Applied Ocean Research最新文献_第9页

Effects of the inlet gas volume fraction on the structural dynamic characteristics of gas‒liquid multiphase pumps

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104347

Xin Wu, Guojun Zhu, Jianjun Feng, Xingqi Luo

{"title":"Effects of the inlet gas volume fraction on the structural dynamic characteristics of gas‒liquid multiphase pumps","authors":"Xin Wu, Guojun Zhu, Jianjun Feng, Xingqi Luo","doi":"10.1016/j.apor.2024.104347","DOIUrl":"10.1016/j.apor.2024.104347","url":null,"abstract":"<div><div>The inlet gas volume fraction (IGVF) has a significant impact on the performance of the gas‒liquid multiphase pump. To explore the effects of different IGVFs on the performance of gas–liquid multiphase pump, it is taken as the research object and the deformation and stress of the impeller and pump shaft under pure water and different IGVFs are obtained. The results show that the maximum deformation of the impeller occurs at the blade trailing edge. The maximum deformation of the impeller under 20%, 30% IGVF is reduced by 9.97% and 16.79% compared to 10% IGVF, respectively. The maximum deformation of the pump shaft is decreased by 0.15% and 0.26%, respectively. The stress of the impeller at the connection between the blade trailing edge and the hub is the largest. The maximum stress of the impeller at 20% and 30% IGVF is reduced by 310 Pa and 600 Pa compared to 10% IGVF, respectively. The radial force and the axis trace are uniformly under pure water and are chaotic under different IGVFs. Under pure water condition, the maximum deformation and stress fluctuation on the impeller and shaft is smaller than that under different IGVFs. The amplitude of maximum deformation fluctuation on the impeller and shaft becomes smaller with the increasing IGVF.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104347"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166913","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}

引用次数: 0

Study on stress prediction model of EH36 steel in polar environments: Optimization of basis functions using adaptive genetic algorithm and simulated annealing

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104402

Hegang Ji, Jian Zhang, Shi Hua, Renwei Liu, Sanxia Shi

{"title":"Study on stress prediction model of EH36 steel in polar environments: Optimization of basis functions using adaptive genetic algorithm and simulated annealing","authors":"Hegang Ji, Jian Zhang, Shi Hua, Renwei Liu, Sanxia Shi","doi":"10.1016/j.apor.2024.104402","DOIUrl":"10.1016/j.apor.2024.104402","url":null,"abstract":"<div><div>As global temperatures rise, the Arctic route—a vital maritime corridor connecting Asia, Europe, and North America—is increasingly becoming a focus of international shipping. However, the extreme low-temperature environment of the Arctic presents significant challenges to the mechanical properties of shipbuilding steel, especially in marine engineering and ship design. This study investigates the mechanical behavior of polar-grade EH36 shipbuilding steel across a temperature range from -40 °C to 20 °C and strain rates from 0.00037/s to 5000/s, introducing an innovative approach for accurately predicting stress under these conditions. Comprehensive experimental data were obtained through a combination of quasi-static, low strain rate tensile tests, and high strain rate Split Hopkinson Pressure Bar tests. A stress prediction model was developed by integrating adaptive genetic algorithms and simulated annealing to optimize basis functions, enabling precise predictions across a wide spectrum of temperatures and strain rates. Model validation results demonstrate that the prediction error remains within 6 % under moderate strain rates from 1/s to 200/s, highlighting the model's high accuracy and broad applicability. This model not only overcomes the limitations of traditional experimental and interpolation methods but also provides essential data for the design and material selection of polar icebreaking ships, offering an efficient tool for engineers in extreme environments. These findings contribute to enhancing the performance and safety of vessels operating in polar regions.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104402"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166947","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}

引用次数: 0

Failure modes and inclined bearing capacity of a modified suction caisson in sand

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2025.104416

Yun Bai , Dayong Li

{"title":"Failure modes and inclined bearing capacity of a modified suction caisson in sand","authors":"Yun Bai , Dayong Li","doi":"10.1016/j.apor.2025.104416","DOIUrl":"10.1016/j.apor.2025.104416","url":null,"abstract":"<div><div>Experiments and simulations are undertaken on the bearing behavior of a modified suction caisson (MSC) in sand under inclined load. Results show that the MSC movement modes include forward rotation, backward rotation, translations mainly along the horizontal and vertical directions. When the MSC rotates forward, the earth pressure generated by the upper soil in front of the MSC plays an important part of the capacity. The lower passive earth pressure is crucial when the backward rotation occurs. When the MSC mainly moves horizontally, the soil in front of the MSC is entirely aroused. For the MSC mainly translates vertically, the passive suction is an important part of the bearing capacity. Moreover, the earth pressure induced by the soil in front of the external structure cannot be ignored when the MSC rotates forward and moves horizontally. In addition, the inclined loading rate shows a significant impact on the inclined bearing behavior. In the case of rotation occurring, both the earth pressure and passive suction display positive correlations with the loading rate. As loading rate increases, the increment of passive suction for forward rotation is greater than that for backward rotation, while the increment of earth pressure is greater for backward rotation.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104416"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166954","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}

