Version 1
: Received: 30 November 2023 / Approved: 1 December 2023 / Online: 1 December 2023 (08:09:59 CET)
How to cite:
Nazemian, A.; Boulougouris, E.; Aung, M.Z. A Systematic Series Development and Calm Water Resistance Prediction for a Fast Catamaran Ferry Utilizing Machine Learning Tools. Preprints2023, 2023120049. https://doi.org/10.20944/preprints202312.0049.v1
Nazemian, A.; Boulougouris, E.; Aung, M.Z. A Systematic Series Development and Calm Water Resistance Prediction for a Fast Catamaran Ferry Utilizing Machine Learning Tools. Preprints 2023, 2023120049. https://doi.org/10.20944/preprints202312.0049.v1
Nazemian, A.; Boulougouris, E.; Aung, M.Z. A Systematic Series Development and Calm Water Resistance Prediction for a Fast Catamaran Ferry Utilizing Machine Learning Tools. Preprints2023, 2023120049. https://doi.org/10.20944/preprints202312.0049.v1
APA Style
Nazemian, A., Boulougouris, E., & Aung, M.Z. (2023). A Systematic Series Development and Calm Water Resistance Prediction for a Fast Catamaran Ferry Utilizing Machine Learning Tools. Preprints. https://doi.org/10.20944/preprints202312.0049.v1
Chicago/Turabian Style
Nazemian, A., Evangelos Boulougouris and Myo Zin Aung. 2023 "A Systematic Series Development and Calm Water Resistance Prediction for a Fast Catamaran Ferry Utilizing Machine Learning Tools" Preprints. https://doi.org/10.20944/preprints202312.0049.v1
Abstract
The aim of article is to design a calm water resistance predictor based on Machine Learning Tools and development of a systematic series for battery-driven catamaran hull forms. Regression Trees (RT), Support Vector Machines (SVM), and Artificial Neural Network (ANN) regression models are applied for dataset training on developed systematic series of catamarans. A hullform optimization was implemented for various catamarans including dimensional and hull coefficient parameters based on resistance and structural weight reduction and battery performance improvement. This paper provides a diverse database of catamaran hullform. Hence, an automated Matlab program was coded for geometry generation and cost function evaluation. Design distribution based on Lackenby transformation fulfills all 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 generated data of case study. This study shows that ANN algorithm correlates well with the measured resistance. Accordingly, a general and unique tool is proposed for optimized and desired design in first design stage.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
