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Machine Learning and Modeling for Ship Design
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Machine Learning and Modeling for Ship Design

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: closed (15 February 2024) | Viewed by 24310

Special Issue Editors

Prof. Dr. Panagiotis D. Kaklis

E-Mail Website
Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: isogeometric analysis (IGA); naval hydrodynamics; computer-aided geometric design; CAD; parametric modelling; shape optimisation; dimensionality reduction; virtual environments
Special Issues, Collections and Topics in MDPI journals
Dr. Konstantinos Kostas

E-Mail Website
Guest Editor
School of Engineering and Digital Sciences, Nazarbayev University, Astana 010000, Kazakhstan
Interests: virtual environments with applications in naval architecture & marine engineering; parametric geometrical modeling & design optimization; application of the isogeometric concept in engineering & ship design
Dr. Shahroz Khan

E-Mail Website
Guest Editor
Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK
Interests: computer-aided design & engineering; machine learning; generative design; shape optimisation; design intelligence

Special Issue Information

Dear Colleagues,

Machine Learning (ML) is a sub-field of Artificial Intelligence (AI), devoted to understanding and building methods that leverage data to improve performance on some set of tasks. A 2020 Deloitte survey found that 67% of companies are using machine learning, and 97% are using or planning to use it in the next year. During the last decade, as a result of installing geospatial data systems, measuring and monitoring onboard ships, and proliferation of using simulation and optimization algorithms, Big Data has been established in Shipping providing a steadily expanding data flow to industry and research. As a result, the literature distribution of ML applications in Shipping has experienced an exponential growth since 2005, having reached thousands of citations per year.

The aim of this Special Issue (SI) is to profile the current status of research versus the next major aim of ML-based research in shipping, namely the need for a deeper embedding in AI of ML technologies such as Neural Networks (NNs), recurrent NNs (RNNs), autoencoders, support vector machines (SVMs), convolutional NNs (CNNs),  generative adversarial networks (GANs), etc. Since it is generally accepted that around 70% of the manufacturing costs of a product can be derived from design decisions, our SI will focus on Ship Design including its impact on lifecycle operation issues.

More specifically, we are looking for papers dealing with one or more themes from the following, not exhaustive, list:

  • ML for design and analysis: estimation of main particulars and conceptual design, fluid-flow modeling and resistance predictions, turbulence modeling, wind/wave induced loads modeling, hull/propeller/foils design and modelling, etc.
  • ML, dimensionality reduction (DR) and sensitivity analysis (SA) in Optimization: versatility and capacity of design spaces, intrasensitivity, ship and/or systems design optimization, shipbuilding optimization, design for reliability, etc.
  • ML for operational modeling: wind and/or wave forecasting and ship loading, route design and prediction, systems condition monitoring, predictive maintenance, fuel consumption and engine power predictions, stability, maneuvering, docking / collision avoidance, JIT (just-in-time) arrival, human-factor modeling for accidents prevention, etc.
  • ML for autonomous systems: autonomous ships, autonomous vehicles for inspection, design for autonomous maintenance operations, rerouting and automatic docking/ maneuvering, etc.
  • Mixed-initiative generative learning models: intelligent learning systems combining artificial and human agents to work corporately and complementarily during the training process.

We especially welcome works contributing to cutting-edge topics, such as:

  • Physics-informed ML tools;
  • Moment-driven ML tools for DR/SA;
  • Unsupervised Learning in heterogeneous Design Spaces.

Prof. Dr. Panagiotis D. Kaklis
Dr. Konstantinos Kostas
Dr. Shahroz Khan
Guest Editors

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

  • artificial intelligence
  • machine learning
  • ship design
  • parametric modeling
  • design spaces
  • mixed-initiative modelling
  • shape optimization
  • dimensionality reduction
  • sensitivity analysis

Published Papers (17 papers)

