Artificial Intelligence in Marine Science and Engineering

Márquez, Fausto Pedro García; Papaelias, Mayorkinos; Marini, Simone

doi:10.3390/jmse10060711

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Later, Marian and Tremmel 7 in their review titled "Current Trends and Applications of Machine Learning in Tribology," outline recent trends and applications of ML in tribology, emphasizing its potential to revolutionize the understanding and optimization of friction, wear, and lubrication processes. Furthermore, García Márquez et al 8 investigate the use of AI in the context of marine science and engineering. Their review discusses the multifaceted applications of AI techniques within marine-related disciplines.…”

Section: Literature Reviewmentioning

confidence: 99%

AI techniques for evaluating misaligned journal bearing performance: An approach beyond the Sommerfeld number

Rossopoulos,

Papadopoulos

2024

Proceedings of the Institution of Mechanical Engineers, Part J:

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This paper presents two novel artificial intelligence-based approaches for evaluating the performance of heavily loaded marine journal bearings including shaft misalignment. Traditionally, the Sommerfeld number has been used as a key parameter to evaluate the performance similarity between different bearings. However, this method has limitations, particularly when dealing with complex elastic geometries, heavily loaded journal bearings and shaft misalignment. The first proposed approach leverages neural networks to analyze key bearing performance parameters and provide a more accurate and comprehensive assessment of bearing performance similarity, including additional parameters beyond the Sommerfeld number limitations. The second method utilizes artificial intelligence convolutional networks to assess the bearing similarity based on their simulated pressure profiles under isoviscous and isothermal hydrodynamic lubrication regime. The effectiveness of the proposed methods is demonstrated and compared to the traditional Sommerfeld number method, discussing various potential applications and extensions of this concept.

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Section: Literature Reviewmentioning

confidence: 99%

AI techniques for evaluating misaligned journal bearing performance: An approach beyond the Sommerfeld number

Rossopoulos,

Papadopoulos

2024

Proceedings of the Institution of Mechanical Engineers, Part J:

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“…However, all changes in the environment are detected only by the onboard sensing device when an unmanned system is used, and situation identification and response are relatively slow because the environmental status is transmitted through wireless communication. Therefore, unmanned vehicles have begun to strive for automation [ 120 ]. This involves the development of long-term targeting and control navigation for mission performance, with the aim of maximizing operational efficiency by communicating with and complementing various types of equipment.…”

Section: Intelligencementioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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“…In their most fundamental form, these industries are intricately intertwined, with sea transportation playing a significant role in enabling the flow of products inside and between nations [14]- [17]. It is essential to have a sound understanding of the intricacies and dynamics of these industries to comprehend their role in the construction of the modern world [18], [19]. The marine sector, often called the maritime industry, encompasses all aspects of marine transportation, including shipping, shipbuilding, port operations, and maritime services [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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Artificial Intelligence in Marine Science and Engineering

Abstract: This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e [...]

Cited by 7 publications

References 15 publications

AI techniques for evaluating misaligned journal bearing performance: An approach beyond the Sommerfeld number

AI techniques for evaluating misaligned journal bearing performance: An approach beyond the Sommerfeld number

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

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