Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Francesco Maione, Paolo Lino, Giuseppe Giannino et al.

Catastrophic failures of marine engines imply severe loss of functionality and destroy or damage the systems irreversibly. Being sudden and often unpredictable events, they pose a severe threat to navigation, crew, and passengers. The abrupt nature makes early detection the only effective countermeasure. However, research has concentrated on modeling the gradual degradation of components, with limited attention to sudden and anomalous phenomena. This work proposes a new method for early detection of catastrophic failures. Based on real data from a failed engine, the approach evaluates the derivatives of the deviation between actual sensor readings and expected values of engine variables. Predictions are obtained by a Random Forest, which is the most suitable Machine Learning algorithm among the tested ones. Traditional methods focus on deviations of monitored signals, whereas the proposed approach employs the derivatives of the deviations to provide earlier indications of abnormal dynamics, and to alert that a rapid and dangerous event is breaking out within the system. The method allows the detection of anomalies before measurements reach critical thresholds and alarms are triggered, which is the common method in industry. Consequently, operators can be warned in advance and shut down the engine, then prevent damage and unexpected power loss. Moreover, they have the time to safely change the ship route and avoid potential obstacles. Simulation results conf irm the effectiveness of the proposed approach in anticipating occurrence of catastrophic failures. Validation on real-world data further reinforces the robustness and practical applicability of the method. It is worth noting that data acquisition to train the predictive algorithm is not a problem, since a Deep Learning-based data augmentation procedure is used.

Deming Chen, Mingchen Lin, Jinxin Zhou et al.

The integrated development of offshore wind power and marine aquaculture represents a promising approach to the sustainable utilization of ocean resources. The present study investigated the hydrodynamic response of an innovative combination of a wind farm monopile and pontoon raft aquaculture facilities (PRAFs). Physical water tank experiments were conducted on PRAFs deployed around a wind farm monopile using the following configurations: single- and three-row arrangements of PRAFs with and without a monopile. The interaction between the aquaculture structure and the wind farm monopile was examined, with a particular focus on the mooring line tensions and bridle line tensions under different wave conditions. Utilizing the wind farm monopile foundation as an anchor, the mooring line tension was reduced significantly by 16–66% in the single-row PRAF. The multi-row PRAF arrangement experienced lower mooring line tension in comparison with the single-row PRAF arrangement, with the highest reduction of 73%. However, for the bridle line tension, the upstream component was enhanced, while the downstream one was weakened with a monopile, and they both decreased in the multi-row arrangement. Finally, we developed numerical models based on flume tank tests that examined the interactions between the monopile and PRAFs, including configurations of a single monopile, along with single- and three-row arrangements of PRAFs. The numerical simulation results confirmed that the monopile had a dampening effect on the wave propagation of 5% to 20%, and the impact of the pontoons on the monopile was negligible, implying that the integration of aquaculture facilities around wind farm infrastructure may not significantly alter the hydrodynamic loads experienced by the monopile.

Anatoly A. Krasnovsky

Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often. Model-based analyses can estimate dependability, yet in practice they are tricky to build and keep current because models are typically handcrafted. We claim that a simple connectivity-only topological model - just the service-dependency graph plus replica counts - can provide fast, low-risk availability estimates under fail-stop faults. To make this claim practical without hand-built models, we introduce model discovery: an automated step that can run in CI/CD or as an observability-platform capability, synthesizing an explicit, analyzable model from artifacts teams already have (e.g., distributed traces, service-mesh telemetry, configs/manifests) - providing an accessible gateway for teams to begin resilience testing. As a proof by instance on the DeathStarBench Social Network, we extract the dependency graph from Jaeger and estimate availability across two deployment modes and five failure rates. The discovered model closely tracks live fault-injection results; with replication, median error at mid-range failure rates is near zero, while no-replication shows signed biases consistent with excluded mechanisms. These results create two opportunities: first, to triage and reduce the scope of expensive chaos experiments in advance, and second, to generate real-time signals on the system's resilience posture as its topology evolves, preserving live validation for the most critical or ambiguous scenarios.