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16 pages, 710 KiB  
Article
Generative vs. Non-Generative Models in Engineering Shape Optimization
by Zahid Masood, Muhammad Usama, Shahroz Khan, Konstantinos Kostas and Panagiotis D. Kaklis
J. Mar. Sci. Eng. 2024, 12(4), 566; https://doi.org/10.3390/jmse12040566 - 27 Mar 2024
Viewed by 609
Abstract
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be overcome with the introduction of augmented shape signature vectors (SSVs) [...] Read more.
Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be overcome with the introduction of augmented shape signature vectors (SSVs) to represent both geometric and physical information. This recent advancement has inspired a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization, which is demonstrated in this work. These models are showcased in airfoil/hydrofoil design, and a comparison of the resulting design spaces is conducted in this work. A conventional generative adversarial network (GAN) and a state-of-the-art generative model, the performance-augmented diverse generative adversarial network (PaDGAN), are juxtaposed with a linear non-generative model based on the coupling of the Karhunen–Loève Expansion and a physics-informed shape signature vector (SSV-KLE). The comparison demonstrates that, with an appropriate shape encoding and a physics-augmented design space, non-generative models have the potential to cost-effectively generate high-performing valid designs with enhanced coverage of the design space. In this work, both approaches were applied to two large foil profile datasets comprising real-world and artificial designs generated through either a profile-generating parametric model or a deep-learning approach. These datasets were further enriched with integral properties of their members’ shapes, as well as physics-informed parameters. The obtained results illustrate that the design spaces constructed by the non-generative model outperform the generative model in terms of design validity, generating robust latent spaces with no or significantly fewer invalid designs when compared to generative models. The performance and diversity of the generated designs were compared to provide further insights about the quality of the resulting spaces. These findings can aid the engineering design community in making informed decisions when constructing design spaces for shape optimization, as it has been demonstrated that, under certain conditions, computationally inexpensive approaches can closely match or even outperform state-of-the art generative models. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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17 pages, 2497 KiB  
Article
Improvement of Machine Learning-Based Modelling of Container Ship’s Main Particulars with Synthetic Data
by Darin Majnarić, Sandi Baressi Šegota, Nikola Anđelić and Jerolim Andrić
J. Mar. Sci. Eng. 2024, 12(2), 273; https://doi.org/10.3390/jmse12020273 - 02 Feb 2024
Cited by 1 | Viewed by 702
Abstract
One of the main problems in the application of machine learning techniques is the need for large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for studies in which it is not possible to access large amounts of data—for [...] Read more.
One of the main problems in the application of machine learning techniques is the need for large amounts of data necessary to obtain a well-generalizing model. This is exacerbated for studies in which it is not possible to access large amounts of data—for example, in the case of ship main data modelling, where a limited amount of real-world data (ship main data) is available for dataset creation. In this paper, a synthetic data generation technique has been applied to generate a large amount of synthetic data points regarding container ships’ main particulars. Models are trained using a multilayer perceptron (MLP) regressor on both original and synthetic data mixed with original data points. Then, the authors validate the performance of the obtained models on the original data and conclude whether a synthetic-data-based approach can be used to develop models in instances where the amount of data on ship main particulars may be limited. The results demonstrate an improvement across almost all outputs, ranging between 0.01 and 0.21 when evaluated using the coefficient of determination (R2) and between 0.27% and 3.43% when models are evaluated with mean absolute percentage error (MAPE). This indicates that the application of synthetic data can indeed be used for the improvement of ML-based model performance. The presented study demonstrates that the application of ML-based syncretization techniques can provide significant improvements to the process of ML-based determination of a ship’s main particulars at the early design stage. This paper suggests that, in cases where only a small dataset is available, artificial neural networks (ANN) can still be effectively employed to derive early-stage design values for the main particulars through the use of synthetic data. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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24 pages, 12095 KiB  
Article
Utilizing Machine Learning Tools for Calm Water Resistance Prediction and Design Optimization of a Fast Catamaran Ferry
by Amin Nazemian, Evangelos Boulougouris and Myo Zin Aung
J. Mar. Sci. Eng. 2024, 12(2), 216; https://doi.org/10.3390/jmse12020216 - 25 Jan 2024
Cited by 1 | Viewed by 756
Abstract
The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in [...] Read more.