引用次数: 0

Size effects on the in-situ compressive behavior of first-year sea ice

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104395

Yan Huang , Jiale Han , Jianqiao Sun , Tong Zhao

引用次数: 0

Deep learning-based landslide tsunami run-up prediction from synthetic gage data

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104360

Mustafa Açıkkar , Baran Aydın

{"title":"Deep learning-based landslide tsunami run-up prediction from synthetic gage data","authors":"Mustafa Açıkkar , Baran Aydın","doi":"10.1016/j.apor.2024.104360","DOIUrl":"10.1016/j.apor.2024.104360","url":null,"abstract":"<div><div>The present study proposes a deep learning model based on Long-Short Term Memory (LSTM) that uses gage measurements for prediction of landslide-driven maximum tsunami run-up. In an attempt to overcome the limitation of insufficient real-world data in the field, our methodology refers to analytical models to create a comprehensive dataset employing a time series recorded from an offshore gage as input and its corresponding maximum run-up at the shoreline as output, for different landslide scenarios with pre-determined parameters. The LSTM-based model is then trained using this dataset in order to predict the maximum run-up. The results, with mean values of 0.211 m, 0.149 m, 1.745% and 0.9988 for <em>RMSE</em>, <em>MAE</em>, <em>MAPE</em> and <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span>, respectively, indicate that our model is both accurate and precise. As the data-driven models such as the one proposed here are often utilized to identify relationships that may not be immediately apparent from the physical models alone, our interdisciplinary approach has the potential to foster the development of innovative solutions and methodologies for addressing complex natural hazards by enhancing early warning systems, preparedness and response to tsunamis.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104360"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166999","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}

引用次数: 0

Multi-fidelity strength monitoring method for dynamic response of deep-sea pipelines based on digital-twin technology

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2025.104414

Jianxing Yu , Zihang Jin , Yang Yu , Qiang Shao , Yefan Su , Zhenmian Li , Ruilong Gao , Hanxu Tian

{"title":"Multi-fidelity strength monitoring method for dynamic response of deep-sea pipelines based on digital-twin technology","authors":"Jianxing Yu , Zihang Jin , Yang Yu , Qiang Shao , Yefan Su , Zhenmian Li , Ruilong Gao , Hanxu Tian","doi":"10.1016/j.apor.2025.104414","DOIUrl":"10.1016/j.apor.2025.104414","url":null,"abstract":"<div><div>Cost-effective structural monitoring of deep-sea pipelines in harsh environments is essential for advancing deep-sea oil and gas development. This paper presents a multi-fidelity monitoring method for deep-sea pipelines based on digital twin (DT) model, applied to dynamic response monitoring under high pressure and forced vibration. This method integrates low-fidelity (LF) data from numerical simulations—clustered using the Hierarchical Geodesic K-means++ (HG-kmeans++) method—with high-fidelity (HF) data from full-scale pipeline tests. A pre-training model is developed using XGBoost based on LF data, and a DT model is established via the XGBoost-based Stacking-CGAN (XS-CGAN) method by integrating multi-fidelity data. A case study involving full-scale experiment demonstrates that the proposed approach significantly enhances both global and local accuracy. Compared to finite element analysis (FEA), the RRMSE and RE<sub>max</sub> improve by 40 % and 55.61 % respectively, outperforming current advanced method by 5.14 % and 3.79 %. These findings demonstrate the method's practical value for structural monitoring of deep-sea pipelines.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104414"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166405","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}

引用次数: 0

Semi-supervised learning network for deep-sea nodule mineral image segmentation

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104356

Zhongjun Ding , Chen Liu , Xingyu Wang , Guangyang Ma , Chanjuan Cao , Dewei Li

{"title":"Semi-supervised learning network for deep-sea nodule mineral image segmentation","authors":"Zhongjun Ding , Chen Liu , Xingyu Wang , Guangyang Ma , Chanjuan Cao , Dewei Li","doi":"10.1016/j.apor.2024.104356","DOIUrl":"10.1016/j.apor.2024.104356","url":null,"abstract":"<div><div>The accurate segmentation of deep-sea nodule mineral images is crucial for scientific mining. However, due to the low contrast of deep-sea images and the varying sizes of nodule minerals, existing methods are not effective in segmenting these images. Furthermore, fully supervised deep learning methods require numerous labelled images for training, and labelling deep-sea nodule mineral images is highly difficult, resulting in a scarcity of available labeled images, which limits the model generalization ability. To address these challenges, a semi-supervised learning network for deep-sea nodule image segmentation (NmiNet) was proposed. In this method, a semi-supervised training paradigm based on underwater image enhancement perturbation and uncertainty weighted optimization (UEUO) was designed. This paradigm enabled the model to fully mine the features in many unlabeled nodule mineral images under the condition of few labelled nodule mineral images, improving the model generalization ability. Moreover, a lightweight global and local feature extraction (GLFE) module was designed to enhance the attention of the module to small nodules, and its ability to locate nodules of different scales by fusing local and global features without considerably increasing model complexity. Experimental results on deep-sea nodule mineral images reveal that this method outperforms existing approaches.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104356"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166908","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}

