Hongbo Li, Jiancheng Yu, Zhier Chen, Kai Ren, Zhiduo Tan
The efficient and stable application of periodic forcing for drag-reduction can help underwater vehicles operate at high speed for long durations and improve their energy-utilization efficiency. This study considers flow control around a body-of-revolution model subjected to periodic blowing or suction through annular slots. The focus is on the boundary-layer structure, properties, and drag of the control fluid under a wide range of body variables (size, free-flow velocity, slot area, and blowing/suction velocity) and control parameters (normalized periodic-forcing amplitude and relative slot sizes). Body variables differ in their effects on the drag-reduction rate, with the surface pressure pushing the model vehicle when S and v are higher than S0 and v0. In particular, the lowest pressure drag was −26.4 N with v increasing, and the maximum drag-reduction rate of total drag exceeded 135%. At a fixed Reynolds number, increasing the values of the control parameters leads to larger-scale unstable vortex rings downstream from the slots; the surface-velocity gradient is reduced, effectively lowering the drag. A simple model relating the periodic fluctuation of pressure drag to the body variables is developed through quantitative analysis and used to determine navigational stability.
高效、稳定地应用周期性强制来减少阻力，有助于水下航行器长时间高速运行，并提高其能量利用效率。本研究考虑了通过环形槽受到周期性吹气或吸气的旋转体模型周围的流动控制。重点是在各种体变量（尺寸、自由流动速度、槽面积和吹气/吸气速度）和控制参数（归一化周期性强制振幅和相对槽尺寸）条件下控制流体的边界层结构、特性和阻力。车体变量对阻力减小率的影响各不相同，当 S 和 v 高于 S0 和 v0 时，表面压力会推动模型车辆。其中，随着 v 的增大，最低压力阻力为-26.4 N，总阻力的最大阻力减少率超过 135%。在雷诺数固定的情况下，控制参数值的增加会导致槽下游出现更大尺度的不稳定涡环；表面速度梯度减小，从而有效降低阻力。通过定量分析，建立了压力阻力周期性波动与机体变量相关的简单模型，并用于确定航行稳定性。
{"title":"Adjustability and Stability of Flow Control by Periodic Forcing: A Numerical Investigation","authors":"Hongbo Li, Jiancheng Yu, Zhier Chen, Kai Ren, Zhiduo Tan","doi":"10.3390/jmse12091613","DOIUrl":"https://doi.org/10.3390/jmse12091613","url":null,"abstract":"The efficient and stable application of periodic forcing for drag-reduction can help underwater vehicles operate at high speed for long durations and improve their energy-utilization efficiency. This study considers flow control around a body-of-revolution model subjected to periodic blowing or suction through annular slots. The focus is on the boundary-layer structure, properties, and drag of the control fluid under a wide range of body variables (size, free-flow velocity, slot area, and blowing/suction velocity) and control parameters (normalized periodic-forcing amplitude and relative slot sizes). Body variables differ in their effects on the drag-reduction rate, with the surface pressure pushing the model vehicle when S and v are higher than S0 and v0. In particular, the lowest pressure drag was −26.4 N with v increasing, and the maximum drag-reduction rate of total drag exceeded 135%. At a fixed Reynolds number, increasing the values of the control parameters leads to larger-scale unstable vortex rings downstream from the slots; the surface-velocity gradient is reduced, effectively lowering the drag. A simple model relating the periodic fluctuation of pressure drag to the body variables is developed through quantitative analysis and used to determine navigational stability.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"60 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The wake behind an aircraft carrier under heavy wind condition is a key concern in ship design. The Chinese Liaoning ship’s upturned bow and the island on the deck could cause serious flow separation in the landing and take-off area. The flow separation induces strong velocity gradients and intense pulsations in the flow field. In addition, the sway of the aircraft carrier caused by waves could also intensify the flow separation. The complex flow field poses a significant risk to the shipboard aircraft take-off and landing operation. Therefore, accurately predicting the wake of an aircraft carrier during wave action motion is of great interest for design optimization and recovery aircraft control. In this research, the aerodynamic around an aircraft carrier (i.e., Liaoning) was analyzed using the computational fluid dynamics technique. The validity of two turbulence models was verified through comparison with the existing data from the literature. The upturned bow take-off deck and the right-hand island were the main areas where flow separation occurred. Delayed detached eddy simulation (DDES), which combines the advantages of LES and RANS, was adopted to capture the full-scale spatial and temporal flow information. The DDES was also coupled with the overset grid to calculate the flow field characteristics under the effect of hull sway. The downwash area at 15° starboard wind became shorter when the hull was stationary, while the upwash area and turbulence intensity increased. The respective characteristics of the wake flow field in the stationary and swaying state of the ship were investigated, and the flow separation showed a clear periodic when the ship was swaying. Comprehensive analysis of the time-dependent flow characteristic of the approach line for fixed-wing naval aircraft is also presented.
