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Intelligent Approaches to Marine Engineering Research
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Intelligent Approaches to Marine Engineering Research

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 10 February 2025 | Viewed by 3897

Special Issue Editor

Prof. Dr. Graciliano Nicolás Marichal

E-Mail Website
Guest Editor
INGEMAR Group, University of La Laguna, 38001 Tenerife, Spain
Interests: artificial intelligence; neuro-fuzzy systems; predictive maintenance; autonomous vehicles; desalination; marine renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence techniques have advanced significantly. Many fields have benefited from these advances. In particular, the maritime sector, and specifically marine engineering, has benefited greatly from many of the systems that contribute to the navigation of a ship, which are sometimes autonomous, as well as systems that we find on a ship (such as purifiers, servo steering, and propulsion management) and auxiliary systems (such as the installation of various kinds of emergency generators or installations). Thus, desalination plants have been found to be present on deep draft ships. Aside from the presence of these intelligent systems on ships today, these systems are part of different maintenance or construction operations on the ships themselves in all types of offshore structures or in operations related to interventions in docks and coastal areas. In particular, it highlights marine robotics, control engineering, modeling and simulation techniques, and the use of different sensors and actuators for different purposes in marine and maritime sectors.

Prof. Dr. Graciliano Nicolás Marichal
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • marine robotics
  • intelligent techniques applied to power system and power distribution
  • intelligent techniques applied guidance, navigation, and control
  • maritime autonomous surface ships (MASSs)
  • renewable energy
  • hybrid energy systems
  • offshore and tidal energy
  • floating offshore wind turbine
  • oscillating water columns
  • smart maintenance
  • offshore wind energy
  • energy storage technology
  • energy management

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Published Papers (5 papers)

