Hasil untuk "Naval architecture. Shipbuilding. Marine engineering"

Jinwei Chen, Zhenchao Hu, Huisheng Zhang

The accurate prediction of gas turbine output power is critical for flexible scheduling and shipboard microgrid resilience. However, purely data-driven models suffer from poor generalization and physical inconsistency in complex marine environments, especially under unseen operation conditions. This paper proposes a condition-adaptive physics-informed long short-term memory (CAPI-LSTM) framework to ensure physical consistency across the full operation envelope. In the proposed framework, an MLP-based condition-adaptive regulator is developed to dynamically adjust the compressor air flow rate within the embedded physics-informed loss function. The proposed CAPI-LSTM model is verified using the operation data from an LM2500+ gas turbine. The comparison results demonstrate the superiority of the proposed method over traditional architectures. The CAPI-LSTM model achieves the lowest root mean square error of 0.177 MW, and its error distribution is the most concentrated near zero among all compared models. The robustness of the CAPI-LSTM model is further verified under the unseen operation conditions. The CAPI-LSTM still maintains excellent generalization capability compared to both purely data-driven models and standard physics-informed models, with an average error of only 0.218 MW and a narrow interquartile range of [0.058, 0.363]. The paired <i>t</i>-test results confirm that the improvement of the CAPI-LSTM model is statistically significant. The CAPI-LSTM model achieves competitive computational efficiency despite the integration of the physics-informed loss function with a condition-adaptive regulator. Furthermore, the CAPI-LSTM model achieves superior performance in noise immunity and transferability to other types of gas turbines. In summary, the proposed CAPI-LSTM model provides an effective and practical solution for marine gas turbine output power prediction.

Chengming Qin, Zhe Chen, Yanping He et al.

For deep-draft cylindrical platforms with a large annular column boot, the influence of the column boot diameter-to-height ratio (d/h) on motion performance remains unclear. This study investigates the effect of d/h on platform hydrodynamics while keeping the main body geometry, displacement, and draft unchanged. A hybrid numerical model validated against tests is adopted: STAR-CCM+ free-decay simulations identify equivalent linear damping, and ANSYS AQWA predicts hydrodynamic coefficients, response amplitude operators, and coupled time-domain responses under a 100-year survival sea state in the western South China Sea. Increasing d/h substantially increases heave added mass and added pitch moment of inertia, leading to longer natural periods and higher damping in heave and pitch. However, its effect on motion responses is non-monotonic and strongly response-dependent. As d/h increases, the responses are initially reduced markedly. The minimum surge and heave responses occur at d/h = 2.39 and 4.67, with reductions of about 34.0% and 87.2%, respectively, while the pitch response is already reduced by about 67.3% at d/h = 7.22. Further increases in d/h may weaken surge and heave mitigation while providing limited additional benefit for pitch. These findings provide qualitative understanding and quantitative guidance for response-oriented column boot design and optimization of similar platforms.

Fabian Stiehle, Markus Funke, Patricia Lago et al.

The digital markets act (DMA) regulates very large digital platforms like Meta's Facebook or Apple's iOS with the goal to promote fairness, contestability (of market power) and user choice. From a system design or broader technical perspective, the implications of the DMA have not been studied so far. Using systematic methods from qualitative coding and thematic analysis, we investigate the DMA from a technical perspective and derive eight high-level design strategies that serve as fundamental approaches towards value-based architectural goals like 'fair practice', or 'user choice' (as envisioned by the DMA). We investigate how compliance with the DMA has been achieved and derive 15 tactics that we map to our strategies. While the DMA obligations challenge existing platform designs, they also create new opportunities for designing services within these huge ecosystems. We, thus, discuss our strategies in light of both. We see this work as a first step towards filling this pressing gap in the architecture of platform ecosystems, i.e., how to incorporate abstract human values in architecture design.

Yucheng Yan, Zhichao Chen, Diju Gao

Hybrid power technology for ships is an effective way to promote the green and low-carbon development of the maritime industry. The development of pattern recognition technology provides new research ideas for the rational allocation and utilization of energy in hybrid power ships. To reduce fuel consumption, a nonlinear model predictive control energy management strategy based on working condition identification is proposed for optimal energy management to solve the problem of real-time optimal adjustment of generators and batteries. The core of the strategy is to identify the ship’s working conditions and the nonlinear model predictive control algorithm. Firstly, to achieve the working condition identification task, a ship working condition dataset based on a hybrid supply power ship data is constructed. The labeled dataset is trained using deep learning techniques. Secondly, based on the identification results, a nonlinear model predictive control algorithm is designed to adjust the generator speed and the battery current to achieve energy optimization control under constraints. Finally, the effectiveness of the proposed strategy in optimizing energy control and reducing fuel consumption is verified through simulation. The proposed strategy can reduce the generator fuel consumption by 5.5% under no noise disturbance when compared with conventional predictive control. Under 10% noise disturbance, it is still able to reduce the fuel consumption by 2.6%.

