Utilizing Machine Learning Tools for Calm Water Resistance Prediction and Design Optimization of a Fast Catamaran Ferry

Aug 4, 2026

The study designs a calm-water resistance predictor using machine learning tools and builds a systematic series for battery-driven catamaran hullforms. Regression Trees, Support Vector Machines, and Artificial Neural Network regression models train on resistance data, while hullform optimization is performed using Lackenby transformation, dimensional and hull coefficient parameters, structural weight reduction, and improved battery performance. A sequential self-blending method reconstructs hullforms from two parents. An ANN-based predictor supports fast Genetic Algorithm optimization, yielding a 9.5% cost-function improvement on a 40 m passenger catamaran and accelerating the ship design process.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages24

Reading Time1 h 0 min

AccessFree to download

Introduction

Background

Article Utilizing Machine Learning Tools for Calm Water Resistance Prediction and Design Optimization of a Fast Catamaran Ferry Amin Nazemian *, Evangelos Boulougouris and Myo Zin Aung Citation: Nazemian, A.; Boulougouris, E.; Aung, M.Z. Utilizing Machine Learning Tools for Calm Water Resistance Prediction and Design Optimization of a Fast Catamaran Ferry. J. Mar. Sci. Eng. 2024, 12, 216 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jmse12020216 Academic Editor: Diego Villa Received: 23 December 2023 Revised: 19 January 2024 Accepted: 22 January 2024 Published: 25 January 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . Maritime Safety Research Centre (MSRC), Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G4 0LZ, UK; [evangelos.boulougouris@strath.ac.uk](evangelos.boulougouris@strath.ac.uk) (E.B.); [myo.aung@strath.ac.uk](myo.aung@strath.ac.uk) (M.Z.A.) * Correspondence: [amin.nazemian@strath.ac.uk](amin.nazemian@strath.ac.uk) 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. Keywords: systematic series; machine learning; lackenby variation method; self-blending method; genetic algorithm 1. Introduction The EU-funded project “TrAM-Transport: Advanced and Modular” develops batterydriven zero-emission fast passenger vessels for coastal areas and inland waterways. Modular design and manufacturing methods are the focus of this project, with the objectives of minimizing environmental impact and life cycle cost [1,2] . The development of a systematic series of zero-emission catamaran hullforms for different displacement tonnage and ship types can significantly help this process. Enormous catamaran hullforms will be generated during the systematic series development, and resistance calculation takes time for each design. An accurate and fast resistance predictor leads to a convenient tool for a class of hullforms. Therefore, a new model for such diversity with appropriate generalization to new predictions is desired in this field, leading to data mining approaches [3] . ML can be combined with optimization algorithms to efficiently explore the design space and identify optimal or near-optimal solutions. Genetic algorithms, par

FAQs

Which machine learning models are used for calm water resistance prediction?

Regression Trees, Support Vector Machines, and Artificial Neural Network regression models are trained on the dataset to predict calm-water resistance.

How is hullform design space explored and new hullforms generated?

Design space distribution uses Lackenby transformation, and a sequential self-blending method reconstructs new hullforms by blending two parent designs.

How does Genetic Algorithm optimization use the ML predictor, and what improvement is reported?

The ML fast resistance calculator provides objective evaluations inside the GA loop. On a 40 m passenger catamaran case study, the optimization achieves a 9.5% cost-function improvement.

Aug 4, 2026

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