International Journal of Naval Architecture and Ocean Engineering最新文献

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IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

Machine learning-based optimal value calculation for welding variables in AR training 基于机器学习的AR训练中焊接变量最优值计算

IF 2.3 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.ijnaoe.2025.100652

Chang Sub Song , Jong-Ho Nam

Currently, the shipbuilding industry is experiencing a surge in orders due to the rising demand for eco-friendly ships, necessitating the optimal use of available resources for production. However, the production workforce has not fully recovered to the level required to meet these increased orders following large-scale industry restructuring. In particular, there is a shortage of highly skilled welders, and concerns are growing about the transfer of expertise due to an aging workforce and a lack of younger workers. Shipbuilders worldwide face similar challenges and are exploring various methods to transfer the tacit knowledge of skilled welders to less experienced workers, which has introduced unforeseen challenges. In this study, we develop a machine learning algorithm that suggests the optimal values of key welding variables for an AR-based welding training system designed to assist less skilled workers. We collected welding data from highly skilled workers using the FCAW (Flux-Cored Arc Welding) technique, which is commonly employed in the shipbuilding process. The welding variables that represent tacit knowledge were identified and trained using the Extra Trees Regressor model. Subsequently, a welding AR training system was implemented, allowing the trained model to guide users on the optimal values for welding variables. Finally, the effectiveness of this system in training welders was verified at a shipyard technical training center.

目前，由于对环保船舶的需求不断增加，造船业的订单量急剧增加，因此必须充分利用现有资源进行生产。然而，在大规模行业重组之后，生产劳动力还没有完全恢复到满足这些增加的订单所需的水平。特别是，高技能焊工短缺，而且由于劳动力老龄化和年轻工人缺乏，人们越来越担心专业知识的转移。世界各地的造船商都面临着类似的挑战，他们正在探索各种方法，将熟练焊工的隐性知识传授给经验不足的工人，这带来了意想不到的挑战。在本研究中，我们开发了一种机器学习算法，该算法为基于ar的焊接培训系统提供关键焊接变量的最佳值，旨在帮助技能较低的工人。我们收集了使用FCAW（药芯弧焊）技术的高技能工人的焊接数据，这种技术通常用于造船过程。使用额外树回归模型对代表隐性知识的焊接变量进行识别和训练。随后，实现了焊接AR训练系统，训练后的模型可以指导用户选择焊接变量的最优值。最后，以某船厂技术培训中心为例，验证了该系统在焊工培训中的有效性。

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引用次数: 0

Estimation of ship operational performance degradation using deep-learning-based fuel oil consumption prediction models 基于深度学习的燃油消耗预测模型的船舶使用性能退化估计

IF 2.3 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.ijnaoe.2025.100666

Donghyun Park , Jae-Yoon Jung , Beom Jin Park

This paper proposes a novel method for estimating ship operational performance degradation (SOPD) using a fuel oil consumption (FOC) prediction model based on deep neural networks with shortcut connections. The model leverages operational and environmental data from a crude oil tanker over a 21-month period to predict FOC and assess SOPD. A cumulative anchoring effect is introduced as a new feature of the FOC prediction model, capturing the impact of biofouling caused by prolonged anchoring in warm waters. In this study, SOPD is considered the additional fuel rate required for a journey leg due to degradation, which is estimated by comparing predicted FOC with and without the cumulative anchoring effect. The SOPD estimation is illustrated according to increasing journey legs based on the FOC prediction models. The proposed SOPD estimation method provides valuable insights for shipping companies to optimize operational schedules and improve fuel efficiency.

提出了一种基于快速连接深度神经网络的船舶燃油消耗预测模型，用于船舶使用性能退化（SOPD）估计。该模型利用一艘原油油轮在21个月期间的运行和环境数据来预测FOC和评估SOPD。作为FOC预测模型的新特征，引入了累积锚定效应，捕捉了在温暖水域长时间锚定引起的生物污染的影响。在本研究中，SOPD被认为是由于退化导致的行程段所需的额外燃油率，通过比较有和没有累积锚定效应的预测FOC来估计。在FOC预测模型的基础上，给出了基于增加行程的SOPD估计。提出的SOPD估计方法为航运公司优化运营计划和提高燃油效率提供了有价值的见解。

引用次数: 0

Design optimization of Savonius hydrokinetic turbine with aid of an artificial neural network model 基于人工神经网络模型的萨沃纽斯水轮机设计优化