Mikaël Grondeau, Sylvain S. Guillou

As with wind turbines, marine tidal turbines are expected to be deployed in arrays of multiple turbines. To optimize these arrays, a more profound understanding of the interactions between turbines is necessary. This paper employs the Actuator Line Method alongside the Lattice Boltzmann Method and Large Eddy Simulation to develop a numerical model of tidal turbine arrays. It studies a vertical-axis turbine manufactured by HydroQuest/CMN that is equipped with two counter-rotating columns, each comprising two rotors. The ambient turbulence and upstream velocity profiles correspond to the characteristics of a tidal site such as the Alderney Race. Six turbine layouts are modeled: three aligned layouts with three turbines and three staggered layouts with four turbines. The spacing between turbines varies depending on the layout. This study yields several observations regarding array configuration. A minimum distance of 300 m, or <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>12</mn><msub><mi>D</mi><mrow><mi>e</mi><mi>q</mi></mrow></msub></mrow></semantics></math></inline-formula>, between aligned turbines is necessary for full wake recovery. At shorter distances, the accumulation of velocity deficits significantly decreases the efficiency of the third turbine in the array. Pairs of counter-rotating vortices are observed in the wake of turbines. The evolution of these vortices and their influence on the wake depend greatly on the array configuration. An optimal configuration is observed in which the overall averaged power is not impaired by the interactions.

Yanzhao Jia, Zhaobo Wu, Zheyi Cao et al.

This paper introduces a novel disk array architecture, designated RAID-0e (Resilient Striping Array), designed to superimpose a low-overhead fault tolerance layer upon traditional RAID 0 (striping). By employing a logically and physically separate parity domain to protect a primary data domain, RAID-0e mitigates the risk of array-wide data loss from common, non-catastrophic media failures, such as isolated bad blocks, transient read errors, or sector-level corruption. The architecture is engineered to preserve the intrinsic read performance advantages of RAID 0 while significantly enhancing data availability and operational resilience. This document provides a comprehensive exposition of the architectural principles, operational workflows, performance characteristics, failure mode analysis, and security considerations of RAID-0e. It is presented as an experimental yet pragmatic solution for environments seeking a new equilibrium between I/O performance, storage cost, and data resilience, particularly where full drive failure is a secondary concern to media degradation.

Yuang Chen, Chengdi Lu, Yongsheng Huang et al.

The dispersed node locations and complex topologies of edge networks, combined with intricate dynamic microservice dependencies, render traditional centralized microservice architectures (MSAs) unsuitable. In this paper, we propose a decentralized microservice architecture (DMSA), which delegates scheduling functions from the control plane to edge nodes. DMSA redesigns and implements three core modules of microservice discovery, monitoring, and scheduling for edge networks to achieve precise awareness of instance deployments, low monitoring overhead and measurement errors, and accurate dynamic scheduling, respectively. Particularly, DMSA has customized a microservice scheduling scheme that leverages multi-port listening and zero-copy forwarding to guarantee high data forwarding efficiency. Moreover, a dynamic weighted multi-level load balancing algorithm is proposed to adjust scheduling dynamically with consideration of reliability, priority, and response delay. Finally, we have implemented a physical verification platform for DMSA. Extensive empirical results demonstrate that compared to state-of-the-art and traditional scheduling schemes, DMSA effectively counteracts link failures and network fluctuations, improving the service response delay and execution success rate by approximately $60\% \sim 75\%$ and $10\%\sim15\%$, respectively.

Laibin Chang, Yunke Wang, JiaXing Huang et al.

Marine Saliency Segmentation (MSS) plays a pivotal role in various vision-based marine exploration tasks. However, existing marine segmentation techniques face the dilemma of object mislocalization and imprecise boundaries due to the complex underwater environment. Meanwhile, despite the impressive performance of diffusion models in visual segmentation, there remains potential to further leverage contextual semantics to enhance feature learning of region-level salient objects, thereby improving segmentation outcomes. Building on this insight, we propose DiffMSS, a novel marine saliency segmenter based on the diffusion model, which utilizes semantic knowledge distillation to guide the segmentation of marine salient objects. Specifically, we design a region-word similarity matching mechanism to identify salient terms at the word level from the text descriptions. These high-level semantic features guide the conditional feature learning network in generating salient and accurate diffusion conditions with semantic knowledge distillation. To further refine the segmentation of fine-grained structures in unique marine organisms, we develop the dedicated consensus deterministic sampling to suppress overconfident missegmentations. Comprehensive experiments demonstrate the superior performance of DiffMSS over state-of-the-art methods in both quantitative and qualitative evaluations.