大风条件下航母后方的尾流是船舶设计中的一个关键问题。中国辽宁舰上翘的舰艏和甲板上的舰岛可能会在起降区域造成严重的气流分离。流体分离会在流场中产生强烈的速度梯度和脉动。此外，波浪造成的航母摇摆也会加剧流场分离。复杂的流场对舰载机的起飞和着陆操作构成了极大的风险。因此，准确预测航母在波浪作用运动时的尾流对于优化设计和回收飞机控制具有重要意义。本研究利用计算流体力学技术对航空母舰（即辽宁舰）周围的空气动力进行了分析。通过与现有文献数据的对比，验证了两个湍流模型的有效性。上翘的舰首起飞甲板和右侧舰岛是发生气流分离的主要区域。采用了结合了 LES 和 RANS 优点的延迟分离涡模拟（DDES）来捕捉全尺度的时空流动信息。DDES 还与超集网格相结合，计算船体摇摆影响下的流场特征。当船体静止时，15°右舷风向的下冲面积变小，而上冲面积和湍流强度增加。研究了船体静止和摇摆状态下尾流流场的各自特征，发现船体摇摆时，流体分离呈现出明显的周期性。此外，还对固定翼舰载机进场线随时间变化的流动特性进行了综合分析。
{"title":"Numerical Simulation of the Unsteady Airwake of the Liaoning Carrier Based on the DDES Model Coupled with Overset Grid","authors":"Xiaoxi Yang, Baokuan Li, Zhibo Ren, Fangchao Tian","doi":"10.3390/jmse12091598","DOIUrl":"https://doi.org/10.3390/jmse12091598","url":null,"abstract":"The wake behind an aircraft carrier under heavy wind condition is a key concern in ship design. The Chinese Liaoning ship’s upturned bow and the island on the deck could cause serious flow separation in the landing and take-off area. The flow separation induces strong velocity gradients and intense pulsations in the flow field. In addition, the sway of the aircraft carrier caused by waves could also intensify the flow separation. The complex flow field poses a significant risk to the shipboard aircraft take-off and landing operation. Therefore, accurately predicting the wake of an aircraft carrier during wave action motion is of great interest for design optimization and recovery aircraft control. In this research, the aerodynamic around an aircraft carrier (i.e., Liaoning) was analyzed using the computational fluid dynamics technique. The validity of two turbulence models was verified through comparison with the existing data from the literature. The upturned bow take-off deck and the right-hand island were the main areas where flow separation occurred. Delayed detached eddy simulation (DDES), which combines the advantages of LES and RANS, was adopted to capture the full-scale spatial and temporal flow information. The DDES was also coupled with the overset grid to calculate the flow field characteristics under the effect of hull sway. The downwash area at 15° starboard wind became shorter when the hull was stationary, while the upwash area and turbulence intensity increased. The respective characteristics of the wake flow field in the stationary and swaying state of the ship were investigated, and the flow separation showed a clear periodic when the ship was swaying. Comprehensive analysis of the time-dependent flow characteristic of the approach line for fixed-wing naval aircraft is also presented.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"8 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yilin Huang, Da Hui, Mingyu Xia, Guangyao Wang, Jinshan Zhu
The influence of oblique currents in narrow and shallow channels causes the fluid flow around ships to become complex. To analyze the hydrodynamic characteristics of a ship in such channels, it is essential to examine the influence of oblique currents on the ship’s hydrodynamic characteristics. In this study, current direction, ship speed, current speed, and water depth were identified as determinants affecting the hydrodynamic characteristics of a ship. Numerical simulations were conducted on a large oil tanker to investigate the effects of these factors on the ship’s hydrodynamic characteristics. The viscous fluid flow was modeled using the unsteady Reynolds-averaged Navier–Stokes (URANS) equations in conjunction with the k-ε turbulence model. The URANS equations were discretized using the finite volume method. The numerical results indicate substantial differences in the hydrodynamic characteristics of ships under oblique current conditions compared to still-water conditions. At a current direction of β = −45°, the direction of the sway force is consistent with that of still water’s sway force, which is an attractive force. The yaw moment at β = −45° changes from a bow-out moment under still-water conditions to a bow-in moment. Conversely, at a current direction of β = 45°, the sway force shifts from an attractive force under still-water conditions to a repulsive force. The yaw moment acts as a bow-out moment, which is consistent with that observed in still-water conditions. Furthermore, the influence of hydrodynamic characteristics on a ship varies significantly with changes in ship speed, current speed, and water depth. To ensure the safe navigation of ships, it is essential to develop and apply comprehensive strategies and countermeasures that account for practical conditions.
