Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Ha Vo, Nhut Tran, Khang Vo et al.

In the era of "Software Engineering 2.0" (SE 2.0), where intelligent agents collaborate with human engineers, Generative AI is advancing beyond code generation into Software Architecture (SA). While Large Language Models (LLMs) demonstrate superior capabilities, computational costs and data privacy concerns drive interest in Small Language Models (SLMs) with fewer than 7 billion parameters. However, the reasoning limits of these resource-constrained models remain unexplored. This study benchmarks 10 state-of-the-art SLMs on Architectural Decision Records generation, introducing a multi-dimensional framework evaluating Technical Compliance and Semantic Diversity. Our empirical results reveal a significant reasoning gap: models above the 3B-parameter threshold demonstrate robust zero-shot capabilities, while sub-2B models show the strongest BERTScore gains from Fine-Tuning, though compliance improvements are not guaranteed. Contrary to assumptions regarding context saturation, Few-Shot prompting serves as a highly effective calibration mechanism for select mid-sized models with short context windows. Furthermore, high semantic diversity in off-the-shelf small models often correlates with hallucination rather than productive exploration. These findings establish a rigorous baseline for deploying sustainable, locally hosted architectural assistants.

Julian Frattini, Quim Motger

Natural Language Processing (NLP) tools support requirements engineering (RE) tasks like requirements elicitation, classification, and validation. However, they are often developed from scratch despite functional overlaps, and abandoned after publication. This lack of interoperability and maintenance incurs unnecessary development effort, impedes tool comparison and benchmarking, complicates documentation, and diminishes the long-term sustainability of NLP4RE tools. To address these issues, we postulate a vision to transition from monolithic NLP4RE tools to an ecosystem of reusable, interoperable modules. We outline a research roadmap towards a software reference architecture (SRA) to realize this vision, elaborated following a standard methodological framework for SRA development. As an initial step, we conducted a stakeholder-driven focus group session to elicit generic system requirements for NLP4RE tools. This activity resulted in 36 key system requirements, further motivating the need for a dedicated SRA. Overall, the proposed vision, roadmap, and initial contribution pave the way towards improved development, reuse, and long-term maintenance of NLP4RE tools.

Hung N. Do, Jessica Z. Kubicek-Sutherland, S. Gnanakaran

Access to the most up-to-date information on medical countermeasures is important for the research and development of effective treatments for viruses and marine toxins. However, there is a lack of comprehensive databases that curate data on viruses and marine toxins, making decisions on medical countermeasures slow and difficult. In this work, we employ two large language models (LLMs) of ChatGPT and Grok to design two comprehensive databases of therapeutic countermeasures for five viruses of Lassa, Marburg, Ebola, Nipah, and Venezuelan equine encephalitis, as well as marine toxins. With high-level human-provided inputs, the two LLMs identify public databases containing data on the five viruses and marine toxins, collect relevant information from these databases and the literature, iteratively cross-validate the collected information, and design interactive webpages for easy access to the curated, comprehensive databases. Notably, the ChatGPT LLM is employed to design agentic AI workflows (consisting of two AI agents for research and decision-making) to rank countermeasures for viruses and marine toxins in the databases. Together, our work explores the potential of LLMs as a scalable, updatable approach for building comprehensive knowledge databases and supporting evidence-based decision-making.

Afzal Machingal

Two of the most environmentally critical and operationally challenging maritime environments anywhere in the world are the Red Sea and the Arabian Gulf. The shallow waters and high salinity levels, along with unprecedented sea surface temperatures and growing traffic from commercial vessels, are presenting major challenges to conventional design concepts that are highly unlikely to overcome the challenges for ship designs. In addition, there are global environmental statutes and regional sustainability agendas, especially within the Middle East, which put a greater focus on greener and more efficient maritime practices. This paper presents an examination of sustainable ship design concepts, focusing specifically on the environment of the Red Sea and the Arabian Gulf. The present paper critically reviews major dimensions of ship design, such as ship optimization, propulsion systems, energy-efficient technologies, alternative fuels, and environmentally adaptable materials, through a comprehensive review of recent developments in naval architecture with an analytical synthesis of advances made in marine engineering and sustainable technologies. Furthermore, this research also identifies the applicability of new concepts of sustainable ship design regarding international maritime legislation and regional policy, considering sustainability factors involved with regional economic and environmental growth. The research shows that fuel efficiency, emission reduction, and consequently environmental protection could be substantially achieved with regionally tailored design approaches, along with improved ships' operational performance and reliability. A conceptual framework for sustainable ship design, incorporating environmental constraints, adopting technological advancement, and adhering to regulations, has been presented in the paper.

