Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Hossein Nejatbakhsh Esfahani, Arash Bahari Kordabad, David Moreno-Salinas

Autonomous and highly automated maritime systems are moving from controlled demonstrations to sustained operations in congested waterways and harsh sea states [...]

Kunlin Wang, Peifan Chen, Yin Ye et al.

As a critical component of marine renewable energy, wave energy has long remained a focal point in research on development and use. The Sharp Eagle wave energy converter (hereafter, Sharp Eagle WEC) exhibits wave energy capture efficiency-related advantages, which are attributed to the unique structural configuration of its Sharp Eagle wave-absorbing buoy (hereafter, buoy). Operational observations reveal that under severe sea conditions, buoy motion amplitude increases significantly. Consequently, the downstream hydraulic and power generation systems experience excessive power loads, and the converter exceeds displacement limits, causing collisions with end-stop structures, which compromises operational safety. Research findings indicate that the attitude of the buoy directly governs its motion characteristics. We proposed a ballast-and-load-based attitude control method for the buoy. This approach provides safe and efficient operation across all sea conditions. Via scaled model tests, converter operational data covering various ballast configurations were compared and analyzed, focusing on the effects of ballast on the capture width ratio (hereafter, CWR) and piston displacement range of energy conversion hydraulic cylinders. Herein, the feasibility of adjusting capture efficiency and motion displacement by controlling the buoy attitude is validated, providing a technical framework for efficient and safe operation of the WEC under all sea conditions.

Shuaishuai Ruan, Weifeng Jin, Xiaohui Liao

Colloidal silica can seep through calcareous sand in the subgrade, forming colloidal-silica-cemented sand with self-sensing ability—that is, it is sensitive to stress changes caused by vehicle loading. Its self-sensing sensitivity is higher than that of traditional Portland-cement-based self-sensing materials. The self-sensing mechanism is attributed to the ionic conductive network formed by seawater. However, a change in tidal water level causes an unsaturated state, and foundation deformation leads to cracking of the roadbed. The effect of unsaturation and cracking on self-sensing remains unclear, and they have not been studied in the previous literature. The aim of this paper is to study the self-sensing ability of subgrades formed via colloidal-silica-cemented sand under unsaturated and cracked states, as well as to explore the underlying mechanisms. Specimens with different degrees of saturation and different levels of joint roughness in precracks were prepared; then, the self-sensing ability was tested using the four-electrode method for each specimen under cyclic stress loading. NMR (nuclear magnetic resonance) and an unsaturated triaxial apparatus were also used to investigate the underlying mechanisms. This paper discovers that (1) either unsaturation or crack alone can increase self-sensing, but their self-sensing sensitivities are on the same order; (2) under the coupled effect of unsaturation and cracking, the self-sensing sensitivity increases by one order of magnitude, which is higher than when only unsaturation or cracking exists; and (3) the joint roughness of precracks does not affect self-sensing in the saturated state, but it affects self-sensing dramatically in the unsaturated state. The NMR test demonstrated the conductive ionic water within nanopores, which forms the conductive network for self-sensing. Unsaturation causes suction-induced shrinkage based on the unsaturated triaxial apparatus, while unsaturation increases self-sensing sensitivity, indicating that shrinkage is accompanied by self-sensing improvement. This paper provides the effects of unsaturation and cracking on the self-sensing capabilities of colloidal-silica-cemented sand, and the findings can contribute to the knowledge of subgrades formed via colloidal-silica-cemented sand for stress-sensing under traffic loading.

Markus Buchholz, Ignacio Carlucho, Michele Grimaldi et al.

This paper introduces a novel simulation framework for evaluating motion control in tethered multi-robot systems within dynamic marine environments. Specifically, it focuses on the coordinated operation of an Autonomous Underwater Vehicle (AUV) and an Autonomous Surface Vehicle(ASV). The framework leverages GazeboSim, enhanced with realistic marine environment plugins and ArduPilots SoftwareIn-The-Loop (SITL) mode, to provide a high-fidelity simulation platform. A detailed tether model, combining catenary equations and physical simulation, is integrated to accurately represent the dynamic interactions between the vehicles and the environment. This setup facilitates the development and testing of advanced control strategies under realistic conditions, demonstrating the frameworks capability to analyze complex tether interactions and their impact on system performance.