Luca Marini

Aquatic bodies face numerous environmental threats caused by several marine anomalies. Marine debris can devastate habitats and endanger marine life through entanglement, while harmful algal blooms can produce toxins that negatively affect marine ecosystems. Additionally, ships may discharge oil or engage in illegal and overfishing activities, causing further harm. These marine anomalies can be identified by applying trained deep learning (DL) models on multispectral satellite imagery. Furthermore, the detection of other anomalies, such as clouds, could be beneficial in filtering out irrelevant images. However, DL models often require a large volume of labeled data for training, which can be both costly and time-consuming, particularly for marine anomaly detection where expert annotation is needed. A potential solution is the use of semi-supervised learning methods, which can also utilize unlabeled data. In this project, we implement and study the performance of FixMatch for Semantic Segmentation, a semi-supervised algorithm for semantic segmentation. Firstly, we found that semi-supervised models perform best with a high confidence threshold of 0.9 when there is a limited amount of labeled data. Secondly, we compare the performance of semi-supervised models with fully-supervised models under varying amounts of labeled data. Our findings suggest that semi-supervised models outperform fully-supervised models with limited labeled data, while fully-supervised models have a slightly better performance with larger volumes of labeled data. We propose two hypotheses to explain why fully-supervised models surpass semi-supervised ones when a high volume of labeled data is used. All of our experiments were conducted using a U-Net model architecture with a limited number of parameters to ensure compatibility with space-rated hardware.

Abi Aryaza, Novanto Yudistira, Tibyani

Marine debris poses significant harm to marine life due to substances like microplastics, polychlorinated biphenyls, and pesticides, which damage habitats and poison organisms. Human-based solutions, such as diving, are increasingly ineffective in addressing this issue. Autonomous underwater vehicles (AUVs) are being developed for efficient sea garbage collection, with the choice of object detection architecture being critical. This research employs the YOLOv4 model for real-time detection of marine debris using the Trash-ICRA 19 dataset, consisting of 7683 images at 480x320 pixels. Various modifications-pretrained models, training from scratch, mosaic augmentation, layer freezing, YOLOv4-tiny, and channel pruning-are compared to enhance architecture efficiency. Channel pruning significantly improves detection speed, increasing the base YOLOv4 frame rate from 15.19 FPS to 19.4 FPS, with only a 1.2% drop in mean Average Precision, from 97.6% to 96.4%.

Pibo Su, Zhongquan Zhao, Kangshou Zhang

The Mesozoic subduction zone over the Dongsha Waters (DSWs) of the South China Sea (SCS) is a part of the westward subduction of the ancient Pacific plate. Based on the comprehensive interpretation of deep reflection seismic profile data and polar magnetic anomaly data, and the zircon dating results of igneous rocks drilled from well LF35-1-1, the Mesozoic subduction zone in the northeast SCS is accurately identified, and a Mesozoic subduction model is proposed. The accretion wedges, trenches, and igneous rock zones together form the Mesozoic subduction zone. The evolution of the Mesozoic subduction zone can be divided into two stages: continental subduction during the Late Jurassic and continental collision during the late Cretaceous. The Mesozoic subduction zone controlled the structural pattern and evolution of the Chaoshan depression (CSD) during the Mesozoic and Neogene eras. The gas source of the hydrate comes from thermogenic gas, which is accompanied by mud diapir activity and migrates along the fault. The gas accumulates to form gas hydrates at the bottom of the stable domain; BSR can be seen above the mud diapir structure; that is, hydrate deposits are formed under the influence of mud diapir structures, belonging to a typical leakage type genesis model.

Yawen He, Yanhua Liu, Duxian Feng et al.

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

Bo Wang, Jie Wang, Chen Zheng et al.

Due to the weak structural features of pipelines and underwater light attenuation, the complex and harsh environment of the seabed greatly increases the possibility of an underwater autonomous remotely operated vehicle losing a detected seabed-contacting segment during pipe-laying operations. To address this problem, we propose a cascade attention module and a prefusion module with a convolutional neural network. The cascade attention module samples the feature maps in a non-convolutional form to realize the interaction between structure and channels, and the attention map is generated by cascading attention. The prefusion module pre-fuses the three layers of feature maps from different stages in the backbone, and the delicate features of the shallow feature maps are fused with the deeper feature maps to generate richer feature maps with space location and semantic classification information. We conduct experiments to verify our modules, both on the underwater pipeline dataset and in a tank test. The results show that our modules can improve the performance of different neural network models for seabed-contacting segment detection. The target detection and instance segmentation performance of the best model is improved through a 6.3% increase in AP and a 3.8% increase in mean intersection over union compared with the baseline model.