Jun Li, Kai Zhan, Ming Zhang et al.

The conveying hose is an important piece of equipment in the field of Marine engineering. Its spatial configuration and force conditions affect the normal operation of the Marine engineering system. This paper proposes a flexible, fully hose-based conveyance method for the field of deep-sea mining and mainly uses Orcaflex software to simulate and analyze the characteristics of the conveying hose in this system. This paper studies the influences of the top spacing, incoming flow direction, and placement and recovery processes on the configuration characteristics and force conditions of the hose. The conclusion drawn is that the conveying hose studied in this paper can maintain a good spatial configuration underwater and has a stable force condition. When the top spacing is 20 m, the transition of the curved section at the bottom of the hose is relatively smooth. The top tension of the hose has a good adaptability to the top spacing and the direction of the incoming flow. The conveying hose can stably complete the deployment and recovery operations.

Qunpeng WANG, Longhao LI, Hongxu GUAN et al.

Objectives To make local path planning algorithms more consistent with the maneuvering characteristics of ships, thereby generating safer and more reliable reference paths, this paper proposes a three-dimensional potential field modeling method. MethodsBy converting the Cartesian coordinate system to the ellipsoidal coordinate system, it addresses the anisotropy problem of the potential field distribution function. The ship’s potential energy distribution function is calculated by solving the Laplace equation. A control framework combining the potential field model and model predictive control (MPC) algorithm is designed to enhance the adaptability of dynamic real-time local path planning for ships in different scenarios. Simulations are conducted with actual navigation vessels in the waters of the Sutong Yangtze River Highway Bridge area. The three-dimensional potential field model is used to obtain local reference paths, and the MPC algorithm is employed for ship path tracking control simulation experiments.ResultsAs the results show, compared to reference paths generated by traditional and improved artificial potential field methods, the three-dimensional potential field model’s local reference paths are superior in terms of length, curve smoothness, maximum steering angle, and average absolute heading error. This model can generate shorter and smoother local paths which are more consistent with the actual maneuvering habits of ships and exhibit less jitter in traffic-intensive scenarios. ConclusionsThis study demonstrates that the local reference paths generated by the three-dimensional potential field model can effectively identify the target ship’s steering angles and differences in ship scale, reduce reliance on the number of surrounding target ships, and effectively capture the interactions between ship agents, thus showing high reliability.

Olga Szulecka, Tomasz Kulikowski, Adam Mytlewski et al.

Pelagic fish side-streams are nutritionally rich raw materials that are currently utilised for fishmeal, fish oil, and pet food. This study examined the motivators and barriers to using fish processing side-streams for human consumption in Poland and Norway. In Norway, the prospect of increased revenue and profit emerged as the primary motivator for enhanced side-stream utilization, whereas in Poland the highest-rated motivators were environmental benefits and waste reduction. Market-related factors were identified as the main barrier in Poland, whereas financial constraints were the primary issue in Norway. Industry representatives confirmed the technical feasibility of recovering valuable nutrients from side-streams but emphasized the need for proven technologies, investment security, and sufficient market demand to justify the new initiatives.

María José Pérez-Molina, José A. Carta

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (<i>MAE</i>) of 2.56 kW/m and a root mean square error (<i>RMSE</i>) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an <i>MAE</i> of 4.38 kW/m and an <i>RMSE</i> of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data.

Guangjie Fang, Jun Liang, Rijin Jiang et al.