{"title":"Numerical Investigation of Oblique Currents’ Effects on the Hydrodynamic Characteristics of Ships in Restricted Waters","authors":"Yilin Huang, Da Hui, Mingyu Xia, Guangyao Wang, Jinshan Zhu","doi":"10.3390/jmse12091592","DOIUrl":"https://doi.org/10.3390/jmse12091592","url":null,"abstract":"The influence of oblique currents in narrow and shallow channels causes the fluid flow around ships to become complex. To analyze the hydrodynamic characteristics of a ship in such channels, it is essential to examine the influence of oblique currents on the ship’s hydrodynamic characteristics. In this study, current direction, ship speed, current speed, and water depth were identified as determinants affecting the hydrodynamic characteristics of a ship. Numerical simulations were conducted on a large oil tanker to investigate the effects of these factors on the ship’s hydrodynamic characteristics. The viscous fluid flow was modeled using the unsteady Reynolds-averaged Navier–Stokes (URANS) equations in conjunction with the k-ε turbulence model. The URANS equations were discretized using the finite volume method. The numerical results indicate substantial differences in the hydrodynamic characteristics of ships under oblique current conditions compared to still-water conditions. At a current direction of β = −45°, the direction of the sway force is consistent with that of still water’s sway force, which is an attractive force. The yaw moment at β = −45° changes from a bow-out moment under still-water conditions to a bow-in moment. Conversely, at a current direction of β = 45°, the sway force shifts from an attractive force under still-water conditions to a repulsive force. The yaw moment acts as a bow-out moment, which is consistent with that observed in still-water conditions. Furthermore, the influence of hydrodynamic characteristics on a ship varies significantly with changes in ship speed, current speed, and water depth. To ensure the safe navigation of ships, it is essential to develop and apply comprehensive strategies and countermeasures that account for practical conditions.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"38 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to study the impact of acoustic propagation characteristics in the northeastern South China Sea, GEBCO08 global terrain grid data and Argo data were used to numerically simulate the acoustic transmission characteristics of two stations in the northeast South China Sea affected by the Kuroshio. The impact of different marine environments on acoustic transmission characteristics was analyzed. The results show that increasing the deployment depth of a sound source within a certain range will reduce the transmission loss; deploying a sound source near the axis of the surface acoustic channel or the deep-sea acoustic channel will also greatly increase the propagation distance of sound signals; and the presence of topography such as undersea mountains will increase the transmission loss.
{"title":"The Impact of Special Marine Environments Such as the Kuroshio on Hydroacoustic Detection Equipment","authors":"Xueqin Zhang, Kunde Yang, Xiaolin Yu","doi":"10.3390/jmse12091594","DOIUrl":"https://doi.org/10.3390/jmse12091594","url":null,"abstract":"In order to study the impact of acoustic propagation characteristics in the northeastern South China Sea, GEBCO08 global terrain grid data and Argo data were used to numerically simulate the acoustic transmission characteristics of two stations in the northeast South China Sea affected by the Kuroshio. The impact of different marine environments on acoustic transmission characteristics was analyzed. The results show that increasing the deployment depth of a sound source within a certain range will reduce the transmission loss; deploying a sound source near the axis of the surface acoustic channel or the deep-sea acoustic channel will also greatly increase the propagation distance of sound signals; and the presence of topography such as undersea mountains will increase the transmission loss.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"72 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.
