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Computational Fluid Dynamics-Based for Ship Hydrodynamics Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Marine Science and Engineering".

Deadline for manuscript submissions: closed (20 April 2025) | Viewed by 747

Special Issue Editors


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Guest Editor
Department of Marine Structure and Ship Engineering, Ocean College, Zhejiang University, Hangzhou 316000, China
Interests: ship hydrodynamics; computational fluid dynamics; marine structures; naval architecture; fluid mechanics; seakeeping; autonomous underwater vehicles; modeling and simulation

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Guest Editor
Department of Naval Architecture and Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: ship maneuvering and seakeeping; modeling of ship dynamics; numerical ship hydrodynamics
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Special Issue Information

Dear Colleagues,

This Special Issue of Applied Sciences aims to promote the state-of-the-art technical and science–cultural exchange among hydrodynamic researchers in Computational Fluid Dynamics-based for Ship Hydrodynamics Applications (formed 2024.09 with a due date of 2025.04.20).

This Special Issue concerns novel CFD-based hydrodynamics in naval architecture, ocean engineering and other marine-related aspects. Faced with the exciting challenge of new oceanic market demands and the maritime industry, it is extremely significant for researchers from different countries and regions to share innovative CFD technology and further their understanding of marine hydrodynamics and CFD applications.

Hopefully, this Special Issue will provide a forum for a clear overview and deep discussions on the recent research progress in marine hydrodynamics. Meanwhile, it offers high-quality paper publishing services, clear research content, and a rapid review process. Innovative CFD-based Ship Hydrodynamics Applications will enhance the effective and efficient design index (EEDI). The more efficient utilization of ship energy relies on optimizing hydrodynamic performance to promote carbon neutrality engineering.

We also hope that this Special Issue will help researchers obtain better insight into CFD-based studies on ship hydrodynamics, propeller design, cavitation analysis, maneuvering ability, controllability, seakeeping, free surface and wave effects on ships, etc., so that they can make more contributions to the development of marine hydrodynamics research. Furthermore, this Special Issue addresses the following topics:

  • Hydrodynamic modeling and simulation for ships.
  • Free surface and/or wave effects on ship performance.
  • Ship maneuverability with seakeeping performance.
  • CFD verification method.
  • CFD turbulent modeling and applications.
  • CFD trade-off study on hydrodynamic, cavitation, and noise for marine propulsion systems.
  • Special propeller modeling and propulsion system design.
  • Scale effect on ship hydrodynamics.
  • Scale effect on tip-rake propeller.
  • Cavitation effect on hull form, rudder, and/or propeller.
  • Hydrodynamic optimization of ultra-large cargo ships, including LNG.
  • Comprehensive optimization design for clean energy ships using CFD.
  • Hydrodynamic shape design on novel marine vehicles, including AUV (autonomous underwater vehicle), USV (unmanned surface vehicle), and ROV (remotely operator vehicle).
  • Self-propulsion simulation in one or multi-DOFs.
  • AI for optimal ship hydrodynamic design.
  • Ship digital twin technology for hydrodynamic performance and applications, etc.
  • Ship hydrodynamic modeling in offshore or shallow water areas.
  • Active and/or passive wave compensation systems, including hanging ladders and cranes.
  • Monitoring and verification technology for ship motion performance on waves.

We sincerely look forward to your contribution.

Dr. Chen-Wei Chen
Prof. Dr. Zaojian Zou
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • ship hydrodynamics
  • computational fluid dynamics
  • marine structures
  • naval architecture
  • fluid mechanics
  • seakeeping
  • maneuvering
  • controllability
  • modeling and simulation

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Published Papers (1 paper)

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Research

23 pages, 2527 KiB  
Article
Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
by Yujie Shen, Shuxia Ye, Yongwei Zhang, Liang Qi, Qian Jiang, Liwen Cai and Bo Jiang
Appl. Sci. 2025, 15(6), 2934; https://doi.org/10.3390/app15062934 - 8 Mar 2025
Viewed by 522
Abstract
Resistance is a key index of a ship’s hydrodynamic performance, and studying the design of the bulbous bow is an important method to reduce ship resistance. Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses [...] Read more.
Resistance is a key index of a ship’s hydrodynamic performance, and studying the design of the bulbous bow is an important method to reduce ship resistance. Based on the ship resistance sample data obtained from computational fluid dynamics (CFD) simulation, this study uses a machine learning method to realize the fast prediction of ship resistance corresponding to different bulbous bows. To solve the problem of insufficient accuracy in the single surrogate model, this study proposes a CBR surrogate model that integrates convolutional neural networks with backpropagation and radial basis function models. The coordinates of the control points of the NURBS surface at the bulbous bow are taken as the design variables. Then, a convergence factor is introduced to balance the global and local search abilities of the whale algorithm to improve the convergence speed. The sample space is then iteratively searched using the improved whale algorithm. The results show that the mean absolute error and root mean square error of the CBR model are better than those of the BP and RBF models. The accuracy of the model prediction is significantly improved. The optimized bulbous bow design minimizes the ship resistance, which is reduced by 4.95% compared with the initial ship model. This study provides a reliable and efficient machine learning method for ship resistance prediction. Full article
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