Beta diversity is an important way to analyze community assembly mechanisms in different habitats or along environmental gradients. However, research on marine fish assemblages around islands has lagged, especially for functional beta diversity. In this study, we evaluated taxonomic and functional beta diversity change of island fish assemblages along the coast in two seasons and revealed its relationship with environmental factors and geographical distance. Taxonomic and functional beta diversity were both dominated by turnover (over 80% and 60%), while the contribution of nestedness on functional beta diversity was significantly increased. Environmental factors such as temperature and dissolved oxygen were important drivers of beta diversity rather than geographical distance. Fish assemblages around islands that are far away from mainlands or affected greatly by anthropogenic activities usually have higher beta diversity. These results indicated that environmental filtration is the primary factor driving the mechanism of fish community assembly. Our study revealed the importance of the integrated application of two facets of biodiversity to investigate beta diversity. The findings can provide theoretical support for the protection of marine fish and the planning of marine protected areas in the future.

Tianjun Li, Yifei Zhao, Lizhu Wang et al.

Understanding the morphodynamic evolution of muddy coasts under complex wave–tidal forcing is crucial for effective coastal management, particularly under the unstable hydrodynamic conditions associated with global climate change. This study employs a one-dimensional Delft3D model to investigate a tidal flat north of Dafeng Port, Jiangsu Province, China, validated with multi-year profile measurements. Under typical conditions, the profile consistently exhibits upper-flat accretion and lower-flat erosion, with threshold values of Hs ≈ 1.2 m and Tp ≈ 4.5 s triggering nonlinear bed-level changes. During storm tides, the profile displays a distinct upper flood-tide and lower ebb-tide response. Long-term simulations suggest that the profile will likely reach dynamic equilibrium by 2026. Overall, this study demonstrates the capability of one-dimensional modeling to capture nonlinear tidal flat evolution and provides valuable insights into the spatially variable morphodynamics of muddy coasts for adaptive management.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

YuK-Kwan Wong, Tuan-An To, Jipeng Zhang et al.

We have witnessed promising progress led by large language models (LLMs) and further vision language models (VLMs) in handling various queries as a general-purpose assistant. VLMs, as a bridge to connect the visual world and language corpus, receive both visual content and various text-only user instructions to generate corresponding responses. Though great success has been achieved by VLMs in various fields, in this work, we ask whether the existing VLMs can act as domain experts, accurately answering marine questions, which require significant domain expertise and address special domain challenges/requirements. To comprehensively evaluate the effectiveness and explore the boundary of existing VLMs, we construct the first large-scale marine VLM dataset and benchmark called MarineEval, with 2,000 image-based question-answering pairs. During our dataset construction, we ensure the diversity and coverage of the constructed data: 7 task dimensions and 20 capacity dimensions. The domain requirements are specially integrated into the data construction and further verified by the corresponding marine domain experts. We comprehensively benchmark 17 existing VLMs on our MarineEval and also investigate the limitations of existing models in answering marine research questions. The experimental results reveal that existing VLMs cannot effectively answer the domain-specific questions, and there is still a large room for further performance improvements. We hope our new benchmark and observations will facilitate future research. Project Page: http://marineeval.hkustvgd.com/

Chunfeng Huang, Rui Guan, Xin Liu et al.

Quantum key distribution (QKD) is a key application in quantum communication, enabling secure key exchange between parties using quantum states. Twin-field (TF) QKD offers a promising solution that surpasses the repeaterless limits, and its measurement-device-independent nature makes it suitable for star-type network architectures. In this work, we propose a scalable TF-QKD network with adaptable architecture, where users prepare quantum signals and send them to network nodes. These nodes use an optical switch to route the signals to multi-user measurement units, enabling secure key distribution among arbitrary users and adapting to complex connection demands of the network. A proof-of-principle demonstration with three users successfully achieved secure key sharing over simulated link losses of up to $30$ dB, with an average rate of $19.57$ bit/s. Additionally, simulations show that the proposed architecture can achieve a total secure key rate of $4.84 \times 10^{4}$ bit/s at $100$ km in a symmetric $32$-user network. This approach represents a significant advancement in the topology of untrusted-node QKD networks and holds promise for practical, large-scale applications in secure communication.

Heng Liao, Bingyang Liu, Xianping Chen et al.