{"title":"Sequential Two-Mode Fusion Underwater Single-Photon Lidar Imaging Algorithm","authors":"Tian Rong, Yuhang Wang, Qiguang Zhu, Chenxu Wang, Yanchao Zhang, Jianfeng Li, Zhiquan Zhou, Qinghua Luo","doi":"10.3390/jmse12091595","DOIUrl":"https://doi.org/10.3390/jmse12091595","url":null,"abstract":"Aiming at the demand for long-range and high-resolution imaging detection of small targets such as submerged submarine markers in shallow coastal waters, research on single-photon lidar imaging technology is carried out. This paper reports the sequential two-mode fusion imaging algorithm, which has a strong information extraction capability and can reconstruct scene target depth and reflection intensity images from complex signal photon counts. The algorithm consists of four steps: data preprocessing, extremely large group value estimation, noise sieving, and total variation smoothing constraints to image the target with high quality. Simulation and test results show that the imaging performance and imaging characteristics of the method are better than the current high-performance first-photon group imaging algorithm, indicating that the method has a great advantage in sparse photon counting imaging, and the method proposed in this paper constructs a clear depth and reflectance intensity image of the target scene, even in the 50,828 Lux ambient strong light and strong interference, the 0.1 Lux low-light environment, or the underwater high-attenuation environment.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"1 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yucheng Zou, Yuan Du, Zhe Zhao, Fuzhen Pang, Haichao Li, David Hui
At high speeds, flow-induced vibration noise is the main component of underwater vehicle noise. The turbulent fluctuating pressure is the main excitation source of this noise. It can cause vibration of the underwater vehicle’s shell and eventually radiate noise outward. Therefore, by reducing the turbulent pressure fluctuation or controlling the vibration of the underwater vehicle’s shell, the radiation noise of the underwater vehicle can be effectively reduced. This study designs a cone–column–sphere composite structure. Firstly, the effect of fluid–structure coupling on pulsating pressure is studied. Next, a machine learning method is used to predict the turbulent pressure fluctuations and the fluid-induced vibration response of the structure at different speeds. The results were compared with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsating pressure of the cylindrical section. The machine learning method based on the BP (back propagation) neural network model can quickly predict the pulsating pressure and vibration response of the cone–cylinder–sphere composite structure under different Reynolds numbers. Compared with the experimental results, the error of the machine learning prediction results is less than 7%. The research method proposed in this paper provides a new solution for the rapid prediction and control of hydrodynamic vibration noise of underwater vehicles.
{"title":"Experimental and Simulation Study on Flow-Induced Vibration of Underwater Vehicle","authors":"Yucheng Zou, Yuan Du, Zhe Zhao, Fuzhen Pang, Haichao Li, David Hui","doi":"10.3390/jmse12091597","DOIUrl":"https://doi.org/10.3390/jmse12091597","url":null,"abstract":"At high speeds, flow-induced vibration noise is the main component of underwater vehicle noise. The turbulent fluctuating pressure is the main excitation source of this noise. It can cause vibration of the underwater vehicle’s shell and eventually radiate noise outward. Therefore, by reducing the turbulent pressure fluctuation or controlling the vibration of the underwater vehicle’s shell, the radiation noise of the underwater vehicle can be effectively reduced. This study designs a cone–column–sphere composite structure. Firstly, the effect of fluid–structure coupling on pulsating pressure is studied. Next, a machine learning method is used to predict the turbulent pressure fluctuations and the fluid-induced vibration response of the structure at different speeds. The results were compared with experimental and numerical simulation results. The results show that the deformation of the structure will affect the flow field distribution and pulsating pressure of the cylindrical section. The machine learning method based on the BP (back propagation) neural network model can quickly predict the pulsating pressure and vibration response of the cone–cylinder–sphere composite structure under different Reynolds numbers. Compared with the experimental results, the error of the machine learning prediction results is less than 7%. The research method proposed in this paper provides a new solution for the rapid prediction and control of hydrodynamic vibration noise of underwater vehicles.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"60 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific article is to comprehensively analyze and identify the primary security threats and vulnerabilities that IoT devices face when deployed in port environments. This includes examining potential risks, such as unauthorized access, cyberattacks, malware, etc., that could disrupt critical port operations and compromise sensitive information. This research aims to assess the critical entities associated with IoT devices in port environments and develop a comprehensive risk-management framework tailored to these settings. It also aims to explore and propose strategic measures and best practices to mitigate these risks. For this research, a risk-management framework grounded in the principles of ORM, which includes risk avoidance, reduction, sharing, and retention strategies, was developed. The primary outcome of this research is the development of a comprehensive risk-management framework specifically tailored for IoT devices in port environments, utilizing Operational Risk-Management (ORM) methodology. This framework will systematically identify and categorize critical vulnerabilities and potential threats for IoT devices. By addressing these objectives, the article seeks to provide actionable insights and guidelines that can be adopted by port authorities and stakeholders to safeguard their IoT infrastructure and maintain operational stability in the face of emerging threats.
