Utilizing Machine Learning Tools for calm water resistance prediction and design optimization of a fast catamaran ferry
Nazemian, Amin and Boulougouris, Evangelos and Aung, Myo Zin (2024) Utilizing Machine Learning Tools for calm water resistance prediction and design optimization of a fast catamaran ferry. Journal of Marine Science and Engineering, 12 (2). 216. ISSN 2077-1312 (https://doi.org/10.3390/jmse12020216)
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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 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.
ORCID iDs
Nazemian, Amin ORCID: https://orcid.org/0000-0001-6861-4488, Boulougouris, Evangelos ORCID: https://orcid.org/0000-0001-5730-007X and Aung, Myo Zin ORCID: https://orcid.org/0000-0001-6370-0029;-
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Item type: Article ID code: 87982 Dates: DateEvent25 January 2024Published22 January 2024Accepted23 December 2023SubmittedSubjects: Naval Science > Naval architecture. Shipbuilding. Marine engineering
Science > Mathematics > Electronic computers. Computer scienceDepartment: Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering Depositing user: Pure Administrator Date deposited: 30 Jan 2024 11:52 Last modified: 09 Nov 2024 01:48 URI: https://strathprints.strath.ac.uk/id/eprint/87982