Transfer Learning with Deep Neural Network toward the Prediction of Wake Flow Characteristics of Containerships DOI Creative Commons

Min-Kyung Lee,

Inwon Lee

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(10), P. 1898 - 1898

Published: Sept. 29, 2023

In this study, deep neural network (DNN) and transfer learning (TL) techniques were employed to predict the viscous resistance wake distribution based on positions of flow control fins (FCFs) applied containerships various sizes. Both methods utilized data collected through computational fluid dynamics (CFD) analysis. The position fin (FCF) hull form information as input data, output included coefficients components propeller axial velocity. base DNN model was trained validated using a source dataset from 1000 TEU containership. grid search cross-validation technique optimize hyperparameters model. Then, for varying To enhance accuracy feature prediction with limited amount rate optimization conducted. Transfer involves retraining reconfiguring model, verified fine-tuning method results study can provide designers performance evaluation by predicting distribution, without relying CFD

Language: Английский

Review of the Decision Support Methods Used in Optimizing Ship Hulls towards Improving Energy Efficiency DOI Creative Commons
Mina Tadros, L. Ventura, C. Guedes Soares

et al.

Journal of Marine Science and Engineering, Journal Year: 2023, Volume and Issue: 11(4), P. 835 - 835

Published: April 15, 2023

This paper presents a review of the different methods and techniques used to optimize ship hulls over last six years (2017–2022). shows percentages reduction in resistance, thus fuel consumption, improve ships’ energy efficiency, towards achieving goal maritime decarbonization. Operational research machine learning are common decision support find optimal solution. covers four areas hulls, including hull form, structure, cleaning lubrication. In each area research, several computer programs used, depending on study’s complexity objective. It has been found that no specific method is considered optimum, while combination can achieve more accurate results. Most work focused concept stage design, operational conditions recently taken place, an improvement efficiency. The finding this study contributes mapping scientific knowledge technology identifying relevant topic areas, recognizing gaps opportunities. also helps present holistic approaches future supporting realistic solutions sustainability.

Language: Английский

Citations

18
On reduced-order modeling of gas–solid flows using deep learning DOI Creative Commons
Shuo Li, Guangtao Duan, Mikio Sakai

et al.

Physics of Fluids, Journal Year: 2024, Volume and Issue: 36(3)

Published: March 1, 2024

Reduced-order models (ROMs) have been extensively employed to understand complex systems efficiently and adequately. In this study, a novel parametric ROM framework is developed produce Eulerian–Lagrangian simulations. This study employs two typical strategies reproduce the physical phenomena of gas–solid flow by predicting adequate dynamics modal coefficients in ROM: (i) based on radial-basis function (RBF) interpolation, termed ROM-RBF (ii) long–short term memory (LSTM) neural network, ROM-LSTM. ROM, an advanced technique, namely, Lanczos-based proper orthogonal decomposition (LPOD), transform numerical snapshots into coefficients. Validation tests are conducted system such as spouted bed. The coherent structures flows shown be captured LPOD technique. Besides, comparison with high-fidelity simulations, our proposed ROMs simulate significantly reducing calculation time several orders magnitude faithfully macroscopic properties. particular, compared ROM-RBF, ROM-LSTM can capture fields more accurately within flows.

Language: Английский

Citations

6
Resistance reduction optimization of an amphibious transport vehicle DOI
Bolong Liu,

Xiaojun Xu,

Dibo Pan

et al.

Ocean Engineering, Journal Year: 2023, Volume and Issue: 280, P. 114854 - 114854

Published: May 29, 2023

Language: Английский

Citations

10
Hull form optimization of fully parameterized small ships using characteristic curves and deep neural networks DOI Creative Commons
Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo

et al.

International Journal of Naval Architecture and Ocean Engineering, Journal Year: 2024, Volume and Issue: 16, P. 100596 - 100596

Published: Jan. 1, 2024

Designing a hull form typically involves beginning with reference based on ship owner requirements, editing the to satisfy and determining most efficient form. Numerical analyses using computational fluid dynamics (CFD) were employed assess performance of However, these require extensive resources, making it challenging perform thorough within design timeframe. To address this issue, paper proposes an approach that defining range forms characteristic curves, predicting their deep neural networks (DNNs), subsequently optimal predictions. Initially, small was defined four curves parameterized 29 variables. Fairness optimization performed define surface. By varying parameters, 896 different generated, CFD analysis conducted for each variant. These data then used build DNN model capable parameters. The accuracy evaluated, resulting in mean absolute error (MAE) 2.835%. Subsequently, is combined genetic algorithm identify set parameters form, This process revealed reduced total hydrodynamic resistance by approximately 7% compared initial design. Consequently, study demonstrates effectiveness proposed method deriving ships.

Language: Английский

Citations

4
Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms DOI

Shuwei Zhu,

Ning Sun,

Siying Lv

et al.

Journal of Membrane Computing, Journal Year: 2024, Volume and Issue: 6(4), P. 318 - 334

Published: Aug. 28, 2024

Language: Английский

Citations

4
A fast and high-fidelity multi-parameter thermal-field prediction system based on CFD and POD coupling: Application to the RPV insulation structure DOI
Yanjun Dai, Jie Zhao,

Xiaoli Gui

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2024, Volume and Issue: 233, P. 125985 - 125985

Published: July 30, 2024

Language: Английский

Citations

3
A method for generating multiple hull forms at once using MLP (Multi-Layer Perceptron) DOI
Jin-Hyeok Kim, Myung-Il Roh, In-Chang Yeo

et al.

Ocean Engineering, Journal Year: 2025, Volume and Issue: 324, P. 120659 - 120659

Published: Feb. 20, 2025

Language: Английский

Citations

0
Aerodynamic performance modeling method of high-altitude propellers across the entire flight envelope DOI Creative Commons
Miao Zhang, Jun Jiao, Jian Zhang

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 2, 2025

Language: Английский

Citations

0
Temporal super-resolution prediction of wave field for trimaran with arbitrary layout based on dynamic mode decomposition- α method DOI
Xinwang Liu,

Xu Sun,

Zhaojin Rong

et al.

Physics of Fluids, Journal Year: 2025, Volume and Issue: 37(4)

Published: April 1, 2025

As an important technology in ocean engineering and aerospace fields, the development of flow field super-resolution reconstruction stems from urgent need for high-fidelity analysis. In order to avoid randomness difficulty parameter adjustment caused by machine-learning-based methods reconstruction, this paper uses idea dynamic mode decomposition (DMD), introduces numerical method Schur–Padé real power matrix, proposes a temporal prediction DMD-α, which only matrix manipulation realize periodic at any time. Taking wave formed movement trimaran regular waves as example, selection strategy based on DMD-α is proposed take accuracy efficiency into account. Furthermore, proper orthogonal Kriging surrogate models are combined with arbitrary side-hull layout validate robustness method. The results show that stable, efficient, can obtain prediction, has great potential complex fields optimization design fluid performances various structures.

Language: Английский

Citations

0
A novel hull form optimization framework based on multi-fidelity deep neural network DOI

Ya-bo Wei,

Guohua Pan,

Passakorn Paladaechanan

et al.

Journal of Hydrodynamics, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Language: Английский

Citations

0