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Research

18 pages, 11716 KiB  
Article
Performance Analysis of Underwater Radiofrequency Communication in Seawater: An Experimental Study
by Raji Alahmad, Hussam Alraie, Ryosuke Hasaba, Kazuhiro Eguchi, Tohlu Matsushima, Yuki Fukumoto and Kazuo Ishii
J. Mar. Sci. Eng. 2024, 12(11), 2104; https://doi.org/10.3390/jmse12112104 - 20 Nov 2024
Viewed by 281
Abstract
Communication with the underwater vehicles during their tasks is one of the most important issues. The need for real-time data transfer raises the necessity of developing communication systems. Conventional underwater communication systems, such as acoustic systems, cannot satisfy applications that need a high [...] Read more.
Communication with the underwater vehicles during their tasks is one of the most important issues. The need for real-time data transfer raises the necessity of developing communication systems. Conventional underwater communication systems, such as acoustic systems, cannot satisfy applications that need a high transmission data rate. In this study, we investigate the radio frequency communication system in seawater, which is crucial for real-time data transfer with underwater vehicles. The experiments were in a water tank full of seawater and a real environment in the ocean. Three types of antennae were used: loop antenna, wire antenna, and helical antenna. An Autonomous Underwater Vehicle (AUV) is used as a transmitter to measure the transmission rate as a function of distance. The helical antenna showed better performance regarding the coverage area. Furthermore, the AUV could move freely within the helical and capture live video streaming successfully. This investigation underscores the potential of radio frequency communication systems for enhancing underwater vehicle operations, offering promising avenues for future research and practical implementation. Full article
(This article belongs to the Special Issue Intelligent Approaches to Marine Engineering Research)
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17 pages, 4048 KiB  
Article
Condition Monitoring in Marine Oil Separation Systems Using Wavelet Packet Transform and Genetic Technique
by Ángela Hernández, Cristina Castejón, Deivis Ávila, María Jesús Gómez-García and Graciliano Nicolás Marichal
J. Mar. Sci. Eng. 2024, 12(11), 2073; https://doi.org/10.3390/jmse12112073 - 17 Nov 2024
Viewed by 487
Abstract
Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capable of analyzing [...] Read more.
Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capable of analyzing the signals from sensors and estimating the remaining useful life in order to avoid breakage or unnecessary replacement. This work presents an intelligent method with signal processing based on Wavelet Packets Transform that provides energy data from vibration measurements as characteristic parameters. These values can be related to its RUL, and they are used as inputs for the training process. In particular, a Genetic Neuro-Fuzzy system is proposed as an intelligent classification technique. Once the training process is completed, it can be concluded that a good classifier has been built, since it relates the energy state of the oil separation system with its remaining useful life, and therefore, the necessary information for efficient predictive maintenance is achieved. Furthermore, a mechanism to obtain the final set of fuzzy rules has been developed, showing the correspondence between these fuzzy rules and the neural network structure. Full article
(This article belongs to the Special Issue Intelligent Approaches to Marine Engineering Research)
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17 pages, 563 KiB  
Article
Research on Underwater Sensor Network Adaptive Clustering Algorithm for Marine Environment Monitoring
by Libin Xue, Chunjie Cao and Rongxin Zhu
J. Mar. Sci. Eng. 2024, 12(11), 1958; https://doi.org/10.3390/jmse12111958 - 1 Nov 2024
Viewed by 566
Abstract
In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting [...] Read more.
In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance. Full article
(This article belongs to the Special Issue Intelligent Approaches to Marine Engineering Research)
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15 pages, 8829 KiB  
Article
Intelligent Detection of 3D Anchor Position Based on Monte Carlo Algorithm
by Zekai Cui, Xueli Zhang, Yuling Chen, Liang Cao, Zeguo Zhang, Zuchao Liang, Meijie Zhou, Jiawen Li and Xiaowen Li
J. Mar. Sci. Eng. 2024, 12(8), 1347; https://doi.org/10.3390/jmse12081347 - 8 Aug 2024
Viewed by 873
Abstract
With the increase in port throughput and the development of the trend of large-scale ships, selecting applicable anchor positions for ships and ensuring the rational and comprehensive utilization of anchorage areas have become a key issue in utilizing the rate of anchorage resources, [...] Read more.
With the increase in port throughput and the development of the trend of large-scale ships, selecting applicable anchor positions for ships and ensuring the rational and comprehensive utilization of anchorage areas have become a key issue in utilizing the rate of anchorage resources, ensuring the safety of ships anchoring operations and promoting the development of the shipping industry. Existing anchor position selection and detection algorithm studies are limited to a two-dimensional plane for ship anchor position selection, with few studies considering intelligent detection algorithms for safe ship anchoring water depths based on three-dimensional space, considering conditions such as wind and waves. By considering water depth conditions and the objectives of anchorage safety issues, this study proposes an intelligent detection method for ship anchor detection to find the ship’s ideal anchor location in the anchorages by applying the Monte Carlo algorithm. In the processing step, in combination with the Monte Carlo random plane anchor position detection algorithm and Monte Carlo random sampling water depth detection method, the anchor position circle radius model, safe spacing model, anchoring area detection model and safe water depth model are used for examining the anchorage area for awaiting the ship in three-dimensions. To verify the accuracy of the proposed model, based on the scale of common ship types and considering the most conservative parameters, a series of simulation experiments are conducted to check whether the water depth meets the requirements and fully ensure the safety of the experimental results. The research results indicate that the detection almost cover the whole anchorage area and obtain safe water depth restrictions. This method helps to improve the efficiency of ship anchoring and makes actual anchoring operations safer. Under the premise of ensuring sufficient safe spacing between ships, the anchorage ground can accommodate more ships and simulate multi-type ship anchor position detection operations concerning various ship-type parameters to further ensure the safety of ship anchoring. Full article
(This article belongs to the Special Issue Intelligent Approaches to Marine Engineering Research)
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34 pages, 15998 KiB  
Article
Method for Collaborative Layout Optimization of Ship Equipment and Pipe Based on Improved Multi-Agent Reinforcement Learning and Artificial Fish Swarm Algorithm
by Hongshuo Zhang, Yanyun Yu, Zelin Song, Yanzhao Han, Zhiyao Yang and Lang Ti
J. Mar. Sci. Eng. 2024, 12(7), 1187; https://doi.org/10.3390/jmse12071187 - 15 Jul 2024
Viewed by 1062
Abstract
The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is [...] Read more.
The engine room is the core area of a ship, critical to its operation, safety, and efficiency. Currently, many researchers merely address the ship engine room layout design (SERLD) problem using optimization algorithms and independent layout strategies. However, the engine room environment is complex, involving two significantly different challenges: equipment layout and pipe layout. Traditional methods fail to achieve optimal collaborative layout objectives. To address this research gap, this paper proposes a collaborative layout method that combines improved reinforcement learning and heuristic algorithms. For equipment layout, the engine room space is first discretized into a grid, and a Markov decision process (MDP) framework suitable for equipment layout is proposed, including state space, action space, and reward mechanisms suitable for equipment layout. An improved adaptive guided multi-agent Q-learning (AGMAQL) algorithm is employed to train the layout model in a centralized manner, with enhancements made to the agent’s exploration state, exploration action, and learning strategy. For pipe layout, this paper proposes an improved adaptive trajectory artificial fish swarm algorithm (ATAFSA). This algorithm incorporates a hybrid encoding method, adaptive strategy, scouting strategy, and parallel optimization strategy, resulting in enhanced stability, accuracy, and problem adaptability. Subsequently, by comprehensively considering layout objectives and engine room attributes, a collaborative layout method incorporating hierarchical and adaptive weight strategies is proposed. This method optimizes in phases according to the layout objectives and priorities of different stages, achieving multi-level optimal layouts and providing designers with various reference schemes with different focuses. Finally, based on a typical real-world engine room engineering case, various leading algorithms and strategies are tested and compared. The results show that the proposed AGMAQL-ATAFSA (AGMAQL-ATA) exhibits robustness, efficiency, and engineering practicality. Compared to previous research methods and algorithms, the final layout quality improved overall: equipment layout effectiveness increased by over 4.0%, pipe optimization efficiency improved by over 40.4%, and collaborative layout effectiveness enhanced by over 2.2%. Full article
(This article belongs to the Special Issue Intelligent Approaches to Marine Engineering Research)
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