Jing Liu, Zongyu Li, Hanming Huang et al.

In response to the deficiencies in existing bridge pier scour protection technologies, this paper introduces a novel protective device, namely a normal distribution-shaped surface (BND) protection devices formed by rotating a concave normal curve. A three-dimensional turbulent SST <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>k</mi><mo>−</mo><mi>ω</mi></mrow></semantics></math></inline-formula> model is constructed, and physical model experiments of conical surfaces are conducted to validate the mathematical model. The simulation analyzes longitudinal water flow, downflow, vorticity intensity, and shear stress within normal and conical surfaces. The results show that the downflow distribution in front of the pier spans a relative water depth of (−0.45, 0.67), with a peak velocity approximately 70% of the longitudinal flow velocity. Circulation forms within the surfaces, with the main vortex flux inside the BND being 33% lower than that inside the conical surface. The maximum shear stress coefficient inside the BND can reach 9, and the protective surface isolates the bed from the flow to prevent scouring by high shear stress. The velocity gradient at the edge of the surface is small, and the edge shear stress of the 3D normal distribution-shaped surface (BND) protection device is only one-third of that of the conical surface, preventing edge scouring. The large shear stress and its distribution area decrease monotonically with the increase in surface width. When the surface width is four times the diameter, the distribution range of the shear stress coefficient greater than 1 is very small. The study of three-Dimensional turbulence within the BND provides a numerical basis for an anti-scour design.

Fan Zhu, Meng Zhang, Fuxuan Ma et al.

Wind turbine towers experience complex dynamic loads during actual operation, and these loads are difficult to accurately predict in advance, which may lead to inaccurate structural fatigue and strength assessment during the structural design phase, thereby posing safety risks to the wind turbine tower. However, online monitoring of wind loads has become possible with the development of load identification technology. Therefore, an identification method for wind load exerted on wind turbine towers was developed in this study to estimate the wind loads using structural strain, which can be used for online monitoring of wind loads. The wind loads exerted on the wind turbine tower were simplified into six equivalent concentrated forces on the topside of the tower, and the initial mathematical model for wind load identification was established based on dynamic load identification theory in the frequency domain, in which many candidate sensor locations and directions were considered. Then, the initial mathematical model was expressed as a linear system of equations. A numerical example was used to verify the accuracy and stability of the initial mathematical model for the wind load identification, and the identification results indicate that the initial mathematical model combined with the Moore–Penrose inverse algorithm can provide stable and accurate reconstruction results. However, the initial mathematical model uses too many sensors, which is not conducive to engineering applications. Therefore, D-optimal and C-optimal design methods were used to reduce the dimension of the initial mathematical model and determine the location and direction of strain gauges. The C-optimal design method adopts a direct optimisation search strategy, while the D-optimal design method adopts an indirect optimisation search strategy. Then, four numerical examples of wind load identification show that dimensionality reduction of the mathematical model leads to high accuracy, in which the C-optimal design algorithm provides more robust identification results. Moreover, the fatigue damage calculated based on the load identification wind loads closely approximates that derived from finite element simulation wind load, with a relative error within 6%. Therefore, the load identification method developed in this study offers a pragmatic solution for the accurate acquisition of the actual wind load of a wind turbine tower.

Adrian Rahmanto Putra, Sol Ha, Kwang-Phil Park

This study proposes to automate the analysis of wiring diagrams to generate cable lists by using machine learning for text classification and pre-trained Deep Neural Network (DNN)-based image classification to detect cable routes. In shipyards, many drawings are produced for each ship, and analyzing these drawings, especially wiring diagrams, to generate cable lists for the Bill of Materials (BOM) can be a time-consuming and error-prone task. This process is performed manually by reading the cable routes and entering the information into a spreadsheet. To address these challenges, this study aims to automate the information extraction from wiring diagrams. The process involves extracting text from the PDF document and classifying it using machine learning, followed by using DNN-based image classification to trace cable routes and identify the relevant annotations. The developed algorithm was tested on three PDF wiring diagram samples and achieved an average accuracy of about 90%, confirming its effectiveness.