{"title":"Assessing Critical Entities: Risk Management for IoT Devices in Ports","authors":"Ioannis Argyriou, Theocharis Tsoutsos","doi":"10.3390/jmse12091593","DOIUrl":"https://doi.org/10.3390/jmse12091593","url":null,"abstract":"Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific article is to comprehensively analyze and identify the primary security threats and vulnerabilities that IoT devices face when deployed in port environments. This includes examining potential risks, such as unauthorized access, cyberattacks, malware, etc., that could disrupt critical port operations and compromise sensitive information. This research aims to assess the critical entities associated with IoT devices in port environments and develop a comprehensive risk-management framework tailored to these settings. It also aims to explore and propose strategic measures and best practices to mitigate these risks. For this research, a risk-management framework grounded in the principles of ORM, which includes risk avoidance, reduction, sharing, and retention strategies, was developed. The primary outcome of this research is the development of a comprehensive risk-management framework specifically tailored for IoT devices in port environments, utilizing Operational Risk-Management (ORM) methodology. This framework will systematically identify and categorize critical vulnerabilities and potential threats for IoT devices. By addressing these objectives, the article seeks to provide actionable insights and guidelines that can be adopted by port authorities and stakeholders to safeguard their IoT infrastructure and maintain operational stability in the face of emerging threats.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"5 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a novel real-time prediction technique for multi-degree-of-freedom ship motion and resting periods utilizing Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the safety and efficiency of shipborne helicopter landings by accurately predicting heave, pitch, and roll data over an 8 s forecast horizon. The proposed method utilizes the LSTM network’s capability to model complex nonlinear time series while employing the User Datagram Protocol (UDP) to ensure efficient data transmission. The model’s performance was validated using real-world ship motion data collected across various sea states, achieving a maximum prediction error of less than 15%. The findings indicate that the LSTM-based model provides reliable predictions of ship resting periods, which are crucial for safe helicopter operations in adverse sea conditions. This method’s capability to provide real-time predictions with minimal computational overhead highlights its potential for broader applications in marine engineering. Future research should explore integrating multi-model fusion techniques to enhance the model’s adaptability to rapidly changing sea conditions and improve the prediction accuracy.
{"title":"Real-Time Prediction of Multi-Degree-of-Freedom Ship Motion and Resting Periods Using LSTM Networks","authors":"Zhanyang Chen, Xingyun Liu, Xiao Ji, Hongbin Gui","doi":"10.3390/jmse12091591","DOIUrl":"https://doi.org/10.3390/jmse12091591","url":null,"abstract":"This study presents a novel real-time prediction technique for multi-degree-of-freedom ship motion and resting periods utilizing Long Short-Term Memory (LSTM) networks. The primary objective is to enhance the safety and efficiency of shipborne helicopter landings by accurately predicting heave, pitch, and roll data over an 8 s forecast horizon. The proposed method utilizes the LSTM network’s capability to model complex nonlinear time series while employing the User Datagram Protocol (UDP) to ensure efficient data transmission. The model’s performance was validated using real-world ship motion data collected across various sea states, achieving a maximum prediction error of less than 15%. The findings indicate that the LSTM-based model provides reliable predictions of ship resting periods, which are crucial for safe helicopter operations in adverse sea conditions. This method’s capability to provide real-time predictions with minimal computational overhead highlights its potential for broader applications in marine engineering. Future research should explore integrating multi-model fusion techniques to enhance the model’s adaptability to rapidly changing sea conditions and improve the prediction accuracy.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"295 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.
{"title":"Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale","authors":"Zemin Zhou, Yanrui Qu, Boqing Zhu, Bingbing Zhang","doi":"10.3390/jmse12091596","DOIUrl":"https://doi.org/10.3390/jmse12091596","url":null,"abstract":"Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"10 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities.
{"title":"GIS-Based Optimal Siting of Offshore Wind Farms to Support Zero-Emission Ferry Routes","authors":"Orfeas Karountzos, Stamatina Giannaki, Konstantinos Kepaptsoglou","doi":"10.3390/jmse12091585","DOIUrl":"https://doi.org/10.3390/jmse12091585","url":null,"abstract":"To achieve net zero emissions from ships by 2050 and align with the IMO 2023 GHG strategy, the maritime industry must significantly increase zero-emission vessels by 2030. Transitioning to fully electric ferry lines requires enhanced energy supply through renewable energy sources (RES) for complete GHG mitigation and net-zero emissions. This study presents a GIS-based framework for optimally selecting offshore wind farm locations to meet the energy demands of electric ferry operations along coastal routes. The framework involves two stages: designing feasible zero-emission ferry routes between islands or to the mainland and identifying optimal offshore wind farm sites by evaluating technical, spatial, economic, social, and environmental criteria based on national legislation and the academic literature. The aim is to create a flexible framework to support decision making for establishing sustainable electric ferry operations at a regional level, backed by strategically located offshore wind farms. The study applies this framework to the Greek Coastal Shipping Network, focusing on areas with potential for future electrification. The findings can aid policymakers in utilizing spatial decision support systems (SDSS) to enhance efficient transportation and develop sustainable island communities.","PeriodicalId":16168,"journal":{"name":"Journal of Marine Science and Engineering","volume":"2 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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