引用次数: 0

Polyester line damage assessment considering fatigue and abrasion

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104376

Hongsheng Yan, Tongyu Jia, Jianxing Yu

引用次数: 0

Nonlinear modeling of the 3-D ocean sound speed field via incremental sample convolutional exchange network

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104383

Kequan Shi , Qi Li , Pan Xu , Bingbing Zhang , Hongna Zhu

{"title":"Nonlinear modeling of the 3-D ocean sound speed field via incremental sample convolutional exchange network","authors":"Kequan Shi , Qi Li , Pan Xu , Bingbing Zhang , Hongna Zhu","doi":"10.1016/j.apor.2024.104383","DOIUrl":"10.1016/j.apor.2024.104383","url":null,"abstract":"<div><div>The accurate construction of the underwater sound speed fields (SSFs) is critical for systems of positioning, navigation, timing, and communication (PNTC). Currently, the 3-D ocean currents observation is limited by insufficient flow field data and technical constraints, resulting in significant sound speed errors that adversely affect underwater positioning accuracy. To address the challenge of achieving continuous spatiotemporal observations within marine areas, we propose an incremental sample convolutional exchange network (ISCEN) to construct localized 3-D ocean SSFs. Through split and interactive learning, the model iteratively extracts and exchanges ocean acoustic environmental parameters across multiple temporal resolutions. Therefore, it can learn enhanced features of the SSFs and predicting future sound speed distributions. Note that the limitations of computational power and prolonged training times associated with traditional offline learning, we introduce an improved incremental learning method. This approach leverages prior information to achieve intelligent and high-precision construction of SSFs. Experimental results indicate that the model can accurately reconstruct sound speed profiles (SSPs) within the 0 m to 2000 m depth range in selected regions of the South China Sea and the Western Pacific on CORTA 1.0-WNP dataset. The proposed method can flexibly provide real-time sound speed distributions tailored to the varying needs across different areas.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104383"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166919","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}

引用次数: 0

Sediment dynamics and vegetation resilience: A case study on the tidal flats in the Yangtze Delta

IF 4.3 2区工程技术

Applied Ocean Research Pub Date : 2025-01-01 DOI: 10.1016/j.apor.2024.104399

Yong Xu , Haifei Yang , Hongjie Qiao , Chunye Hu , Biaobiao Peng , Zhenchang Zhu , Ya Ping Wang

{"title":"Sediment dynamics and vegetation resilience: A case study on the tidal flats in the Yangtze Delta","authors":"Yong Xu , Haifei Yang , Hongjie Qiao , Chunye Hu , Biaobiao Peng , Zhenchang Zhu , Ya Ping Wang","doi":"10.1016/j.apor.2024.104399","DOIUrl":"10.1016/j.apor.2024.104399","url":null,"abstract":"<div><div>Salt marshes are among the most resilient systems in coastal environments, playing a key role in resisting rising sea level through sediment accretion and mitigating the pressures of climate change. However, our understanding of the highly complex interactions between salt marsh vegetation resilience and sediment dynamics is limited. Hence this study collected data from three land-to-sea stations (interior salt marsh, salt marsh edge, mudflat) in the Yangtze Delta. Our findings revealed that waves were attenuated more heavily than currents in salt marshes, whereas both dissipated less intensely in mudflats. The tide-averaged, wave-current coupled bed shear stress was 0.25 N/m<sup>2</sup> at the mudflat, respectively 1.5 and 2.2 times greater than values at the edge of the salt marsh (0.17 N/m<sup>2</sup>) and in the marsh itself (0.12 N/m<sup>2</sup>). The dynamic intensity (bed-shear-stress impulse after accounting for inundation duration), was 2.1 and 3.2 times higher at the mudflat than at the other two stations. We identified two possible key interactive processes. First, long-term dynamic intensity could be a better representative indicator that affects the spatial distribution of vegetation, with plants becoming sparser seaward as the enhancement of dynamic intensity accelerates. Second, root length might be related to intratidal-scale sediment resuspension controlled by hydrodynamic forces, with roots becoming shorter shoreward as the thickness of intratidal erodible sediment decreases. In conclusion, we addressed the critical question of how environmental drivers affect spatial distribution and resilience of salt marsh vegetation. These findings have major implications for global efforts to restore natural coastal ecosystems and enhance resilience against marine disasters.</div></div>","PeriodicalId":8261,"journal":{"name":"Applied Ocean Research","volume":"154 ","pages":"Article 104399"},"PeriodicalIF":4.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143166944","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}

引用次数: 0