## 5 Discussion and Conclusions
### Scalability of Intelligent Algorithms  
The implementation of intelligent algorithms in the context of hydrodynamic noise problems frequently necessitates the analysis of multidimensional data and the execution of large-scale computations. Conventional algorithms frequently encounter challenges characterized by low efficiency and inadequate scalability when confronted with intricate flow fields. In the context of substantial datasets, the employment of efficient multi-objective optimization and parallel computing strategies has emerged as a critical means to enhance the efficacy of algorithms. Research has demonstrated that employing parallel computing techniques in fluid mechanics problems can effectively reduce computing times, while preserving a model's high accuracy. Additionally, deep learning methods have demonstrated notable efficacy in high-complexity scenarios, due to their adaptability to high-dimensional data and their capacity for strong nonlinear fitting. The further application of AI technology hinges on the optimization of existing computing resources and the enhancement of computing efficiency. For instance, [PERSON], H. employed a large-scale eddy current simulation in conjunction with the Lighthill acoustic analogy to achieve high-precision noise prediction outcomes in the context of underwater noise research. However,it should be noted that this method incurs a high computational cost, rendering it ill-suited for large-scale real-time applications. The application of intelligent algorithms to hydrodynamic noise problems generally necessitates the processing of multidimensional data and the execution of large-scale computations, thereby rendering their computational efficiency and resource utilization a pivotal concern. Conventional optimization algorithms frequently exhibit deficiencies in efficiency and scalability when confronted with intricate flow fields. This is particularly pronounced when dealing with voluminous datasets encompassing multiple objectives and high-dimensional data, wherein traditional methodologies encounter challenges in fulfilling the demands of real-time computation and precise prediction. Consequently, the development of efficient multi-objective optimization and parallel computing strategies has become imperative, to enhance the performance of algorithms in addressing these challenges. For instance, it has been demonstrated that the integration of parallel computing techniques can lead to substantial reductions in computing time in problems related to fluid dynamics, while maintaining the accuracy of the model [103]. Parallel computing has the capacity to effectively address both large-scale data and multi-dimensional and multi-objective optimization problems. In the study of fluid noise, the accurate prediction of noise sources often necessitates the modeling and analysis of a substantial volume of hydrodynamic data. This necessitates the development of algorithms capable of an expeditious response and the generation of precise results within constrained timeframes. The utilization of parallel computing facilitates the distribution of data processing across multiple computing units, thereby enhancing the efficiency and precision of the overall process. The utilization of multi-core and distributed computing capabilities in contemporary computing systems enables the expeditious execution of large-scale computing tasks. Moreover, the deep learning method has been shown to possess superior adaptive capabilities and strong nonlinear fitting abilities. This method has been shown to excel in high-complexity problems. A key strength of deep learning models lies in their capacity to automatically extract noise features from vast repositories of experimental and simulation data. They can effectively predict the distribution of flow fields and sound fields, especially in the face of high-dimensional data, and can carry out more accurate fitting and optimization.