Joost van Dalen, Yuki M. Asano, Marc Russwurm

This work proposes SAMSelect, an algorithm to obtain a salient three-channel visualization for multispectral images. We develop SAMSelect and show its use for marine scientists visually interpreting floating marine debris in Sentinel-2 imagery. These debris are notoriously difficult to visualize due to their compositional heterogeneity in medium-resolution imagery. Out of these difficulties, a visual interpretation of imagery showing marine debris remains a common practice by domain experts, who select bands and spectral indices on a case-by-case basis informed by common practices and heuristics. SAMSelect selects the band or index combination that achieves the best classification accuracy on a small annotated dataset through the Segment Anything Model. Its central assumption is that the three-channel visualization achieves the most accurate segmentation results also provide good visual information for photo-interpretation. We evaluate SAMSelect in three Sentinel-2 scenes containing generic marine debris in Accra, Ghana, and Durban, South Africa, and deployed plastic targets from the Plastic Litter Project. This reveals the potential of new previously unused band combinations (e.g., a normalized difference index of B8, B2), which demonstrate improved performance compared to literature-based indices. We describe the algorithm in this paper and provide an open-source code repository that will be helpful for domain scientists doing visual photo interpretation, especially in the marine field.

Elizabeth Dietrich, Emir Cem Gezer, Bingzhuo Zhong et al.

We develop a hierarchical control architecture for autonomous docking maneuvers of a dynamic positioning vessel and provide formal safety guarantees. At the upper-level, we treat the vessel's desired surge, sway, and yaw velocities as control inputs and synthesize a symbolic controller in real-time. The desired velocities are then executed by the vessel's low-level velocity feedback control loop. We next investigate methods to optimize the performance of the proposed control scheme. The results are evaluated on a simulation model of a marine surface vessel in the presence of static obstacles and, for the first time, through physical experiments on a scale model vessel.

Heegyeong Kim, Alice James, Avishkar Seth et al.

This paper introduces an autonomous UAV vision system for continuous, real-time tracking of marine animals, specifically sharks, in dynamic marine environments. The system integrates an onboard computer with a stabilised RGB-D camera and a custom-trained OSTrack pipeline, enabling visual identification under challenging lighting, occlusion, and sea-state conditions. A key innovation is the inter-UAV handoff protocol, which enables seamless transfer of tracking responsibilities between drones, extending operational coverage beyond single-drone battery limitations. Performance is evaluated on a curated shark dataset of 5,200 frames, achieving a tracking success rate of 81.9\% during real-time flight control at 100 Hz, and robustness to occlusion, illumination variation, and background clutter. We present a seamless UAV handoff framework, where target transfer is attempted via high-confidence feature matching, achieving 82.9\% target coverage. These results confirm the viability of coordinated UAV operations for extended marine tracking and lay the groundwork for scalable, autonomous monitoring.

Sen Deng, Weiqiang Zhao, Tianbao Huang et al.

Kaplan turbines are generally used in working conditions with a high flow and low head. These are a type of axial-flow hydro turbine that can adjust the opening of the guide vanes and blades simultaneously in order to achieve higher efficiency under a wider range of loads. Different combinations of the opening of the guide vanes and blades (cam relationship) will lead to changes in the efficiency of the turbine unit as well as its vibration characteristics. A bad cam relationship will cause the low efficiency or unstable operation of the turbine. In this study, the relative efficiency and vibration of a large-scale Kaplan turbine with 200 MW output were tested with different guide vane and blade openings. The selection of the cam relationship curve for both optimal efficiency and optimal vibration is discussed. Compared with the cam relationship given by the model test, the prototype cam relationship improves the efficiency and reduces the vibration level. Compared to the optimal efficiency cam relationship, the optimal vibration cam relationship reduces the efficiency of the machine by 1% to 2%, while with the optimal efficiency cam relationship, the vibration of the unit increases significantly. This research provides guidance for the optimization of the regulation of a large adjustable-blade Kaplan turbine unit and improves the overall economic benefits and safety performance of the Kaplan turbine power station.

