waveform-based architectures often exhibit inferior classification performance compared to spectrogram-based methods. This study identifies two key limitations of existing waveform-based approaches: (1) the lack of explicit frequency feature extraction mechanisms, and (2) information degradation due to downsampling. To address these challenges, we propose an end-to-end frequency separation network (FSNet), which includes two innovative components: a frequency separation (FS) module that explicitly captures discriminative frequency characteristics, and a scale-adaptive max pooling (SAMP) layer that preserves critical information during dimensionality reduction. Comprehensive evaluations on ShipsEar and DeepShip datasets demonstrate that our framework achieves competitive accuracy performance (82.91% on ShipsEar and 78.39% on DeepShip) while maintaining exceptional computational efficiency, requiring only 0.49M parameters—over three times fewer than the second-smallest model.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104554"},"PeriodicalIF":4.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental investigation of the asymmetric oscillation mechanism of an oscillating water column wave energy converter","authors":"Yuan Lin,&nbsp;Jiapeng Pan,&nbsp;Maoxing Wei,&nbsp;Fang He","doi":"10.1016/j.apor.2025.104557","DOIUrl":"10.1016/j.apor.2025.104557","url":null,"abstract":"<div><div>While numerous studies focus on the energy extraction rate of oscillating water column (OWC) converters, many assume symmetric water column oscillations within the flow field, despite observed discrepancies in real-world conditions. This study employs particle image velocimetry (PIV) to analyze flow field asymmetry under various regular wave conditions, with wave height being a key variable. Experimental results reveal significant differences in velocity distributions and flow patterns. At higher wave heights, a high-velocity region near the front wall of the OWC converter emerges as the primary outflow pathway, while the inflow largely adheres to the wave-induced flow velocity. These flow pattern variations closely correlate with the vortex evolution region. Further analysis uncovers asymmetry in vortex generation: the seaward vortex originates from a stable shear layer, whereas the leeward vortex forms through multiple flow separations and turbulent mixing. These vortices shape the asymmetric flow pattern, with the leeward vortex driving outflow near the front wall and the seaward vortex creating a high-velocity “curtain” that impedes water inflow. The inherent asymmetry in the flow field, minimal under weaker hydrodynamic conditions, becomes more pronounced at higher wave heights due to intensified vortex influences. Additionally, a symbiotic interaction between the seaward and leeward vortices is observed, where each influences the other's formation,This interplay ultimately impacts the overall flow dynamics and energy extraction efficiency.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104557"},"PeriodicalIF":4.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LiDAR target detection and classification for ship situational awareness: A hybrid learning approach","authors":"Filippo Ponzini,&nbsp;Raphael Zaccone,&nbsp;Michele Martelli","doi":"10.1016/j.apor.2025.104552","DOIUrl":"10.1016/j.apor.2025.104552","url":null,"abstract":"<div><div>In recent years, LiDARs have been used to enhance situational awareness of autonomous vehicles, including in the marine domain, driven by the need for reliable detections in Marine Autonomous Surface Ships and Unmanned Surface Vehicles. Detecting obstacles and targets within point clouds is generally handled by a fully unsupervised learning framework. While effective and simple, this approach cannot classify targets. This paper presents a combined unsupervised/supervised approach for detecting and classifying marine targets and obstacles. The unsupervised detection framework is maintained by incorporating a lightweight supervised module capable of classifying detection outputs without disrupting the workflow. Rather than training on the entire point cloud, the proposed method focuses on selected target features, reducing model size and information exchange. Specifically, a Random Forest Classifier is trained on features extracted from the point-cloud dataset. The acquisition of an ad-hoc training dataset and its statistical analysis are presented to identify key features. The selection, training, and validation processes are outlined. Finally, the supervised model is integrated into a state-of-the-art unsupervised LiDAR detection pipeline and tested in a real scenario. The results demonstrate the hybrid framework’s effectiveness and compliance with real-time constraints.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104552"},"PeriodicalIF":4.