In the context of carbon neutrality, the low-carbon development of ships has become the consensus of shipbuilding and shipping circles all over the world, and green ship technology will play an important role in the development of low-carbon ships. As a kind of green technology for the shipping industry, ship optimum trim energy-saving technology does not need to modify the ship, but only needs to adjust the ship's bow and stern draft to reduce resistance. This report proposes a trim optimization method for oil tankers based on the neural network model, which can quickly obtain the optimum trim state of the oil tanker with the minimum resistance during operation. Firstly, combined with the trim optimization data of oil tanker model, backpropagation neural network, radial basis function neural network, GA-BP neural network, and generalized regression neural network were trained respectively, and four prediction models for resistance of oil tanker model were established. Then, by analyzing and comparing the performance of the validation set on the four neural network models, the model with the best performance was selected as the trim optimization prediction model. Finally, the eigenvalue data corresponding to the training data set during the operation of the oil tanker was input into the trim optimization prediction model to obtain the optimum trim with the minimum resistance of the oil tanker. The research on oil tanker trim optimization based on neural network models is of great significance to energy conservation and emission reduction of the shipbuilding industry, and to promote the green development of ships.


Oil plays an increasingly important role in world trade as its increasingly widespread use as a source of energy (Ag et al., 2020; Michail et al., 2020; Zhao et al., 2021), as one of the most important ways of oil transportation, the demand for oil tanker transportation is also increasing rapidly (Lun et al., 2013). A large amount of polluting gases emitted during the operation of ships and a share of energy consumption in the operating costs (Jia et al., 2018) have forced shipping companies to consider improving the energy efficiency of ships. As one of the ways to improve the energy efficiency of ships, the optimum trim energy-saving technology is favored by shipping companies and scholars in the field of shipbuilding because of its advantages of not being affected by the types of ships and not needing to change the structure. Scholars in the field of shipbuilding have conducted a series of model simulations of ship optimum trim energy-saving technology. Sherbaz et al. (2014) used the calculation methods to optimize the trim of the KCS container ship, compared the calculated values with the experimental values, found reasonable consistency between the two kinds of values, and found the optimum trim with minimum resistance. Moustafa et al. (2015) used bulk carriers as the research object, and it is verified that the ship's resistance can be reduced by optimizing the trim, thereby minimizing fuel use. Sun et al. (2016) used CFD combined with model tests to carry out a study on the trim optimization of 4250TEU container ships. And developed a trim optimization system to guide the actual ship navigation. Lv et al. (2013, 2018) applied the surface element method based on the potential flow theory to calculate the wave-making resistance at different trim conditions and found the optimum trim angle by using the trim optimization response surface. The research described above indicates that the research on trim optimization is mainly carried out by combining ship model tests and numerical calculations.

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