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Data-Driven Methods for Marine Structures
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Data-Driven Methods for Marine Structures

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: 25 December 2024 | Viewed by 4922

Special Issue Editor

Dr. Burak Can Cerik

E-Mail Website
Guest Editor
School of Water, Energy and Environment, Cranfield University, Cranfield, UK
Interests: marine structures; structural integrity; fatigue and fracture; structural impact; offshore wind turbines; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the synergy between data-driven methodologies and offshore engineering has yielded transformative insights, revolutionizing the way we perceive and manage marine structures. We invite researchers and practitioners to contribute to a Special Issue that explores the forefront of "Data-driven Methods for Marine Structures", aiming to accelerate advancements in structural health monitoring, integrity assessment, environmental condition forecasting, and performance optimization.

Scope and Focus:

This Special Issue seeks to assemble cutting-edge research at the intersection of data science, offshore engineering, and ocean renewable energy. The marine environment, characterized by its complexity and variability, demands innovative approaches to ensure the robustness and efficiency of structures such as offshore wind turbines, floating platforms, and offshore installations. We encourage contributions that encompass, but are not limited to, the following areas:

Structural Health Monitoring (SHM) and Integrity Assessment: novel data-driven techniques for real-time monitoring, damage detection, and prognosis of marine structures, enhancing safety and minimizing downtime.

Environmental Condition Forecasting: data-driven models that predict environmental conditions, such as waves, currents, and wind, enabling optimized operation and maintenance strategies.

Motion Prediction of Floating Platforms: data-driven methods for predicting the dynamic response and motion behavior of floating structures, enhancing design and operational efficiency.

Digital Twin Technologies: advancements in creating accurate and dynamic digital twins that replicate the behavior of marine structures, enabling virtual testing and predictive maintenance.

Application to Floating Offshore Wind Turbines (FOWTs): innovative applications of data-driven approaches to enhance the performance, reliability, and lifespan of FOWTs, catalyzing the growth of sustainable offshore wind energy.

Submission Guidelines:

We welcome original research articles and review papers that elucidate the intersection of data science and offshore engineering. Submissions should demonstrate the practical implications of data-driven methods in addressing challenges faced by marine structures. Manuscripts will undergo a rigorous peer-review process, ensuring high scientific quality and practical relevance.

Join us in charting a new course for offshore engineering by harnessing the power of data-driven methodologies. Your contributions will drive innovation, inform industry practices, and foster sustainable development in the realm of marine structures.

Dr. Burak Can Cerik
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

  • digital twin
  • data-driven mechanics
  • data-driven design
  • machine learning
  • surrogate models
  • structural health monitoring