Yuyin MA, Yanfeng WANG, Sheng GUAN et al.

In recent years, underwater gliders have been widely used in the observation of various ocean surveys. However, their motion is often seriously affected when observing strong currents such as the Kuroshio. Therefore, the motion control of underwater gliders in the Kuroshio was studied in this paper. First, with Petrel-II as the research object, a dynamics model considering the Kuroshio was established based on the momentum and momentum moment theorem. Then, the Kuroshio data downloaded from the HYCOM website was used as interference, which featured varying speeds and directions of Kuroshio at different positions, and Simulink was used to simulate the motion of the Petrel-II under the influence of strong currents. Finally, the radial basis function(RBF) neural network was combined with the conventional proportional-integral-derivative(PID) controller to control the yaw motion and trim motion of the Petrel-II. The simulation results show that the RBF-PID controller can improve the motion tracking accuracy of Petrel-II in the Kuroshio area and enhance its ability to resist the interference of the Kuroshio. This study can provide a reference for the motion control of underwater gliders under the influence of strong currents to some extent.

Madhava Krishna, Bhagesh Gaur, Arsh Verma et al.

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.

Xiaoteng Zhou, Katsunori Mizuno

With the development of coastal construction, a large amount of human-generated waste, particularly plastic debris, is continuously entering the ocean, posing a severe threat to marine ecosystems. The key to effectively addressing plastic pollution lies in the ability to autonomously monitor such debris. Currently, marine debris monitoring primarily relies on optical sensors, but these methods are limited in their applicability to underwater and seafloor areas due to low-visibility constraints. The acoustic camera, also known as high-resolution forward-looking sonar (FLS), has demonstrated considerable potential in the autonomous monitoring of marine debris, as they are unaffected by water turbidity and dark environments. The appearance of targets in sonar images changes with variations in the imaging viewpoint, while challenges such as low signal-to-noise ratio, weak textures, and imaging distortions in sonar imagery present significant obstacles to debris monitoring based on prior class labels. This paper proposes an optical flow-based method for marine debris monitoring, aiming to fully utilize the time series information captured by the acoustic camera to enhance the performance of marine debris monitoring without relying on prior category labels of the targets. The proposed method was validated through experiments conducted in a circulating water tank, demonstrating its feasibility and robustness. This approach holds promise for providing novel insights into the spatial and temporal distribution of debris.

Yujia Yang, Chris Manzie, Ye Pu

The agents within a multi-agent system (MAS) operating in marine environments often need to utilize task payloads and avoid collisions in coordination, necessitating adherence to a set of relative-pose constraints, which may include field-of-view, line-of-sight, collision-avoidance, and range constraints. A nominal controller designed for reference tracking may not guarantee the marine MAS stays safe w.r.t. these constraints. To modify the nominal input as one that enforces safety, we introduce a framework to systematically encode the relative-pose constraints as nonsmooth control barrier functions (NCBFs) and combine them as a single NCBF using Boolean composition, which enables a simplified verification process compared to using the NCBFs individually. While other relative-pose constraint functions have explicit derivatives, the challenging line-of-sight constraint is encoded with the minimum distance function between the line-of-sight set and other agents, whose derivative is not explicit. Hence, existing safe control design methods that consider composite NCBFs cannot be applied. To address this challenge, we propose a novel quadratic program formulation based on the dual of the minimum distance problem and develop a new theory to ensure the resulting control input guarantees constraint satisfaction. Lastly, we validate the effectiveness of our proposed framework on a simulated large-scale marine MAS and a real-world marine MAS comprising one Unmanned Surface Vehicle and two Unmanned Underwater Vehicles.

Kevin Pitstick, Marc Novakouski, Grace A. Lewis et al.