Domagoj Baresic, Nishatabbas Rehmatulla, Tristan Smith

The United Kingdom (UK) shipping industry is facing calls to set out more robust decarbonisation plans. In light of the economic challenges facing the country, including the cost-of-living crisis and energy security considerations, the UK government has outlined plans to spearhead several ‘green’ developments. It is of paramount importance to understand how best to integrate the domestic maritime sector into this process by promoting the adoption of low-carbon marine fuels such as hydrogen and ammonia. However, there is a limited understanding of what are the most suitable locations for the early adoption of such fuels in the UK. The sustainability transitions literature offers interesting insights into how marine fuel transitions can unfold, by combining the study of market factors with various non-market socio-technical forces. Previous academic work has shown the importance of location and proximity in facilitating alternative marine fuel transitions. This paper builds onto that work by applying a socio-technical transitions framework to develop a set of indicators to ascertain the suitability of potential locations for the early adoption of hydrogen and ammonia as marine fuels in the UK. This paper explores these dynamics by combining evidence from documentary sources, a UK ship voyages database, and interviews with key stakeholders. Furthermore, three specific case studies are analysed in detail to outline key drivers for the adoption of hydrogen and ammonia. The findings show that there is a significant difference across the UK in regional viability for the early adoption of hydrogen and ammonia, with some of the best suited sites being in the north of Scotland (Orkney), south of England (the Solent-Isle of Wight), and east of England (Felixstowe-Harwich).

Vittorio Cortellessa, Daniele Di Pompeo, Michele Tucci

As organizations increasingly migrate their applications to the cloud, the optimization of microservices architectures becomes imperative for achieving sustainability goals. Nonetheless, sustainable deployments may increase costs and deteriorate performance, thus the identification of optimal tradeoffs among these conflicting requirements is a key objective not easy to achieve. This paper introduces a novel approach to support cloud deployment of microservices architectures by targeting optimal combinations of application performance, deployment costs, and power consumption. By leveraging genetic algorithms, specifically NSGA-II, we automate the generation of alternative architectural deployments. The results demonstrate the potential of our approach through a comprehensive assessment of the Train Ticket case study.

Oliver Karras

In the last two decades, several researchers provided snapshots of the "current" state and evolution of empirical research in requirements engineering (RE) through literature reviews. However, these literature reviews were not sustainable, as none built on or updated previous works due to the unavailability of the extracted and analyzed data. KG-EmpiRE is a Knowledge Graph (KG) of empirical research in RE based on scientific data extracted from currently 680 papers published in the IEEE International Requirements Engineering Conference (1994-2022). KG-EmpiRE is maintained in the Open Research Knowledge Graph (ORKG), making all data openly and long-term available according to the FAIR data principles. Our long-term goal is to constantly maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. Besides KG-EmpiRE, we provide its analysis with all supplementary materials in a repository. This repository contains all files with instructions for replicating and (re-)using the analysis locally or via executable environments and for repeating the research approach. Since its first release based on 199 papers (2014-2022), KG-EmpiRE and its analysis have been updated twice, currently covering over 650 papers. KG-EmpiRE and its analysis demonstrate how innovative infrastructures, such as the ORKG, can be leveraged to make data from literature reviews FAIR, openly available, and maintainable for the research community in the long term. In this way, we can enable replicable, (re-)usable, and thus sustainable literature reviews to ensure the quality, reliability, and timeliness of their research results.

WANG Hanyu, CHEN Zhen, ZHOU Di, CHEN Zhaoxiang, PAN Ershun

In the process of steel rolling, due to wear and other reasons, the working performance of the roll under long and complex working conditions has a gradual decline. Considering the characteristics of complex working conditions and strong random interference of the roll working environment, this paper proposed a kernel-based Wiener process (KWP) degradation model to characterize the strong randomness of the roll degradation trend by using the Wiener process, and to capture the nonlinear degradation path of the roll by using the kerna function. This paper derives the analytical expression of parameter estimation in the Bayesian framework, and constructs the health index of the roll working rotation, then predicts the remaining useful life (RUL) of the roll. In combination with the field data of 1580 hot rolling production line of an iron and steel company, the goodness of fit of the model built is 0.989, and the residual life prediction error is less than 4.7%. Compared with the common machine learning algorithm, it has achieved better results, which is helpful to improve the operating efficiency and safety of equipment and achieve maintenance as needed.

Kohav Dey, Krishna Bajaj, K S Ramalakshmi et al.