As the Large-scale Language Models (LLMs) continue to scale, the requisite computational power and bandwidth escalate. To address this, we introduce UB-Mesh, a novel AI datacenter network architecture designed to enhance scalability, performance, cost-efficiency and availability. Unlike traditional datacenters that provide symmetrical node-to-node bandwidth, UB-Mesh employs a hierarchically localized nD-FullMesh network topology. This design fully leverages the data locality of LLM training, prioritizing short-range, direct interconnects to minimize data movement distance and reduce switch usage. Although UB-Mesh's nD-FullMesh topology offers several theoretical advantages, its concrete architecture design, physical implementation and networking system optimization present new challenges. For the actual construction of UB-Mesh, we first design the UB-Mesh-Pod architecture, which is based on a 4D-FullMesh topology. UB-Mesh-Pod is implemented via a suite of hardware components that serve as the foundational building blocks, including specifically-designed NPU, CPU, Low-Radix-Switch (LRS), High-Radix-Switch (HRS), NICs and others. These components are interconnected via a novel Unified Bus (UB) technique, which enables flexible IO bandwidth allocation and hardware resource pooling. For networking system optimization, we propose advanced routing mechanism named All-Path-Routing (APR) to efficiently manage data traffic. These optimizations, combined with topology-aware performance enhancements and robust reliability measures like 64+1 backup design, result in 2.04x higher cost-efficiency, 7.2% higher network availability compared to traditional Clos architecture and 95%+ linearity in various LLM training tasks.

J. Martín-Núñez, A. Ar, R. Fernández et al.

The concept of Artificial Intelligence (AI), born as the possibility of simulating the human brain's learning capabilities, quickly evolves into one of the educational technology concepts that provide tools for students to better themselves in a plethora of areas. Unlike the previous educational technology iterations, which are limited to instrumental use for providing platforms to build learning applications, AI has proposed a unique education laboratory by enabling students to explore an instrument that functions as a dynamic system of computational concepts. However, the extent of the implications of AI adaptation in modern education is yet to be explored. Motivated to fill the literature gap and to consider the emerging significance of AI in education, this paper aims to analyze the possible intertwined relationship between students’ intrinsic motivation for learning Artificial Intelligence during the COVID-19 pandemic; the relationship between students’ computational thinking and understanding of AI concepts; and the underlying dynamic relation, if existing, between AI and computational thinking building efforts. To investigate the mentioned relationships, the present empirical study employs mediation analysis based upon collected 137 survey data from Universidad Politécnica de Madrid students in the Institute for Educational Science and the School of Naval Architecture and Marine Engineering during the first quarter of 2022. Findings show that intrinsic motivation mediates the relationship between perceived Artificial Intelligence learning and computational thinking. Also, the research indicates that intrinsic motivation has a significant relationship with computational thinking and perceived Artificial Intelligence learning.

M. Abe, S. Abe, J. Abhir et al.

Axion-like particles (ALPs) are pseudo-Nambu-Goldstone bosons that emerge in various theories beyond the standard model. These particles can interact with high-energy photons in external magnetic fields, influencing the observed gamma-ray spectrum. This study analyzes 41.3 hrs of observational data from the Perseus Galaxy Cluster collected with the MAGIC telescopes. We focused on the spectra the radio galaxy in the center of the cluster: NGC 1275. By modeling the magnetic field surrounding this target, we searched for spectral indications of ALP presence. Despite finding no statistical evidence of ALP signatures, we were able to exclude ALP models in the sub-micro electronvolt range. Our analysis improved upon previous work by calculating the full likelihood and statistical coverage for all considered models across the parameter space. Consequently, we achieved the most stringent limits to date for ALP masses around 50 neV, with cross sections down to $g_{a\gamma} = 3 \times 10^{-12}$ GeV$^{-1}$.

M. Abe, J. Abhir, A. Abhishek et al.

The Lorentz Invariance Violation (LIV), a proposed consequence of certain quantum gravity (QG) scenarios, could instigate an energy-dependent group velocity for ultra-relativistic particles. This energy dependence, although suppressed by the massive QG energy scale E_QG, expected to be on the level of the Planck energy 1.22 × 1019 GeV, is potentially detectable in astrophysical observations. In this scenario, the cosmological distances traversed by photons act as an amplifier for this effect. By leveraging the observation of a remarkable flare from the blazar Mrk 421, recorded at energies above 100 GeV by the MAGIC telescopes on the night of April 25 to 26, 2014, we look for time delays scaling linearly and quadratically with the photon energies. Using for the first time in LIV studies a binned-likelihood approach we set constraints on the QG energy scale. For the linear scenario, we set 95% lower limits E_QG>2.7×1017 GeV for the subluminal case and E_QG> 3.6 ×1017 GeV for the superluminal case. For the quadratic scenario, the 95% lower limits for the subluminal and superluminal cases are E_QG>2.6 ×1010 GeV and E_QG>2.5×1010 GeV, respectively.