Increasing rate of progress in hardware and artificial intelligence (AI) solutions is enabling a range of software systems to be deployed closer to their users, increasing application of edge software system paradigms. Edge systems support scenarios in which computation is placed closer to where data is generated and needed, and provide benefits such as reduced latency, bandwidth optimization, and higher resiliency and availability. Users who operate in highly-uncertain and resource-constrained environments, such as first responders, law enforcement, and soldiers, can greatly benefit from edge systems to support timelier decision making. Unfortunately, understanding how different architecture approaches for edge systems impact priority quality concerns is largely neglected by industry and research, yet crucial for national and local safety, optimal resource utilization, and timely decision making. Much of industry is focused on the hardware and networking aspects of edge systems, with very little attention to the software that enables edge capabilities. This paper presents our work to fill this gap, defining a reference architecture for edge systems in highly-uncertain environments, and showing examples of how it has been implemented in practice.

Yutuo Yang, Wei Liang, Daoxian Zhou et al.

Cultural artifacts found underwater are located in complex environments with poor imaging conditions. In addition, the artifacts themselves present challenges for automated object detection owing to variations in their shape and texture caused by breakage, stacking, and burial. To solve these problems, this paper proposes an underwater cultural object detection algorithm based on the deformable deep aggregation network model for autonomous underwater vehicle (AUV) exploration. To fully extract the object feature information of underwater objects in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. In addition, the approach also incorporates a BAM module for feature optimization, which enhances the potential feature information of the object while weakening the background interference. Finally, the object prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validated and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, our method has been practically deployed on an AUV. In the field testing over a shipwreck site, the artifact detection frame rate reached up to 18 fps, which satisfies the real-time object detection requirement.

LIU Kezhen, CHEN Xueou, CHEN Leidan, LIN Zheng, SHEN Fu

The proportion of renewable energy in the new power system is further increased, and the grid connected capacity of photovoltaic units has a trend of obvious improvement. The dynamic behavior of the photovoltaic (PV) power generation system at different permeabilities has a significant impact on the load characteristics of the power grid. However, the complex dynamic model of photovoltaic power generation grid connection and the large number of parameters to be identified increase the difficulty of practical application of the model. Therefore, a dynamic discrete equivalent model of the PV power generation model based on the physical model of the PV power generation model is established, and the parameters of the dynamic discrete equivalent model for the PV power generation model are obtained. The IEEE 14-bus system, which is subject to various PV permeabilities, is adopted to verify the superb dynamic characteristics of the proposed discrete equivalent model for the PV power generation in power system simulations. The pertinent simulation results show that the dynamic discrete equivalent model of the PV power generation system can accurately describe the dynamic characteristics of the PV power generation system with a high accuracy and an easy identification performance.

Vladimir Yakimov, Oleg Gaidai, Fang Wang et al.

Floating Production Storage and Offloading Unit (FPSO) is designed to produce, store and transport hydrocarbon products. FPSO's hawsers may be exposed to both extreme and fatigue loads during operations. Hence prediction of their fatigue life is important for operational safety. During some unloading operations, consistent hawser tensions could develop as a result of internal friction in nylon ropes, casing wear and accumulated fatigue damage. Methodology, suggested in this study, may be effectively employed at the vessel design phase, when optimizing vessel parameters, reducing potential FPSO hawser tension fatigue damage. This study aims to contribute to development of novel fatigue assessment approaches, in order to use limited available datasets more effectively. Stresses occurring within FPSO hawsers have been modelled, using actual in situ environmental conditions. Simulated continuous stress time series were used as input for the rainflow counting analysis; the cumulative fatigue damage was then evaluated. Note on experimental validation has been provided.

Juan José Cartelle Barros, María Isabel Lamas Galdo, María Jesús Rodríguez Guerreiro et al.