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of cost elements on optimum layout of an offshore wind farm","authors":"Pegah Ziyaei,&nbsp;Mahdi Khorasanchi","doi":"10.1016/j.apor.2025.104537","DOIUrl":"10.1016/j.apor.2025.104537","url":null,"abstract":"<div><div>The main goal in wind farm layout optimization is to minimize the cost of energy (COE). This can be achieved by reducing the costs associated with the wind farm and/or increasing the power output, as both factors directly impact the COE. In this study, we focus on minimizing the levelized cost of energy (LCOE) by comprehensively considering all costs throughout the lifetime of a wind farm. To investigate the impact of the LCOE on optimization process, we examine a non-homogeneous wind farm. Consequently, we let the optimization process choose between two distinct categories of commercially available turbine sizes. The proposed cost model includes expenses associated with the key components involved in design, construction, and operation and maintenance processes throughout the wind farm's lifespan. Considering the change in wind speed, we first study two cases of identical turbines and a combination of different sizes of turbines by single and multi-objective optimization processes to minimize LCOE. While varying turbine sizes contribute to an increase in power production of the wind farm, the significant rise in elements of cost makes it impractical from a developer's perspective. Finally, we investigate the significance of considering different elements of cost in the objective function and emphasize the importance and superiority of the LCOE over the traditional COE.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104537"},"PeriodicalIF":4.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven 6-hour ahead nowcasting of sea-surface currents using HF Radar","authors":"Mattia Cavaiola ,&nbsp;Simone Marini ,&nbsp;Marcello G. Magaldi ,&nbsp;Andrea Mazzino","doi":"10.1016/j.apor.2025.104542","DOIUrl":"10.1016/j.apor.2025.104542","url":null,"abstract":"<div><div>This study introduces novel AI-driven models, Bidirectional Encoding-Forecasting (BiEF) and Variational Bidirectional Encoding-Forecasting (VBiEF), for nowcasting sea-surface currents using High Frequency (HF) Radar data. These models leverage advanced deep learning techniques to predict the dynamics of sea currents with accuracy and temporal resolution. Our research demonstrates that these AI models significantly outperform traditional persistence-based methods, providing skillful forecasts up to six hours ahead. While the VBiEF model, in particular, showcases good skill in capturing both the spatial and temporal complexities of sea currents, as well as in reconstructing intricate oceanographic features such as vorticity, divergence fields, and the rate of deformation tensor, several challenges remain to be addressed to further increase predictability levels. Furthermore, the good performance of these models in areas beyond their training domain suggests their adaptability and scalability for global ocean studies, opening new avenues for future research and application, highlighting the potential of AI in marine science.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104542"},"PeriodicalIF":4.3,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of particle crushing on the mechanical behavior of unreinforced and geogrid-reinforced marine coral sands","authors":"Zhaogang Luo ,&nbsp;Qiang Ou ,&nbsp;Xuanming Ding ,&nbsp;Ting Zhang ,&nbsp;Jinqiao Zhao","doi":"10.1016/j.apor.2025.104555","DOIUrl":"10.1016/j.apor.2025.104555","url":null,"abstract":"<div><div>Strength deterioration induced by particle crushing is detrimental to the construction of marine geo-structures. As an efficient reinforcement method, the geogrid-reinforced coral sand (GRCS) technique also suffers from the adverse effects of particle crushing. In this study, the macro-micro mechanical behavior of unreinforced and geogrid-reinforced coral sands under triaxial loading was investigated using an experimentally validated three-dimensional discrete element method (DEM), focusing on the effect of particle crushing. The results reveal that crushability determines the stress-strain response, strength parameters, spatial distribution of crushing, and number of particle fragments differently. The analysis of microscopic contact forces indicates that the resistance pattern of GRCS under triaxial loading results in the gap in spatial distribution patterns and the number of fragments compared with the unreinforced condition. Further micro-scale analysis of the crushing behavior shows that the increased fragments induce a transition of the particle morphology from angular to round-like patterns, thus the low occlusion and increased plasticity of crushed coral sands contribute to the stress-strain softening and peak strength reduction. In conjunction with the effect of crushing on shear strength, the strength envelope characteristics and the evolution mechanism of shear mechanical parameters are revealed under the effects of reinforcement, low crushing, and high crushing.