Lin Ke, Jinming Ye, Wei He

To assess the effectiveness of subgrid scale (SGS) models on the prediction results of unsteady loads and turbulent fluctuation of pumpjet propulsors equipped with both front and rear stators, a pumpjet propulsor computational model with attached parts at the model scale is developed using a fully structured mesh, and large eddy simulations are conducted. The computational results of the different SGS models are compared based on five aspects: open water characteristics, turbulence parameters, incoming turbulence spectrum, vortex structure, and fluctuating pressure. Their results are also compared with the experimental values, and the correlation between the internal flow characteristics of the pumpjet propulsor and the turbulent fluctuation is analyzed. According to the results, as regards the prediction of the open water performance of the pumpjet propulsor containing both front and rear stators, the overall trend obtained by the three subgrid models is similar, and the error between the values predicted by the SL model and the experimental ones is the smallest. At the same mesh level, the turbulent fluctuating scale obtained by the SL model is larger than that obtained by the WALE and DSL models, and the turbulent time scale obtained by the DSL model has the smallest fluctuation in the circumferential direction. Among the three SGS models, the turbulent fluctuating scale of the SL model is larger than those of the WALE and DSL models. The SL model exhibits the largest energy dissipation among the three SGS models, followed by the DSL model, while that of the WALE model is the smallest. In the WALE model, the leakage vortex at the top of the blade is the longest, followed by the DSL model, while it is the shortest in the SL model. In the WALE and DSL models, the fluctuating load fluctuates more in the transition region from the middle section to the trailing edge of the blade.

Hao WU, Ziye YANG, Jianxin CAO et al.

ObjectivesThis paper explores the effects of air flow rate and sailing angle on the air layer retention and energy efficiency of the bottom groove of a ship, focusing on a large scale model of a bulk carrier. MethodsAn air layer drag reduction self-propelled model system and hull cavity scheme are designed, and drag reduction experiments are conducted under open water conditions. The jet drag reduction effect on the model in a positive floating attitude of ship is examined, as well as the impact of a certain trim angle on the speed and shaft power of the model. Results The results indicate that, when the main engine speed is constant, air injection can significantly improve the speed of model; after stopping the jet, the air layer within the air cavity of the bottom groove can be maintained for a long time, with better drag reduction efficiency when the ship is in an upright state and the trimming is within 0.25 degrees. When the trimming angle is too large, the gas will overflow from both sides of the model head and the air layer will not effectively cover the bottom of the ship, decreasing the efficiency of drag reduction. ConclusionsSeveral meaningful conclusions are obtained from the above experiments, providing useful references for the engineering application of air layer drag reduction technology on full-formed ships.

Hao Guo, Zhongcheng Wang, Song Zhou et al.

With increasing environmental pollution from ship exhaust emissions and increasingly stringent International Maritime Organization carbon regulations, there is a growing demand for cleaner and lower-carbon fuels and near-zero-emission marine engines worldwide. Liquefied natural gas is a low-carbon fuel, and when liquefied natural gas (LNG) is used on ships, dual-fuel methods are often used. The pre-chamber plays a key role in the working process of dual-fuel engines. In this paper, an effective three-dimensional simulation model based on the actual operating conditions and structural characteristics of a marine low-pressure dual-fuel engine is established. In addition, the effects of changing the Precombustion chamber (PCC) volume ratio and the PCC orifice diameter ratio on the mixture composition, engine combustion performance, and pollutant generation were thoroughly investigated. It was found that a small PPC volume ratio resulted in a higher flame jet velocity, a shorter stagnation period, and an acceleration of the combustion process in the main combustion chamber. When the PCC volume was large, the Nitrogen oxygen (NOx) ratio emission was elevated. Moreover, the angle of the PCC orifice affected the flame propagation direction of the pilot fuel. Optimizing the angle of the PCC orifice can improve combustion efficiency and reduce the generation of NOx. Furthermore, reasonable arrangement of the PCC structure can improve the stability of ignition performance and accelerate the flame jet velocity.