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.ijnaoe.2025.100693

Mafira Ayu Ramdhani, Senthil Kumar Natarajan, Il Hyoung Cho

The Savonius hydrokinetic turbine (SHT), a vertical-axis turbine, efficiently extracts energy from low-speed water currents in rivers, canals, and marine environments. Optimizing the tip speed ratio (TSR), gap ratio (GR), and immersion ratio (

Z / D

) enhances its power extraction efficiency. Traditionally, optimization relies on computationally intensive CFD simulations, which are time-consuming. To address this, a machine learning-based approach, specifically an Artificial Neural Network (ANN) model is employed, reducing reliance on extensive CFD simulations while maintaining accuracy. The ANN model was trained using CFD simulations performed in StarCCM+ and then used to predict power coefficients for various design configurations. The CFD simulations are validated against experimental results reported in the literature. The optimal parameters, tip speed ratio (0.7808), gap ratio (−0.0498), and immersion ratio (1.0661) yielded the highest power coefficient (0.2179). This study demonstrates that machine learning accurately predicts turbine performance, reducing reliance on extensive CFD simulations, making turbine optimization more efficient.

Savonius水动力涡轮机（SHT）是一种垂直轴涡轮机，可以有效地从河流、运河和海洋环境中的低速水流中提取能量。通过对叶尖速比（TSR）、间隙比（GR）和浸泡比（Z/D）的优化，提高了抽油效率。传统上，优化依赖于计算密集型的CFD模拟，这非常耗时。为了解决这个问题，采用了基于机器学习的方法，特别是人工神经网络（ANN）模型，减少了对大量CFD模拟的依赖，同时保持了准确性。通过在StarCCM+中进行CFD模拟来训练人工神经网络模型，然后用于预测各种设计配置的功率系数。CFD模拟与文献报道的实验结果进行了对比验证。在叶尖速比（0.7808）、间隙比（−0.0498）和浸没比（1.0661）的优化参数下，功率系数最高，为0.2179。该研究表明，机器学习可以准确预测涡轮机性能，减少对大量CFD模拟的依赖，从而提高涡轮机优化效率。

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引用次数: 0

Adaptive event-triggered anti-windup control for dynamic positioning of turret-moored vessels with nonconvex input constraint and uncertainties 具有非凸输入约束和不确定性的炮塔系泊船舶动态定位自适应事件触发反卷绕控制

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01 DOI: 10.1016/j.ijnaoe.2025.100694

Bingyao Tan , Yulong Tuo , Yuanhui Wang , Zhouhua Peng , Shasha Wang

This paper proposes an adaptive event-triggered anti-windup dynamic positioning (DP) control method for a turret-moored vessel subject to the uncertainties and nonconvex control input constraint. Firstly, by introducing a nonconvex constraint operator, the designed control input is mapped to the actual control input vector with the maximum amplitude constraint in the same direction, thereby ensuring that the actual control input remains within the nonconvex constraint set. Secondly, the uncertainties are ingeniously separated into an unavailable single parameter and available state-related items. The unavailable single parameter is estimated by an adaptive law online. Then, an adaptive nonconvex constraint anti-windup DP controller is proposed based on the single parameter adaptive law and the nonconvex constraint operator. Furthermore, we integrate an adaptive event-triggered mechanism into the DP controller to decrease its execution frequency. The adaptive event-triggered mechanism can effectively balance the control performance and control signal update frequency. Finally, the effectiveness of the proposed methods is validated through simulations.

针对具有不确定性和非凸控制输入约束的炮塔系泊船舶，提出了一种自适应事件触发反卷绕动态定位控制方法。首先，通过引入非凸约束算子，将设计的控制输入映射到具有最大振幅约束的实际控制输入向量上，从而保证实际控制输入保持在非凸约束集内。其次，将不确定性巧妙地分离为不可用的单个参数和可用的状态相关项。通过自适应律在线估计不可用的单个参数。然后，基于单参数自适应律和非凸约束算子，提出了一种自适应非凸约束抗卷绕DP控制器。此外，我们将自适应事件触发机制集成到DP控制器中，以降低其执行频率。自适应事件触发机制可以有效地平衡控制性能和控制信号更新频率。最后，通过仿真验证了所提方法的有效性。

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引用次数: 0

IF 3.9 3区工程技术 Q2 ENGINEERING, MARINE

International Journal of Naval Architecture and Ocean Engineering

Pub Date : 2025-01-01

引用次数: 0

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