Kaixuan Yan, Jie Chen, Tao Zhang

Magmatic and tectonic processes in the formation of oceanic lithosphere at slow–ultraslow-spreading mid-ocean ridges (MORs) are more complicated relative to faster-spreading ridges, as their melt flux is overall low, with highly spatial and temporal variations. Here, we use the teleseismic catalog of magnitudes over 4 between 1995 and 2020 from the International Seismological Center to investigate the characteristics of magmatic and tectonic activities at the ultraslow-spreading Southwest Indian Ridge and Arctic Gakkel Ridge and the slow-spreading North Mid-Atlantic Ridge and Carlsberg Ridge (total length of 14,300 km). Using the single-link cluster analysis technique, we identify 78 seismic swarms (≥8 events), 877 sequences (2–7 events), and 3543 single events. Seismic swarms often occur near the volcanic center of second-order segments, presumably relating to relatively robust magmatism. By comparing the patterns of seismicity between ultraslow- and slow-spreading ridges, and between melt-rich and melt-poor regions of the Southwest Indian Ridge with distinct seafloor morphologies, we demonstrate that a lower spreading rate and a lower melt supply correspond to a higher seismicity rate and a higher potential of large volcano-induced seismic swarms, probably due to a thicker and colder lithosphere with a higher degree of along-axis melt focusing there.

Filip Novák, Tomáš Báča, Ondřej Procházka et al.

A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation and prediction approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

Yi Zheng

By using PTP and DEA methods, the study of marine fishing capacity in China revealed that it's more accurate to consider the input index as total power rather than the number of ships. The analysis clarified that the main issue with marine fishing capacity in China is the excess total power of fishing ships. The study suggested reducing the number of ships by 35.2%, the gross tonnage by 29.8%, and the total power by 37.3% to align with the catch levels of 1999. It also proposed the idea of supplementing peak years. The PTP methodology was found to be suitable for longitudinal analysis over time, while the DEA approach is better for comparing fishing capacity across different regions at the same time. Additionally, the study indicated that practical fishing capacity tends to be underestimated, so actual reductions in capacity are usually larger than calculated values.

Jiqing Du, Dan Zhou, Wei Wang et al.

The Deep Reinforcement Learning (DRL) algorithm is an optimal control method with generalization capacity for complex nonlinear coupled systems. However, the DRL agent maintains control command saturation and response overshoot to achieve the fastest response. In this study, a reference model-based DRL control strategy termed Model-Reference Twin Delayed Deep Deterministic (MR-TD3) was proposed for controlling the pitch attitude and depth of an autonomous underwater vehicle (AUV) system. First, a reference model based on an actual AUV system was introduced to an actor–critic structure, where the input of the model was the reference target, the outputs were the smoothed reference targets, and the reference model parameters can adjust the response time and the smoothness. The input commands were limited to the saturation range. Then, the model state, the real state and the reference target were mapped to the control command through the Twin Delayed Deep Deterministic (TD3) agent for training. Finally, the trained neural network was applied to the AUV system environment for pitch and depth experiments. The results demonstrated that the controller can eliminate the response overshoot and control command saturation while improving the robustness, and the method also can extend to other control platforms such as autonomous guided vehicle or unmanned aerial vehicle.

Chao Fu, Kuan Lu, Qian Li et al.

The engine system is critical for a marine vehicle, and its performance significantly affects the efficiency and safety of the whole ship. Due to the harsh working environment and the complex system structure, a marine system is prone to have many kinds of novelties and faults. Timely detection of faults via effective condition monitoring is vital for such systems, avoiding serious damage and economic loss. However, it is difficult to realize online monitoring because of the limitations of measurement and health monitoring methods. In this paper, a marine engine system simulator is set up with enhanced sensory placement for static and dynamic data collection. The test rig and processing for static and dynamic data are described. Then, a physics-based multivariate modeling method is proposed for the health monitoring of the system. Case studies are carried out considering the misfire fault and the exhaust valve leakage fault. In the misfire fault test, the exhaust gas temperature of the misfired cylinder dropped from the confidence interval 100–150 °C to 70–80 °C and the head vibration features decreased from the confidence interval 900–1300 m/s² to around 200–300 m/s². For the exhaust valve leakage fault, the engine body vibration main bearing impact RMS increased nearly 10 times. Comparisons between the model-predicted confidence interval and measured data reveal that the proposed model based on the fault-related static and dynamic features successfully identified the two faults and their positions, proving the effectiveness of the proposed framework.

Romina Eramo, Michele Tucci, Daniele Di Pompeo et al.