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

19 pages, 10406 KiB  
Article
A Data-Driven DNN Model to Predict the Ultimate Strength of a Ship’s Bottom Structure
by Im-jun Ban, Chaeog Lim, Gi-yong Kim, Seo-young Choi and Sung-chul Shin
J. Mar. Sci. Eng. 2024, 12(8), 1328; https://doi.org/10.3390/jmse12081328 - 6 Aug 2024
Viewed by 658
Abstract
Plates and curved plates are essential components in ship construction. In the design stage, the methods used to evaluate the ultimate strength required to confirm the structural safety of plates include prediction through analytical methods, finite-element analysis (FEA), and empirical formulas. However, with [...] Read more.
Plates and curved plates are essential components in ship construction. In the design stage, the methods used to evaluate the ultimate strength required to confirm the structural safety of plates include prediction through analytical methods, finite-element analysis (FEA), and empirical formulas. However, with nonlinear buckling, the results of the empirical formula and the FEA differ for small flank angles (1~9). As a result, the prediction of the nonlinear ultimate strength of flank angle (1~9) plates still requires significant computation time and cost. To compensate for this, this study performed an ultimate strength prediction method utilizing a deep neural network together with the 4050 curved plate analysis. In addition, this paper presents the analysis results of the nonlinear finite-element method and the geometric shape and ratio of curved plates as training data. Based on the results of this study, designers can more efficiently design appropriate curved plate members by considering the ultimate strength. Full article
(This article belongs to the Special Issue Data-Driven Methods for Marine Structures)
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12 pages, 4364 KiB  
Article
Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network
by Nur Assani, Petar Matić, Danko Kezić and Nikolina Pleić
J. Mar. Sci. Eng. 2024, 12(8), 1318; https://doi.org/10.3390/jmse12081318 - 4 Aug 2024
Viewed by 674
Abstract
Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be [...] Read more.
Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship’s flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. Full article
(This article belongs to the Special Issue Data-Driven Methods for Marine Structures)
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22 pages, 15373 KiB  
Article
A Novel Vision-Based Outline Extraction Method for Hull Components in Shipbuilding
by Hang Yu, Yixi Zhao, Chongben Ni, Jinhong Ding, Tao Zhang, Ran Zhang and Xintian Jiang
J. Mar. Sci. Eng. 2024, 12(3), 453; https://doi.org/10.3390/jmse12030453 - 4 Mar 2024
Viewed by 1101
Abstract
The diverse nature of hull components in shipbuilding has created a demand for intelligent robots capable of performing various tasks without pre-teaching or template-based programming. Visual perception of a target’s outline is crucial for path planning in robotic edge grinding and other processes. [...] Read more.
The diverse nature of hull components in shipbuilding has created a demand for intelligent robots capable of performing various tasks without pre-teaching or template-based programming. Visual perception of a target’s outline is crucial for path planning in robotic edge grinding and other processes. Providing the target’s outline from point cloud or image data is essential for autonomous programming, requiring a high-performance algorithm to handle large amounts of data in real-time construction while preserving geometric details. The high computational cost of triangulation has hindered real-time industrial applications, prompting efforts to improve efficiency. To address this, a new improvement called Directive Searching has been proposed to enhance search efficiency by directing the search towards the target triangle cell and avoiding redundant searches. Another improvement, Heritable Initial, reduces the search amount by inheriting the start position from the last search. Combining Directive Searching and Heritable Initial into a new method called DSHI has led to a significant efficiency advancement, with a calculation efficiency improvement of nearly 300–3000 times compared to the ordinary Bowyer–Watson method. In terms of outlines extraction, DSHI has improved the extraction efficiency by 4–16 times compared to the ordinary Bowyer–Watson methods, while ensuring stable outlines results, and has also increased the extraction efficiency by 2–4 times compared to PCL. The DSHI method is also applied to actual ship component edge-grinding equipment, and its effect meets the shipbuilding process requirements. It could be inferred that the new method has potential applications in shipbuilding and other industries, offering satisfying efficiency and robustness for tasks such as automatic edge grinding. Full article
(This article belongs to the Special Issue Data-Driven Methods for Marine Structures)
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20 pages, 10891 KiB  
Article
Development of Representative Sailing Mode Construction Methodology Using Markov Chain
by Changjae Moon, Sanghun Jeong, Giltae Roh and Kido Park
J. Mar. Sci. Eng. 2024, 12(2), 329; https://doi.org/10.3390/jmse12020329 - 14 Feb 2024
Cited by 1 | Viewed by 1022
Abstract
The strengthening of regulations such as EEXI, EEDI, and CII on ship emissions is underway. Despite their application, objective comparisons of ships are hindered by diverse navigation patterns and varying velocity regulations in different seas and ports. Additionally, a lack of basic data [...] Read more.
The strengthening of regulations such as EEXI, EEDI, and CII on ship emissions is underway. Despite their application, objective comparisons of ships are hindered by diverse navigation patterns and varying velocity regulations in different seas and ports. Additionally, a lack of basic data impedes comparisons of the optimal design and objective energy efficiency for ships. To address these issues, representative sailing modes, similar to those in the automobile industry, are needed. However, there is no reference for marine applications. This study introduces a methodology for representative sailing modes using the Markov chain. A hundred candidate sailing modes were created, and representative modes were identified through an evaluation equation. All chi-square values for representative sailing modes are within 1%, indicating significant results. This study’s findings can aid in designing optimized systems for new vessels and computing authorized fuel efficiency for vessels with diverse sailing patterns. Full article
(This article belongs to the Special Issue Data-Driven Methods for Marine Structures)
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