The design of an artificial reef (AR) module for improving the fishing productivity of cephalopod molluscs in the Ares-Betanzos estuary (Galicia, NW Spain) is addressed in this study. At the time of deciding on a suitable AR design, it is first necessary to assess how the different marine species use ARs so that it is possible to define the complexity of the design: its size and shape, as well as the number of nest cavities it should present and the dimensions of these cavities. Thus, two different cubic modules are proposed, both with an edge of 1500 mm. One of them can be considered as the standard design, while the other has been modified to include four open cylindrical holes. Several tools are employed to assess both proposals. Moreover, a CFD (computational fluid dynamics) model is performed. The results suggest that the flow in the interior of the tubes provides a suitable environment for cephalopod molluscs, given that circulation is produced, guaranteeing nutrient renewal. As further contributions, the present work determines how the capture of cephalopods and other species in Galician fish markets has evolved and reviews the habitat preferred by cephalopods in Galicia. It also proposes and compares two AR modules.

Juanjuan Feng, Jia Li, Wenjie Zhong et al.

Arctic sea ice prediction is of great practical significance in facilitating Arctic route planning, optimizing fisheries management, and advancing the field of sea ice dynamics research. While various deep learning models have been developed for sea ice prediction, they predominantly operate at the seasonal or sub-seasonal scale, often focusing on localized areas, and few cater to full-region daily-scale prediction. This study introduces the use of spatiotemporal sequence data prediction models, namely, the convolutional LSTM (ConvLSTM) and predictive recurrent neural network (PredRNN), for the prediction of sea ice concentration (SIC). Our analysis reveals that, when solely utilizing SIC historical data as the input, the ConvLSTM model outperforms the PredRNN model in SIC prediction. To enhance the models’ capacity to capture spatiotemporal relationships between multiple variables, we expanded the range of input data types to form the ConvLSTM-multi and PredRNN-multi models. Experimental findings demonstrate that the prediction accuracy of the four models significantly surpasses the CMIP6 model in three prospective climate scenarios (SSP126, SSP245, and SSP585). Of the four models, the ConvLSTM-multi model excels in assimilating the influence of reanalysis data on sea ice within the sea ice edge region, thus exhibiting superior performance than the PredRNN-multi model in predicting daily Arctic SIC over the subsequent 10 days. Furthermore, sensitivity tests on various model parameters highlight the substantial impact of sea surface temperature and prediction date on the accuracy of daily sea ice prediction, and meteorological and oceanographic parameters primarily affect the prediction accuracy of the thin-ice region at the edge of the sea ice.

Ammar Nader R., Almas Majid, Nahas Qusai

One potential solution for reducing carbon dioxide emissions from ships and meeting the Energy Efficiency Existing Ship Index (EEXI) requirements is to use a hybrid propulsion system that combines liquid hydrogen and liquefied natural gas fuels. To improve energy efficiency for diesel-electric dual-fuel ship propulsion systems, an engine power limitation system can also be used. This paper examines the potential use of these systems with regard to several factors, including compliance with EEXI standards set by the International Maritime Organization, fuel ratio optimisation, installation requirements, and economic feasibility. As a case study, an LNG carrier is analysed, with dual-fuel diesel-electric and two hybrid systems adjusted to meet IMO-EEXI requirements with engine power limitation percentages of 25%, 0% (hybrid option 1), and 15% (hybrid option 2), respectively. From an economic standpoint, the liquid hydrogen-based system has competitive costs compared to the dual-fuel diesel-electric system, with costs of 2.1 and 2.5 dollars per kilogram for hybrid system options 1 and 2, respectively.

Nelly Elsayed, Zag ElSayed, Anthony S. Maida

Long short-term memory (LSTM) is one of the robust recurrent neural network architectures for learning sequential data. However, it requires considerable computational power to learn and implement both software and hardware aspects. This paper proposed a novel LiteLSTM architecture based on reducing the LSTM computation components via the weights sharing concept to reduce the overall architecture computation cost and maintain the architecture performance. The proposed LiteLSTM can be significant for processing large data where time-consuming is crucial while hardware resources are limited, such as the security of IoT devices and medical data processing. The proposed model was evaluated and tested empirically on three different datasets from the computer vision, cybersecurity, speech emotion recognition domains. The proposed LiteLSTM has comparable accuracy to the other state-of-the-art recurrent architecture while using a smaller computation budget.