The article aims to design a calm water resistance predictor based on Machine Learning (ML) Tools and develop a systematic series for battery-driven catamaran hullforms. Additionally, employing a machine learning predictor for design optimization through the utilization of a Genetic Algorithm (GA) in an expedited manner. Regression Trees (RTs), Support Vector Machines (SVMs), and Artificial Neural Network (ANN) regression models are applied for dataset training. A hullform optimization was implemented for various catamarans, including dimensional and hull coefficient parameters based on resistance, structural weight reduction, and battery performance improvement. Design distribution based on Lackenby transformation fulfills all of the design space, and sequentially, a novel self-blending method reconstructs new hullforms based on two parents blending. Finally, a machine learning approach was conducted on the generated data of the case study. This study shows that the ANN algorithm correlates well with the measured resistance. Accordingly, by choosing any new design based on owner requirements, GA optimization obtained the final optimum design by using an ML fast resistance calculator. The optimization process was conducted on a 40 m passenger catamaran case study that achieved a 9.5% cost function improvement. Results show that incorporating the ML tool into the GA optimization process accelerates the ship design process. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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22 pages, 22898 KiB  
Article
Predictive Model for Hydrostatic Curves of Chine-Type Small Ships Based on Deep Learning
by Dongkeun Lee, Chaeog Lim, Sang-jin Oh, Minjoon Kim, Jun Soo Park and Sung-chul Shin
J. Mar. Sci. Eng. 2024, 12(1), 180; https://doi.org/10.3390/jmse12010180 - 18 Jan 2024
Cited by 1 | Viewed by 695
Abstract
Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents involve small ships (vessels with gross tonnage of less than 10 tons). Stability calculations are critical for assessing the risk of capsizing [...] Read more.
Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents involve small ships (vessels with gross tonnage of less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship’s seaworthiness. Despite the high frequency of capsizing accidents involving them, small ships are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which are required data for stability calculations. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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15 pages, 5676 KiB  
Article
Trajectory Mining and Routing: A Cross-Sectoral Approach
by Dimitrios Kaklis, Ioannis Kontopoulos, Iraklis Varlamis, Ioannis Z. Emiris and Takis Varelas
J. Mar. Sci. Eng. 2024, 12(1), 157; https://doi.org/10.3390/jmse12010157 - 12 Jan 2024
Cited by 1 | Viewed by 750
Abstract
Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when [...] Read more.
Trajectory data holds pivotal importance in the shipping industry and transcend their significance in various domains, including transportation, health care, tourism, surveillance, and security. In the maritime domain, improved predictions for estimated time of arrival (ETA) and optimal recommendations for alternate routes when the weather conditions deem it necessary can lead to lower costs, reduced emissions, and an increase in the overall efficiency of the industry. To this end, a methodology that yields optimal route recommendations for vessels is presented and evaluated in comparison with real-world vessel trajectories. The proposed approach utilizes historical vessel tracking data to extract maritime traffic patterns and implements an A* search algorithm on top of these patterns. The experimental results demonstrate that the proposed approach can lead to shorter vessel routes compared to another state-of-the-art routing methodology, resulting in cost savings for the maritime industry. This research not only enhances maritime routing but also demonstrates the broader applicability of trajectory mining, offering insights and solutions for diverse industries reliant on trajectory data. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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33 pages, 1820 KiB  
Article
Ship Engine Model Selection by Applying Machine Learning Classification Techniques Using Imputation and Dimensionality Reduction
by Kyriakos Skarlatos, Grigorios Papageorgiou, Panagiotis Biris, Ekaterini Skamnia, Polychronis Economou and Sotirios Bersimis
J. Mar. Sci. Eng. 2024, 12(1), 97; https://doi.org/10.3390/jmse12010097 - 03 Jan 2024
Viewed by 1375
Abstract
The maritime is facing a gradual proliferation of data, which is frequently coupled with the presence of subpar information that contains missing and duplicate data, erroneous records, and flawed entries as a result of human intervention or a lack of access to sensitive [...] Read more.
