# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction  
[PERSON]  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (B.J.) [PERSON]  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (B.J.) [PERSON]  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (Y.S. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (B.J.) [PERSON]  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (B.J.) [PERSON]  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (S.Y. ); [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (L.C. ); [EMAIL_ADDRESS] (B.J.) Liwen Cai  
1 School of Automation, Jiangsu University of Science and Technology, Zhenjiang 212100, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); [EMAIL_ADDRESS] (Q.L. ); 202210301  
own geometry. However, the bulbous bow may be detrimental because of the dominance of viscous resistance at low speeds. In a recent review, [PERSON] [5] analyzed the impact of a bulbous bow on the hydrodynamic performance of a ship, noting that the effective power necessary for the motion of the ship correlates with the speed and resistance. The presence of the bulbous bow reduces the effective power, and its performance is influenced by the shape and the Froude number (\(F_{n}\)). The paper indicates that the beneficial range is between 0.238 and 0.563, thus designing an appropriate bulbous bow is key to reducing the ship's resistance. Early ship resistance prediction mainly relied on empirical formulas, such as the Holtrop formula and the ITTC formula. These formulas typically estimate resistance based on geometric parameters (such as aspect ratio and draft depth) and factors like speed. However, as technology has advanced, modeling tests can now simulate the complex hydrodynamic effects of actual currents. A ship hull using a downsized model can be used in a pool for the towing test to predict the resistance. This method reflects the change in resistance under different ship types and speeds with a higher degree of accuracy. It has the potential to more accurately represent the evolution of resistance as a result of varying ship types and speeds and to progressively replace the empirical formula. [PERSON] [6] conducted towing tests on three kinds of hulls with different bows and proved that the effect of the wave amplitude on the added resistance was not negligible. So the geometry of the hull needs to be taken into account in the resistance calculation. The advent of the CFD method in the early 20 th century offers a novel approach for contemporary ship resistance prediction. The CFD approach utilizes computational fluid dynamics numerical analysis technology to determine ship resistance more accurately by solving the Navier-Stokes equations.