Marine ecosystems are vital for the planet's health, but human activities such as climate change, pollution, and overfishing pose a constant threat to marine species. Accurate classification and monitoring of these species can aid in understanding their distribution, population dynamics, and the impact of human activities on them. However, classifying marine species can be challenging due to their vast diversity and the complex underwater environment. With advancements in computer performance and GPU-based computing, deep-learning algorithms can now efficiently classify marine species, making it easier to monitor and manage marine ecosystems. In this paper, we propose an optimization to the MobileNetV2 model to achieve a 99.83% average validation accuracy by highlighting specific guidelines for creating a dataset and augmenting marine species images. This transfer learning algorithm can be deployed successfully on a mobile application for on-site classification at fisheries.

Jing-yun LIU, Yi LIU, Tao SUN et al.

After decades of development, China has gradually developed an independent design capability for underwater weapon power system technologies. However, there is a current need for a wide variety of products and a short research and development cycle; therefore, the original design and development model based on document-based system engineering cannot meet the demand for rapid design. Based on an analysis of the background requirements and current situation of the digital development of model-based system engineering, a three-step digital development scheme is proposed for the power system design of underwater weapons, including technological breakthroughs and solidification of key capabilities, model-driven collaborative design, and construction of digital twin prototypes. The architecture and key technologies of the digital collaborative design platform for the power system of underwater weapons are analyzed; the results can provide a reference for the digital development of power system design for underwater weapons.

Lai Jiang, Jisheng Zhang, Linlong Tong et al.

This paper presents the results from a numerical simulation study to investigate wave trapping by a series of trapezoidal porous submerged breakwaters near a vertical breakwater, as well as the seabed response around the vertical breakwater. An integrated model, based on the volume-averaged Reynolds-averaged Navier–Stokes (VARANS) equations is developed to simulate the flow field, while the dynamic Biot’s equations are used for simulating the wave-induced seabed response. The reflection of the wave energy over the submerged breakwaters, caused by the vertical breakwater, can be reserved, indicating that the existence of the submerged breakwaters in the front of the vertical breakwater can either provide shelter or worsen the hazards to the vertical breakwater. Numerical examples show two different modes under the Fabry–Pérot (F–P) resonance condition of the wave transformation, namely the wave reflection (Mode 1) and the wave trapping (Mode 2). The distance between the submerged breakwaters and the vertical breakwater, is a key parameter dominating the local hydrodynamic process and the resultant dynamic stresses around the vertical breakwater. The numerical results indicated that more submerged breakwaters and a higher porosity of submerged breakwaters will obviously dissipate more wave energy, and hence induce a smaller wave force on the rear vertical breakwater and liquefaction area around the vertical breakwater.

Armin Norouzi, Saeid Shahpouri, David Gordon et al.

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first application, ML is used to identify a model for implementation in model predictive control optimization problems. In the second application, ML is used as a replacement of the NMPC where the ML controller learns the optimal control action by imitating or mimicking the behavior of the model predictive controller. In this study, a deep recurrent neural network including long-short term memory (LSTM) layers are used to model the emissions and performance of an industrial 4.5 liter 4-cylinder Cummins diesel engine. This model is then used for model predictive controller implementation. Then, a deep learning scheme is deployed to clone the behavior of the developed controller. In the LSTM integration, a novel scheme is used by augmenting hidden and cell states of the network in an NMPC optimization problem. The developed LSTM-NMPC and the imitative NMPC are compared with the Cummins calibrated Engine Control Unit (ECU) model in an experimentally validated engine simulation platform. Results show a significant reduction in Nitrogen Oxides (\nox) emissions and a slight decrease in the injected fuel quantity while maintaining the same load. In addition, the imitative NMPC has a similar performance as the NMPC but with a two orders of magnitude reduction of the computation time.

Vaishnavi Khindkar, Janhavi Khindkar

We propose an efficient and generative augmentation approach to solve the inadequacy concern of underwater debris data for visual detection. We use cycleGAN as a data augmentation technique to convert openly available, abundant data of terrestrial plastic to underwater-style images. Prior works just focus on augmenting or enhancing existing data, which moreover adds bias to the dataset. Compared to our technique, which devises variation, transforming additional in-air plastic data to the marine background. We also propose a novel architecture for underwater debris detection using an attention mechanism. Our method helps to focus only on relevant instances of the image, thereby enhancing the detector performance, which is highly obliged while detecting the marine debris using Autonomous Underwater Vehicle (AUV). We perform extensive experiments for marine debris detection using our approach. Quantitative and qualitative results demonstrate the potential of our framework that significantly outperforms the state-of-the-art methods.