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104555"},"PeriodicalIF":4.3,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calibration of medium-range metocean forecasts for the North Sea","authors":"Conor Murphy ,&nbsp;Ross Towe ,&nbsp;Philip Jonathan","doi":"10.1016/j.apor.2025.104538","DOIUrl":"10.1016/j.apor.2025.104538","url":null,"abstract":"<div><div>We assess the value of calibrating forecast models for significant wave height <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>S</mi></mrow></msub></math></span>, wind speed <span><math><mi>W</mi></math></span> and mean spectral wave period <span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>m</mi></mrow></msub></math></span> for forecast horizons between zero and 168 h from a commercial forecast provider, to improve forecast performance for a location in the central North Sea. We consider two straightforward calibration models, linear regression (LR) and non-homogeneous Gaussian regression (NHGR), incorporating deterministic, control and ensemble mean forecast covariates. We show that relatively simple calibration models (with at most three covariates) provide good calibration and that addition of further covariates cannot be justified. Optimal calibration models (for the forecast mean of a physical quantity) always make use of the deterministic forecast and ensemble mean forecast for the same quantity, together with a covariate associated with a different physical quantity. The selection of optimal covariates is performed independently per forecast horizon, and the set of optimal covariates shows a large degree of consistency across forecast horizons. As a result, it is possible to specify a consistent model to calibrate a given physical quantity, incorporating a common set of three covariates for all horizons. For NHGR models of a given physical quantity, the ensemble forecast standard deviation for that quantity is skilful in predicting forecast error standard deviation, strikingly so for <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>S</mi></mrow></msub></math></span>. We show that the consistent LR and NHGR calibration models facilitate reduction in forecast bias to near zero for all of <span><math><msub><mrow><mi>H</mi></mrow><mrow><mi>S</mi></mrow></msub></math></span>, <span><math><mi>W</mi></math></span> and <span><math><msub><mrow><mi>T</mi></mrow><mrow><mi>m</mi></mrow></msub></math></span>, and that there is little difference between LR and NHGR calibration for the mean. Both LR and NHGR models facilitate reduction in forecast error standard deviation relative to naive adoption of the (uncalibrated) deterministic forecast, with NHGR providing somewhat better performance. Distributions of standardised residuals from NHGR are generally more similar to a standard Gaussian than those from LR.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104538"},"PeriodicalIF":4.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Observed changes in significant wave heights derived from long-term homogenized measurements offshore mainland Portugal","authors":"Rita Esteves ,&nbsp;Diogo Mendes ,&nbsp;Maria Graça Neves ,&nbsp;Tiago Oliveira ,&nbsp;José Paulo Pinto","doi":"10.1016/j.apor.2025.104546","DOIUrl":"10.1016/j.apor.2025.104546","url":null,"abstract":"<div><div>Trends in wind-wave climates across the globe have been primarily addressed using numerical models. Availability of long-term data collected by wave buoys is often scarce and they present inhomogeneities associated with wave buoy size and hardware over time. Here, a trend analysis was conducted on approximately 40 years of homogeneous wind-wave data collected by wave buoys offshore mainland Portugal. For that, a homogenization methodology based on RHTestsV4, with ERA5 wave hindcast as reference time series was used. Results indicate that along the north-western coastline facing the North Atlantic, an increasing trend of monthly mean significant wave height of +10 mm/yr was observed at FigLei record for the months between October and December. Along the south-western coastline, no statistically significant trends were observed. Along the southern coastline, which is also exposed to wind-waves generated in the Mediterranean Sea results at Faro record show a decreasing trend of monthly 90th percentile of -22.2 mm/yr between October and December. A further comparison between the wind-wave trends obtained with local wave buoys and those from the global ERA5 wave hindcast highlights that the trends of the later can be opposite, or they can vary by up to a factor of 10 which emphasizes the importance of long-term wave buoy observations networks for a more accurate understanding of local wave climates.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"158 ","pages":"Article 104546"},"PeriodicalIF":4.3,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}