The maritime is facing a gradual proliferation of data, which is frequently coupled with the presence of subpar information that contains missing and duplicate data, erroneous records, and flawed entries as a result of human intervention or a lack of access to sensitive and important collaborative information. Data limitations and restrictions have a crucial impact on inefficient data-driven decisions, leading to decreased productivity, augmented operating expenses, and the consequent substantial decline in a competitive edge. The missing or inadequate presentation of significant information, such as the vessel’s primary engine model, critically affects its capabilities and operating expenses as well as its environmental impact. In this study, a comprehensive study was employed, using and comparing several machine learning classification techniques to classify a ship’s main engine model, along with different imputation methods for handling the missing values and dimensionality reduction methods. The classification is based on the technical and operational characteristics of the vessel, including the physical dimensions, various capacities, speeds and consumption. Briefly, three dimensionality reduction methods (Principal Component Analysis, Uniform Manifold Approximation and Projection, and t-Distributed Stochastic Neighbor Embedding) were considered and combined with a variety of classifiers and the appropriate parameters of the dimensionality reduction methods. According to the classification results, the ExtraTreeClassifier with PCA with 4 components, the ExtraTreeClassifier with t-SNE with perplexity equal to 10 and 3 components, and the same classifier with UMAP with 10 neighbors and 3 components outperformed the rest of the combinations. This classification could provide significant information for shipowners to enhance the vessel’s operation by optimizing it. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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32 pages, 3991 KiB  
Article
ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints
by Noah J. Bagazinski and Faez Ahmed
J. Mar. Sci. Eng. 2023, 11(12), 2215; https://doi.org/10.3390/jmse11122215 - 22 Nov 2023
Viewed by 1278
Abstract
Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to [...] Read more.
Ship design is a years-long process that requires balancing complex design trade-offs to create a ship that is efficient and effective. Finding new ways to improve the ship design process could lead to significant cost savings in the time and effort required to design a ship, as well as cost savings in the procurement and operation of a ship. One promising technology is generative artificial intelligence, which has been shown to reduce design cycle times and create novel, high-performing designs. In a literature review, generative artificial intelligence was shown to generate ship hulls; however, ship design is particularly difficult, as the hull of a ship requires the consideration of many objectives. This paper presents a study on the generation of parametric ship hull designs using a parametric diffusion model that considers multiple objectives and constraints for hulls. This denoising diffusion probabilistic model (DDPM) generates the tabular parametric design vectors of a ship hull, which are then constructed into a point cloud and mesh for performance evaluation. In addition to a tabular DDPM, this paper details adding guidance to improve the quality of the generated parametric ship hull designs. By leveraging a classifier to guide sample generation, the DDPM produced feasible parametric ship hulls that maintained the coverage of the initial training dataset of ship hulls with a 99.5% rate, a 149× improvement over random sampling of the design vector parameters across the design space. Parametric ship hulls produced using performance guidance saw an average 91.4% reduction in wave drag coefficients and an average 47.9× relative increase in the total displaced volume of the hulls compared to the mean performance of the hulls in the training dataset. The use of a DDPM to generate parametric ship hulls can reduce design times by generating high-performing hull designs for future analysis. These generated hulls have low drag and high volume, which can reduce the cost of operating a ship and increase its potential to generate revenue. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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18 pages, 3415 KiB  
Article
Power Prediction Method for Ships Using Data Regression Models
by Yoo-Chul Kim, Kwang-Soo Kim, Seongmo Yeon, Young-Yeon Lee, Gun-Do Kim and Myoungsoo Kim
J. Mar. Sci. Eng. 2023, 11(10), 1961; https://doi.org/10.3390/jmse11101961 - 11 Oct 2023
Cited by 1 | Viewed by 973
Abstract
This study proposes machine learning-based prediction models to estimate hull form performance. The developed models can predict the residuary resistance coefficient (CR), wake fraction (wTM), and thrust deduction fraction (t). The multi-layer perceptron and [...] Read more.
This study proposes machine learning-based prediction models to estimate hull form performance. The developed models can predict the residuary resistance coefficient (CR), wake fraction (wTM), and thrust deduction fraction (t). The multi-layer perceptron and convolutional neural network models, wherein the hull shape was considered as images, were evaluated. A prediction model for the open-water characteristics of the propeller was also generated. The experimental data used in the learning process were obtained from model test results conducted in the Korea Research Institute of Ships and Ocean Engineering towing tank. The prediction results of the proposed models showed good agreement with the model test values. According to the ITTC procedures, the service speed and shaft revolution speed of a ship can be extrapolated from the values obtained from the predictive models. The proposed models demonstrated sufficient accuracy when applied to the sample hull forms based on data not used for training. Thus, they can be implemented in the preliminary design phase of hull forms. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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23 pages, 13094 KiB  
