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

Prediction model of ship air curtain drag reduction based on FAGSO-SVM
Gaolun Zhang, Jinshu Lu, Qidi Gao

School of Naval Architecture and Maritime, Zhejiang Ocean University.
Zhoushan, Zhejiang, China

wave-making resistance, and viscous pressure resistance. For lowspeed ships, the frictional resistance is about 70% ~ 80%, and for highspeed ships, the frictional resistance is about 50%. Drag reduction bybubble curtain technology is a novel energy-saving technology and aneffective drag reduction method to reduce ship friction. Drag reduction
by bubble curtain is a layer of tiny bubbles covering the surface of theship behind the jet exhaust. Generally, the layer of the air curtain is thin(Dong, 1998). It was first verified that gas could reduce resistance byproducing hydrogen on the rotary body through electrolysis in therotary body experiment (ME, 1973). Later, the results of the slab
experiment show that the drag reduction effect of the slab is notaffected by the size of the two air exhaust holes, which is different fromthe drag reduction results of the rotary body. The drag reduction effectof the lower surface of the slab is better than that of the upper surface.
When the airflow is constant, the drag reduction effect decreases with
the increase of the water flow velocity (Madavannk, 1984; Deutschs,1989; Merklecl, 1992). A Japanese scholar made a systematic
experimental study on plate gas drag reduction technology and foundthat the bubble size of the slab jet didn’t affect the drag reduction effect.
The main factors that affect the drag reduction rate are the airflow andthe incoming flow velocity. Different air injection methods have acertain impact on the drag reduction effect (Katoh, 1998; Moriguchiy,2002). Through the catamaran experiment, the impact of air injection
speed on drag reduction was quantified, indicating that excessive airinjection reduced the drag reduction effect, and appropriate injection
could make the drag reduction effect optimum (Sayyaadi, 2013). The
traditional analysis method can not take into account the variables
affecting the effect of drag reduction by bubble curtain, so thedevelopment rate of gas drag reduction is very slow for a long time.

ABSTRACT
To solve the nonlinear problem between optimal bubble volume andflow velocity in the process of ship drag reduction by bubble curtain as
well as the poor prediction effect of the traditional support vectormachine (SVM) model, a ship drag reduction by bubble curtain
prediction model based on gravity operator optimization fireworks
algorithm support vector machine (FAGSO-SVM) was proposed. In
this model, the improved fireworks algorithm is used to optimize theparameters of the support vector machine to obtain a higher prediction
accuracy. The resistance ratios corresponding to 10 different bubbles
volumes at 5 flow velocities were obtained through the plate modelexperiment. The FAGSO-SVM prediction model was establishedaccording to the experimental data, and the prediction results werecompared with those of the traditional support vector machine model.
Finally, the relationship between the optimal bubble volume and theflow velocity was calculated according to the model. The results showthat the ship drag reduction by bubble curtain prediction model basedon the FAGSO-SVM has a small mean square error and high reliability,
and at the same time, it has higher prediction accuracy and stability
than traditional support vector machine models. Therefore, it is feasible
to apply support vector machine optimization based on gravity operator
fireworks algorithm to ship drag reduction by bubble curtain
technology, because it can provide an effective method to realize theintelligent algorithm of drag reduction by bubble curtain.

KEY WORDS: Drag reduction by bubble curtain; support vector
machine; FAGSO-SVM prediction model.

INTRODUCTION
Shipping, as the main mode of transportation used in international trade,
accounts for 60% of the total volume of international trade. The
sustained economic development of various countries as well as therapid growth of international trade bring great challenges to global
environmental governance. Therefore, the International Maritime
Organization constantly updated various mandatory provisions of ship
pollution prevention, so that the energy conservation and emission
reduction technology of existing ships were constantly updated to meet
new requirements. Ship resistance mainly includes frictional resistance,

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Support vector machine is a machine learning method developed under
the framework of statistical learning theory (Vapnik, 1998; Vapnik,1999). Support vector machine is based on the principle of structuralrisk minimization. Compared with neural networks, it has a strict
theoretical and mathematical basis, and there is no local minimumproblem. Small sample learning also has strong generalization ability,
and its dependence on sample size is weak. Meanwhile, the support
vector regression estimation model does not tend to eliminate somelarge regression errors but considers the smoothness of the regression
curve as a whole, which can effectively guarantee the generalization
ability of the model. Support vector machine parameters have a great