The continuous software engineering paradigm is gaining popularity in modern development practices, where the interleaving of design and runtime activities is induced by the continuous evolution of software systems. In this context, performance assessment is not easy, but recent studies have shown that architectural models evolving with the software can support this goal. In this paper, we present a mapping study aimed at classifying existing scientific contributions that deal with the architectural support for performance-targeted continuous software engineering. We have applied the systematic mapping methodology to an initial set of 215 potentially relevant papers and selected 66 primary studies that we have analyzed to characterize and classify the current state of research. This classification helps to focus on the main aspects that are being considered in this domain and, mostly, on the emerging findings and implications for future research

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Valery Bondur, Vladimir Dulov, Vladimir Kozub et al.

A method for retrieving 2D spatial spectra of sea wave elevations and slopes from high resolution (about 1 m) satellite imagery has been developed that also allows for assessing sea wave angular distributions. A validation of the suggested method was carried out based on the results from a comprehensive experiment that included both satellite imaging of the Black Sea water area and sea truth under controlled conditions. The retrieval of spatial wave spectra from fragments of a satellite image and comparison with the results of measuring the frequency spectra from sea truth data obtained using an array of string wave recorders were carried out. Wave spectra from remote and in situ data are consistent in the frequency range of 0.2–1.1 Hz, corresponding to wavelengths from 1.3 to 39 m.

Van Chien Pham, Hanseok Kim, Jae-Hyuk Choi et al.

Recent developments in power electronics, energy storage systems, and renewable energy; increased market demands for more efficient and cleaner electric power to meet stricter environmental regulations; and development in gigawatt (GW)-class DC (direct current) transmission systems for transmission of greater power over longer distances than similar alternative current (AC) systems, have supported the development of the DC grid, making it a promising solution for both the onshore and offshore industries. This paper presents an experimental study on the effectiveness of an engine speed reduction strategy on exhaust gas emission and fuel consumption when applied to a 4-stroke generator engine equipped with a cam-driven plunger diesel injection system. The experiments were performed on an 8-cylinder V-type 4-stroke generator engine installed in the MASTC laboratory, which is the only demonstration testbed for the ship’s electric propulsion system in Korea. Experimental results showed that fuel consumption decreased, but emission mass fraction in exhaust gas increased when maintaining engine power while reducing engine speed. This study has shown economic benefits in reducing fuel consumption, but incurred penalties for the emission performance of 4-stroke generator engines equipped with cam-driven plunger diesel injection systems when applying the engine speed reduction strategy.

Haopeng Zeng, Yu Zhang, Lei Zhang et al.

Most resources of natural gas hydrate (NGH) exist in marine sediments where salts and sea mud are involved. It is of great importance to investigate the effects of salts and sea mud on NGH formation kinetics. In this study, the mixture of silica sand and montmorillonite was used to mimic sea mud. The effects of the NaCl concentration of pore water and montmorillonite content on methane hydrate formation were studied. A low NaCl concentration of 0.2 mol/L and a low montmorillonite content range of 10–25 wt% is beneficial to reduce the induction time of hydrate formation. The high NaCl concentration and high content of montmorillonite will significantly increase the induction time. The average induction time for the experiments with the NaCl concentrations of 0, 0.2, 0.6, and 1.2 mol/L is 20.99, 8.11, 15.74, and 30.88 h, respectively. In the pure silica sand, the NaCl concentration of 0.2 mol/L can improve the final water conversion. In the experiments with pure water, the water conversion increases with the increase of the montmorillonite content due to the improvement of the dispersion of montmorillonite to water. The water conversion of the experiments in pure water with the montmorillonite contents of 0, 10, 25 and 40 wt% is 12.14% (±1.06%), 24.68% (±1.49%), 29.59% (±2.30%), and 32.57% (±1.64%), respectively. In the case of both montmorillonite and NaCl existing, there is a complicated change in the water conversion. In general, the increase of the NaCl concentration enhances the inhibition of hydrate formation and reduces the final water conversion, which is the key factor affecting the final water conversion. The average water conversion of the experiments under the NaCl concentrations of 0, 0.2, 0.6 and 1.2 mol/L is 24.74, 15.14, 8.85, and 5.74%, respectively.

Fredrik Ljungberg, Jonas Linder, Martin Enqvist et al.

In this work, experiment design for marine vessels is explored. A dictionary-based approach is used, i.e., a systematic way of choosing the most informative combination of independent experiments out of a predefined set of candidates. This idea is quite general but is here tailored to an instrumental variable (IV) estimator with zero-mean instruments. This type of estimator is well-suited to deal with parameter estimation for second-order modulus models, which is a class of models often used to describe motion of marine vessels. The method is evaluated using both simulated and real data, the latter from a small model ship as well as from a full-scale vessel. Further, a standard motion-planning problem is modified to account for the prior-made choice of information-optimal sub-experiments, which makes it possible to obtain a plan for the complete experiment in the form of a feasible trajectory.