Article
Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships
by Min-Kyung Lee and Inwon Lee
J. Mar. Sci. Eng. 2023, 11(10), 1898; https://doi.org/10.3390/jmse11101898 - 29 Sep 2023
Viewed by 903
Abstract
In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through [...] Read more.
In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance and wake distribution based on the positions of flow control fins (FCFs) applied to containerships of various sizes. Both methods utilized data collected through computational fluid dynamics (CFD) analysis. The position of the flow control fin (FCF) and hull form information were utilized as input data, and the output data included viscous resistance coefficients and components of propeller axial velocity. The base DNN model was trained and validated using a source dataset from a 1000 TEU containership. The grid search cross-validation technique was employed to optimize the hyperparameters of the base DNN model. Then, transfer learning was applied to predict the viscous resistance and wake distribution for containerships of varying sizes. To enhance the accuracy of feature prediction with a limited amount of data, learning rate optimization was conducted. Transfer learning involves retraining and reconfiguring the base DNN model, and the accuracy was verified based on the fine-tuning method of the learning model. The results of this study can provide hull designers for containerships with performance evaluation information by predicting wake distribution, without relying on CFD analysis. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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14 pages, 3294 KiB  
Article
Power Prediction of a 15,000 TEU Containership: Deep-Learning Algorithm Compared to a Physical Model
by Alessandro La Ferlita, Yan Qi, Emanuel Di Nardo, Karoline Moenster, Thomas E. Schellin, Ould EL Moctar, Christoph Rasewsky and Angelo Ciaramella
J. Mar. Sci. Eng. 2023, 11(10), 1854; https://doi.org/10.3390/jmse11101854 - 23 Sep 2023
Cited by 2 | Viewed by 798
Abstract
The authors proposed a direct comparison between white- and black-box models to predict the engine brake power of a 15,000 TEU (twenty-foot equivalent unit) containership. A Simplified Naval Architecture Method (SNAM), based on limited operational data, was highly enhanced by including specific operational [...] Read more.
The authors proposed a direct comparison between white- and black-box models to predict the engine brake power of a 15,000 TEU (twenty-foot equivalent unit) containership. A Simplified Naval Architecture Method (SNAM), based on limited operational data, was highly enhanced by including specific operational parameters. An OAT (one-at-a-time) sensitivity analysis was performed to recognize the influences of the most relevant parameters in the white-box model. The black-box method relied on a DNN (deep neural network) composed of two fully connected layers with 4092 and 8192 units. The network consisted of a feed-forward network, and it was fed by more than 12,000 samples of data, encompassing twenty-three input features. The test data were validated against realistic operational data obtained during specific operational windows. Our results agreed favorably with the results obtained for the DNN, which relied on sufficiently observed data for the physical model. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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21 pages, 1092 KiB  
Article
Shape-Informed Dimensional Reduction in Airfoil/Hydrofoil Modeling
by Zahid Masood, Konstantinos V. Kostas, Shahroz Khan and Panagiotis D. Kaklis
J. Mar. Sci. Eng. 2023, 11(10), 1851; https://doi.org/10.3390/jmse11101851 - 23 Sep 2023
Viewed by 1017
Abstract
Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, [...] Read more.
Parametric models have been widely used in pertinent literature for reconstructing, modifying and representing a wide range of airfoil and/or hydrofoil profile geometries. Design spaces corresponding to these models can be exploited for modeling and profile-shape optimization under various performance criteria. Accuracy requirements, along with the need for modeling local features, often lead to high-dimensional design spaces that hinder the process of shape optimization and design through analysis. In this work, we propose a shape-informed dimensional reduction approach that attempts to tackle this deficiency by producing low-dimensional latent design spaces that can be efficiently used in shape representation and optimization. Furthermore, geometric moments are introduced in an attempt to cost-effectively capture analysis-relevant information that is generally expensive to produce. Specifically, geometric integral properties, although intrinsic features of the shape, are quite commonly related to performance indicators employed in performance optimization and therefore provide a cost-effective physics-informed component in the description of the design in the latent space. To this end, we employ the generalized Karhunen-Loève expansion to produce a shape- and physics-informed subspace retaining the highest possible geometric variance and robustness, that is, a lack of invalid designs. At the same time, a series of shape discretizations, encoding the foil’s shape profile, are examined with regard to their effect on the resulting latent space’s quality and efficiency. Our results demonstrate a significant reduction in the dimensionality of the original design space while maintaining a high representational capacity and a large percentage of valid geometries that facilitate fast convergence to optimal solutions in performance-based optimization. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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25 pages, 9251 KiB  
