Research on the hull form optimization using the surrogate models

Zhang, Shenglong; Tezdogan, Tahsin; Zhang, Bao-Ji; Li, Ling

doi:10.1080/19942060.2021.1915875

Cited by 13 publications

(14 citation statements)

References 29 publications

(29 reference statements)

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“…Regarding the shape optimization of AUVs, Sun et al [9] proposed a bionic hull shape (BHS) using a surrogate model and response surface methods (RSM). Zhang et al [10] used a deep belt network (DBN) to reduce wave-making resistance by finding the optimal hull parameters of a Wigley ship.…”

Section: Introduction 1importance Of Flow Control Fins (Fcfs) In Ship...mentioning

confidence: 99%

Optimal Design of Flow Control Fins for a Small Container Ship Based on Machine Learning

Lee

2023

JMSE

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In this study, the optimal design of flow control fins (FCFs) for a container ship was carried out via a machine learning approach. The conventional design practice for the FCF relied on simulation-based performance evaluation, which demands a large amount of analysis time. Instead of computational fluid dynamics (CFD)-based prediction, artificial neural network (ANN)-based prediction was attempted. Prior to the machine learning process, the wake distribution data were collected systematically via CFD. Based on the collected data, the wake distributions and resistance performance dependent on varying the fin positions were learned using the ANN, and the optimal fin position was selected with relevant optimization techniques. When multi-objective optimization was employed, it was found that both wake distributions and resistance performance were improved in a practically applicable timeframe. The current process is superior to conventional simulation-based optimization in terms of speed. From the viewpoint of prediction accuracy, in this study, ANN-based prediction was found to be equally accurate as CFD-based prediction. Thus, the results can provide a novel and reliable design methodology for the optimal design of ship appendages.

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Section: Introduction 1importance Of Flow Control Fins (Fcfs) In Ship...mentioning

confidence: 99%

Optimal Design of Flow Control Fins for a Small Container Ship Based on Machine Learning

Lee

2023

JMSE

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“…In this setting, using CFD results is impractical due to its computational requirements [8], [9], [25]. For this reason, recently, Data-Driven Models (DDMs) are attracting the attention of the industry and academia for their ability to accurately surrogate complex experimental (e.g., EFD) [26]- [29] or numerical (e.g., CFD) [1], [14], [16], [17], [19], [21], [22] procedures based on a historical collection of their inputs and outputs, with a function that is computationally expensive to construct but computationally inexpensive to use. Consequently, DDMs can be included directly both in a human-driven optimization loop reducing the computational requirements (i.e., time) between design iterations or developing a fully automated optimization loop requiring minimal human intervention, enabling the exploration of a wider design space [2], [30].…”

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confidence: 99%

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confidence: 99%

“…Current approaches to vessel hull resistance optimization rely on a mix between human experience and DDMs [1], [14], [16], [17], [19], [21], [22], [31], [32]. As the first step, human experts define a specific parametrization, i.e., a rich yet synthetic quantitative descriptors of a set of candidate geometries, and parameter ranges, i.e., the geometry design space [1], [14], [16], [17], [19], [21], [22], [31], [32]. For this purpose, several approaches exist in the literature: from Free-Form Deformation (FFD) [1], [16], [17], [19], [21], [22] to B-Splines [14], [22], and model design parameters [31], [32] each one having its strengths and weaknesses (Section II).…”

mentioning

confidence: 99%

“…As the first step, human experts define a specific parametrization, i.e., a rich yet synthetic quantitative descriptors of a set of candidate geometries, and parameter ranges, i.e., the geometry design space [1], [14], [16], [17], [19], [21], [22], [31], [32]. For this purpose, several approaches exist in the literature: from Free-Form Deformation (FFD) [1], [16], [17], [19], [21], [22] to B-Splines [14], [22], and model design parameters [31], [32] each one having its strengths and weaknesses (Section II). Once the parametrization and parameter ranges have been defined, a dataset composed of parameters' values (using the selected parametrization) and associated resistance (measured with EFD or estimated using CFD) is built [1], [14], [16], [17], [19], [21], [22], [32].…”

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confidence: 99%

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Data-Driven Models for Yacht Hull Resistance Optimization: Exploring Geometric Parameters Beyond the Boundaries of the Delft Systematic Yacht Hull Series

Walker,

Coraddu,

Oneto

2024

IEEE Access

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Optimizing vessel hull resistance is pivotal for enhancing maritime performance and minimizing environmental impacts. Traditional methods combine expert intuition with Data-Driven Models (DDMs), relying on parametrization to predict and optimize hull geometries using Experimental Fluid Dynamics (EFD) or Computational Fluid Dynamics (CFD) data. However, these conventional approaches are hampered by several limitations: they require significant human input, are computationally intensive and costly, and lack flexibility in adapting to new families of geometries or parameters beyond predefined ranges. Addressing these challenges, our research introduces a novel method that significantly reduces the need for human intervention, computational resources, and costs, while also improving the model's adaptability. By proposing a new a parametrization technique that accurately encompasses the Delft Systematic Yacht Hull Series (DSYHS), we demonstrate that DDMs can be effectively trained directly on EFD datasets. This eliminates the dependency on extensive CFD simulations or the generation of new EFD data tailored to a specific investigation. Our approach matches the performance of leading-edge CFD models, even in extrapolating conditions, with physical plausibility and minimal human oversight. The validation of our method under various and increasingly complex extrapolating scenarios, employing statistical analyses on the DSYHS EFD dataset and comparisons with state-of-the-art CFD models, underscores the effectiveness of our proposal. Furthermore, we demonstrated that our model can successfully optimize hull resistance when navigating geometric parameters outside the confines of the DSYHS validating our results through leading-edge CFD simulations. This work addresses the limitations of existing methodologies by offering a novel approach more accurate, efficient, cost-effective, flexible, automated, and robust to extrapolation for hull resistance optimization.

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Review of the IMO Initiatives for Ship Energy Efficiency and Their Implications

Tadros,

Ventura,

Guedes Soares

2023

J. Marine. Sci. Appl.

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This paper presents a review of the different International Maritime Organization (IMO) initiatives to improve the ship energy efficiency of new and existing ships, which is considered one of the essential tasks to reduce Greenhouse Gas (GHG) in the maritime industry. First, the IMO effort and initiatives and the different indices suggested by the IMO are presented till the last version of the Marine Environment Protection Committee (MEPC), showing the effect of different technologies on reducing the level of indices and the suggested improvement of the terms of indices in the next years. Second, the short- and long-term strategies suggested by the IMO are presented, showing that the effect of indices will be noticed in the short term, while the new fuels will show a significant improvement in the long term. Finally, several examples of cooperation between the different organizations are presented, showing that transferring knowledge and experience will significantly impact the maritime industry and thus lead to the concept of green ships in the near future. This paper shows that the combination of different solutions, the cooperation between stakeholders and the sharing of the data and information are important to achieve the required goal.

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Research on the hull form optimization using the surrogate models

Cited by 13 publications

References 29 publications

Optimal Design of Flow Control Fins for a Small Container Ship Based on Machine Learning

Optimal Design of Flow Control Fins for a Small Container Ship Based on Machine Learning

Data-Driven Models for Yacht Hull Resistance Optimization: Exploring Geometric Parameters Beyond the Boundaries of the Delft Systematic Yacht Hull Series

Review of the IMO Initiatives for Ship Energy Efficiency and Their Implications

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