Proceedings of the Thirty-second (2022) International Ocean and Polar Engineering Conference
Shanghai, China, June 5–10, 2022
Copyright © 2022 by the International Society of Offshore and Polar Engineers (ISOPE)
ISBN 978-1-880653-81-4; ISSN 1098-6189

www.isope.org

Optimum trim prediction of bulk carrier based on machine learning
Kai Xia1, 2, Lin Mu1, 3, Haiwen Tu*, 1, 2, 4, Jianglong Sun*, 5
1 Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
2 College of Marine Science and Technology, China University of Geosciences, Wuhan, China
3

College of Life Science and Oceanography, Shenzhen University, Shenzhen, China
Shenzhen Research Institute, China University of Geosciences, Shenzhen, China
5 School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, China
4et al. 2017; Qiang et al. 2019; Zhao et al. 2021). Trim optimization ofships does not require changes to the hull structure or the additional
installation of equipment as an energy-saving measure, and without
affecting the ship's load or speed, only the trim angle during sailing mustbe altered, thereby reducing resistance and achieving energy savings(Duan et al., 2019; Gao et al., 2019). In comparison to other energysaving and emission-reduction technologies, the ship's trim optimizationenergy-saving technology is simple to implement, requires little initialinvestment, and is extremely convenient to operate, making it a growing
shipping industry priority. As a result, it is critical to conduct research on
ship trim energy-saving technology (Bertram et al., 2010; Sherbaz et al.,2012; Hu et al., 2019).
Reinius (2014) studied the effect of trim on fuel consumption of reefer
vessels and showed that economic savings can be achieved by operatinga reefer vessel with optimum trim over a longer period. Sun et al. (2016)used CFD combined with model tests to study the trim optimization of4250TEU container ships and developed a trim optimization programsystem to guide the actual ship navigation. Lv et al. (2013, 2018) used apotential-based panel method coupled with a response surface method to
calculate the wave-making resistance of ships under various trim statesand obtained the optimum trim value with the minimum wave-making
resistance; the optimum trim angle corresponding to the minimum
resistance was determined based on the trim optimization response
surface. The research described above indicates that the currently
available optimum trim energy-saving technology is necessary for a
specific ship draft; ship resistance model tests and numerical calculations
are commonly performed by considering various speeds and trim angles,
and can only obtain the optimum trim state under the working conditions
included in the ship model tests. To obtain the optimum trim for new
working conditions, new model tests or numerical calculations arerequired, although this approach would typically be difficult because of
the large workload, popular machine learning methods can be applied tosolve this problem well.
Machine learning is the science of how to use computers to simulate or
implement human learning activities and is one of the most intelligent
and cutting-edge research areas in artificial intelligence. Machine

ABSTRACTIn the context of carbon-neutral and more stringent emissions regulations
implemented by the IMO, green ship technologies, which show the better
effect of saving energy and environmental protection, have attracted
increasing attention. And ship trim optimization, which can easily andconveniently be operated, is nowadays one of the promising energysaving technologies. This paper proposes a method for predicting trim
optimization of a bulk carrier based on Machine Learning (ML). With
the proposed method, the lowest resistance value can be obtained during
operating bulk carriers at optimum trim. Firstly, a subset of most relevantfeatures (including draft, speed, trim angle, and resistance) related to thebulk carrier in the data set were selected as the training data set and threemachine learning algorithms were utilized to predict the optimum trim
angle at which minimum resistance occurs. Then, the performance ofmachine learning algorithms in forecasting the optimum trim angle wasevaluated by the metrics, and the algorithm that gives the highest
accuracy was used to construct a predictive model on the optimum trim.
Finally, the features about the bulk carrier from the testing data set wereselected as the input to the predictive model, and the predicted minimum
resistance value can be yielded during operating bulk carriers at optimum
trim. The machine learning-based model for predicting optimum trim caneffectively provide predicted results of optimum trim for bulk carriers ina specified state, and thus, the reasonable operation with bulk carriers,
the purpose of energy saving, and encouragement of developing green
ship technology can be achieved.

KEY WORDS: Machine learning; Optimum trim; Energy saving andemission reduction
INTRODUCTION
With global warming and stricter environmental regulations from theIMO, energy conservation and emission reduction have become criticalconsiderations for ship operators (Lei et al. 2013; Cui et al. 2014; Walsh**

Corresponding author: Haiwen Tu, E-mail: tuhw@cug.edu.cn
Corresponding author: Jianglong Sun, E-mail: sundapao@hust.edu.cn

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