Article
Sequential Design-Space Reduction and Its Application to Hull-Form Optimization
by Zu-Yuan Liu, Qiang Zheng, Hai-Chao Chang, Bai-Wei Feng and Xiao Wei
J. Mar. Sci. Eng. 2023, 11(8), 1481; https://doi.org/10.3390/jmse11081481 - 25 Jul 2023
Viewed by 794
Abstract
Hull-form optimization is a complex engineering problem. Owing to the several numerical simulations and complex design-performance spaces, hull-form optimization is considered an inefficient process, which makes determining the global optimum difficult. This study used rough set theory (RST) to acquire knowledge and reduce [...] Read more.
Hull-form optimization is a complex engineering problem. Owing to the several numerical simulations and complex design-performance spaces, hull-form optimization is considered an inefficient process, which makes determining the global optimum difficult. This study used rough set theory (RST) to acquire knowledge and reduce the design space for hull-form optimization. Furthermore, we studied one of the hull-form optimization problems by practically applying RST to the appropriate number of sampling points. To solve this problem, we proposed the RST-based sequential design-space reduction (SDSR) method that uses interval theory to calculate subspace intersections and unions, as well as test calculations to choose an appropriate stopping criterion. Finally, SDSR was used to optimize a KRISO container ship to minimize the wave-making resistance. The results were compared to those of direct optimization and one-time design-space reduction, thus proving the feasibility of this method. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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25 pages, 1836 KiB  
Article
Machine-Learning-Enabled Foil Design Assistant
by Konstantinos V. Kostas and Maria Manousaridou
J. Mar. Sci. Eng. 2023, 11(7), 1470; https://doi.org/10.3390/jmse11071470 - 23 Jul 2023
Cited by 2 | Viewed by 1267
Abstract
In this work, supervised Machine Learning (ML) techniques were employed to solve the forward and inverse problems of airfoil and hydrofoil design. The forward problem pertains to the prediction of a foil’s aerodynamic or hydrodynamic performance given its geometric description, whereas the inverse [...] Read more.
In this work, supervised Machine Learning (ML) techniques were employed to solve the forward and inverse problems of airfoil and hydrofoil design. The forward problem pertains to the prediction of a foil’s aerodynamic or hydrodynamic performance given its geometric description, whereas the inverse problem calls for the identification of the geometric profile exhibiting a given set of performance indices. This study begins with the consideration of multivariate linear regression as the base approach in addressing the requirements of the two problems, and it then proceeds with the training of a series of Artificial Neural Networks (ANNs) in predicting performance (lift and drag coefficients over a range of angles of attack) and geometric design (foil profiles), which were subsequently compared to the base approach. Two novel components were employed in this study: a high-level parametric model for foil design and geometric moments, which, as we will demonstrate in this work, had a significant beneficial impact on the training and effectiveness of the resulting ANNs. Foil parametric models have been widely used in the pertinent literature for reconstructing, modifying, and representing a wide range of airfoil and hydrofoil profile geometries. The parametric model employed in this work uses a relatively small number of parameters, 17, to describe uniquely and accurately a large dataset of profile shapes. The corresponding design vectors, coupled with the foils’ geometric moments, constitute the training input from the forward ML models. Similarly, performance curves (lift and drag over a range of angles of attack) and their corresponding moments make up the input for the models used in the inverse problem. The effect of various training datasets and training methods in the predictive power of the resulting ANNs was examined in detail. The use of the best-performing ML models is then demonstrated in two relevant design scenarios. The first scenario involved a software application, the Design Foil Assistant, which allows real-time evaluation of foil designs and the identification of designs exhibiting a set of given aerodynamic or hydrodynamic parameters. The second case benchmarked the use of ML-enabled, performance-based design optimization against traditional foil design optimization carried out with classical computational analysis tools. It is demonstrated that a user-friendly real-time design assistant can be easily implemented and deployed with the identified models, whereas significant time savings with adequate accuracy can be achieved when ML tools are employed in design optimization. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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13 pages, 8757 KiB  
Article
PIV Snapshot Clustering Reveals the Dual Deterministic and Chaotic Nature of Propeller Wakes at Macro- and Micro-Scales
by Danny D’Agostino, Matteo Diez, Mario Felli and Andrea Serani
J. Mar. Sci. Eng. 2023, 11(6), 1220; https://doi.org/10.3390/jmse11061220 - 13 Jun 2023
