Utilizing Machine Learning Tools for calm water resistance prediction and design optimization of a fast catamaran ferry

Tools

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 logoORCID: https://orcid.org/0000-0001-6861-4488, Boulougouris, Evangelos ORCID logoORCID: https://orcid.org/0000-0001-5730-007X and Aung, Myo Zin ORCID logoORCID: https://orcid.org/0000-0001-6370-0029;
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