# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
[PERSON] and [PERSON] [7] integrated CAESES with CFD to establish a completely automated framework for parametric modeling, hydrodynamic analysis, design assessment, and shape alteration. Recent scholarly studies on ship resistance utilizing computational fluid dynamics have predominantly focused on the following three components: ship modeling, CFD simulation, and optimization design. [PERSON] et al. [8] designed a novel bulbous bow and demonstrated through CFD methods that it influences both the pressure distribution on the ship's free surface and the wave pattern under full load conditions. [PERSON] [9] provided a reverse design methodology to examine the hydrodynamic performance resulting from the removal of the bulbous bow in a fishing vessel, offering a novel perspective for ship design concepts. Nonetheless, as the current ship simulation calculations advance, efficiency concerns are increasingly becoming a factor. Design optimization utilizing the CFD method frequently necessitates numerous iterative calculations, potentially resulting in significant expenses for intricate flow issues. [PERSON] [10] applied NURBS integral formulas to hull parameters, simplifying the computation of the cross-sectional area curve, design water-line, and transverse sections to meet engineering requirements. [PERSON] et al. [11] noted that achieving high accuracy with CFD approaches requires substantial computational resources, posing a paradoxical challenge. CFD relies on commercial simulation software, requiring the purchase of usage rights, which incurs costs. Machine learning has been extensively employed in the field of ship engineering and has undergone rapid development in recent years. Many researchers have employed surrogate models to approximate CFD simulations in response to these issues. The optimal longitudinal inclination of a container ship was determined by [PERSON] et al. [12] through a comparison of various models. Subsequently, they proposed a method that can rapidly predict the optimal longitudinal inclination of an arbitrary container ship. The random forest prediction model was selected as the optimal prediction model. [PERSON] [13] utilized regression tree (RT), support vector machine (SVM), and artificial neural network (ANN)regression models to develop a predictor of calm water resistance for catamaran hull forms. The ship performance was optimized for various types, including resistance-based dimension and hull coefficient optimization, as well as structural weight and battery performance improvements. Based on their research, the model's performance may vary significantly across different ship data. This introduces additional workload, and a universal model with more features is urgently needed. [PERSON] et al. [14] optimized planing hulls at high speeds by focusing on lift and complex hydrodynamic interactions, identifying the optimal hull shape to minimize resistance using a Kriging surrogate model and the Nelder-Mead algorithm. [PERSON] [15] introduced a deep trust network algorithm for estimating the total resistance of various hull modifications. This algorithm was compared with traditional models, namely ELMAN and RBF neural networks, revealing that the deep trust network algorithm effectively achieves the optimal solution for minimizing the total resistance across different speeds. The aforementioned article illustrates the advantages of surrogate models in ship design; however, the impact of the localized hull design on resistance remains significant owing to the multi-layered structure of the hull. [PERSON] et al. [16] conducted an optimal design for the calm water resistance of a high-speed catamaran hull, employing a Kriging model to create a new bulbous bow and utilizing a genetic algorithm to identify the two best vessels with minimal wave resistance. [PERSON] et al. [17] employed the dimensions of the bulbous bow, specifically its length and width, along with the angle relative to the baseline, as optimization variables.