Cited by 2 | Viewed by 1097
Abstract
This study investigates the underlying mechanisms governing the evolution of tip vortices in the far field of a naval propeller wake. To achieve this, a novel approach utilizing data clustering applied to particle image velocimetry snapshots is employed. The clustering of data is [...] Read more.
This study investigates the underlying mechanisms governing the evolution of tip vortices in the far field of a naval propeller wake. To achieve this, a novel approach utilizing data clustering applied to particle image velocimetry snapshots is employed. The clustering of data is carried out using the k-means algorithm, with the optimal number of clusters determined by evaluating two metrics: the within-cluster sum of squares and the average silhouette. The clustering of phase-locked propeller wake data is focused on the vorticity associated with the regions containing tip vortices. Additionally, techniques such as proper orthogonal decomposition, t-distributed stochastic neighbor embedding, and kernel density estimation are employed to visually represent the data clusters in a two-dimensional space, facilitating their assessment and subsequent discussion. This paper shows how the application of data clustering enables a comprehensive understanding of the complex mechanisms driving the dynamics of propeller wake vortices in both the transitional and far fields. Specifically, it reveals the dual nature of the propeller wake flow, characterized by deterministic and chaotic behavior at macro- and micro-scales. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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13 pages, 3291 KiB  
Article
A Hardware-in-the-Loop Simulator to Optimize Autonomous Sailboat Performance in Real Ocean Conditions
by Tanaka Akiyama, Kostia Roncin and Jean-Francois Bousquet
J. Mar. Sci. Eng. 2023, 11(6), 1104; https://doi.org/10.3390/jmse11061104 - 23 May 2023
Viewed by 1119
Abstract
In this work, a hardware-in-the-loop (HIL) simulator is designed to diagnose the behavior of an autonomous sailboat as it navigates between waypoints. At its core, the HIL simulator includes the sailboat pilot on an embedded system. The sensor data input to the embedded [...] Read more.
In this work, a hardware-in-the-loop (HIL) simulator is designed to diagnose the behavior of an autonomous sailboat as it navigates between waypoints. At its core, the HIL simulator includes the sailboat pilot on an embedded system. The sensor data input to the embedded system is fed by a navigation simulator that takes into account the different forces on the sailboat due to the wind, waves and current conditions. The HIL simulator is then tested for a navigation route from sea trials published in 2014, and the behavior of the automated pilot is compared to its behavior when the vessel is driven by a crew. As demonstrated, the automated system can outperform the man-operated vessel. The tool is also used to diagnose weaknesses in the sailboat autopilot algorithm that can be improved in the future. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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19 pages, 2182 KiB  
Article
Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods
by Xianwei Xie, Baozhi Sun, Xiaohe Li, Tobias Olsson, Neda Maleki and Fredrik Ahlgren
J. Mar. Sci. Eng. 2023, 11(4), 738; https://doi.org/10.3390/jmse11040738 - 29 Mar 2023
Cited by 4 | Viewed by 4556
Abstract
An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel [...] Read more.
An accurate fuel consumption prediction model is the basis for ship navigation status analysis, energy conservation, and emission reduction. In this study, we develop a black-box model based on machine learning and a white-box model based on mathematical methods to predict ship fuel consumption rates. We also apply the Kwon formula as a data preprocessing cleaning method for the black-box model that can eliminate the data generated during the acceleration and deceleration process. The ship model test data and the regression methods are employed to evaluate the accuracy of the models. Furthermore, we use the predicted correlation between fuel consumption rates and speed under simulated conditions for model performance validation. We also discuss applying the data-cleaning method in the preprocessing of the black-box model. The results demonstrate that this method is feasible and can support the performance of the fuel consumption model in a broad and dense distribution of noise data in data collected from real ships. We improved the error to 4% of the white-box model and the R2 to 0.9977 and 0.9922 of the XGBoost and RF models, respectively. After applying the Kwon cleaning method, the value of R2 also can reach 0.9954, which can provide decision support for the operation of shipping companies. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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Review

Jump to: Research

25 pages, 2179 KiB  
Review
Review of Challenges and Opportunities in Turbulence Modeling: A Comparative Analysis of Data-Driven Machine Learning Approaches
by Yi Zhang, Dapeng Zhang and Haoyu Jiang
J. Mar. Sci. Eng. 2023, 11(7), 1440; https://doi.org/10.3390/jmse11071440 - 18 Jul 2023
Cited by 1 | Viewed by 2905
Abstract
Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine [...] Read more.
Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development. Full article
(This article belongs to the Special Issue Machine Learning and Modeling for Ship Design)
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