Next Article in Journal
Machine Learning-Based Recursive Prediction and Application of Green’s Function of Water-Wave Radiation and Diffraction
Previous Article in Journal
A Preliminary Assessment of Offshore Winds at the Potential Organized Development Areas of the Greek Seas Using CERRA Dataset
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning

by
Hamid Reza Soltani Motlagh
1,2,
Seyed Behbood Issa-Zadeh
3,*,
Md Redzuan Zoolfakar
1 and
Claudia Lizette Garay-Rondero
4
1
Institut Teknologi Malaysia Kejuruteraan Marin, Universiti Kuala Lumpur, Lumut 32200, Perak, Malaysia
2
International Maritime College Oman, National University of Science and Technology, Sohar P.O. Box 532, Oman
3
School of Maritime Science, University of Gibraltar, Campus Europa Point, Gibraltar GX11 1AA, UK
4
Institute for the Future of Education, School of Engineering and Sciences Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(8), 1487; https://doi.org/10.3390/jmse13081487
Submission received: 25 June 2025 / Revised: 28 July 2025 / Accepted: 30 July 2025 / Published: 31 July 2025
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)

Abstract

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO2 emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship.
Keywords: shipping industry; fuel consumption; machine learning; monte carlo simulations; maritime; sustainability shipping industry; fuel consumption; machine learning; monte carlo simulations; maritime; sustainability

Share and Cite

MDPI and ACS Style

Soltani Motlagh, H.R.; Issa-Zadeh, S.B.; Zoolfakar, M.R.; Garay-Rondero, C.L. Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning. J. Mar. Sci. Eng. 2025, 13, 1487. https://doi.org/10.3390/jmse13081487

AMA Style

Soltani Motlagh HR, Issa-Zadeh SB, Zoolfakar MR, Garay-Rondero CL. Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning. Journal of Marine Science and Engineering. 2025; 13(8):1487. https://doi.org/10.3390/jmse13081487

Chicago/Turabian Style

Soltani Motlagh, Hamid Reza, Seyed Behbood Issa-Zadeh, Md Redzuan Zoolfakar, and Claudia Lizette Garay-Rondero. 2025. "Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning" Journal of Marine Science and Engineering 13, no. 8: 1487. https://doi.org/10.3390/jmse13081487

APA Style

Soltani Motlagh, H. R., Issa-Zadeh, S. B., Zoolfakar, M. R., & Garay-Rondero, C. L. (2025). Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning. Journal of Marine Science and Engineering, 13(8), 1487. https://doi.org/10.3390/jmse13081487

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop