## 1 Introduction
Consequently, there is a compelling need to enhance the acoustic environment in the workplace to safeguard the health of workers and optimize their productivity. In recent years, significant advancements have beenwitnessed in the realm of hydrodynamic noise prediction and control, with a growing body of research focusing on these areas. Advancements in related technologies have enabled the development of effective noise control strategies and the promotion of sustainable engineering practices. A comprehensive understanding of the generation mechanisms and propagation characteristics of hydrodynamic noise is paramount for the development of effective noise reduction schemes, which can address the challenges encountered in various applications. Conventionally, the study of hydrodynamic noise has been predominantly reliant on physical experiments, theoretical modeling, numerical simulation, and analogous methods. While physical experiments can offer an intuitive understanding of hydrodynamic noise formation and propagation, limitations in experimental conditions frequently preclude full representation of complex flow fields. Theoretical modeling provides a fundamental framework for understanding hydrodynamic noise, while numerical simulation has become the primary research tool. Among the various simulation-based numerical methods derived from the Navier-Stokes equations, direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) methods are the most commonly used [14]. When applied under suitable computational conditions, these methods have demonstrated an ability to accurately depict the mechanisms of noise generation and propagation. However, the applicability of these methods is subject to variation when confronted with high-Reynolds-number flows. While these conventional methods can adequately capture the key features of hydrostatic noise, they frequently necessitate substantial computational resources, due to the inherent nonlinearity and complexity of hydrodynamic problems. Moreover, these conventional techniques are still limited in their ability to effectively address complex boundary conditions and flow field characteristics [15; 16; 17]. Consequently, enhancing computational efficiency and reducing costs has emerged as a pivotal concern for researchers in this field. With the rapid development of artificial intelligence and computer technology, the potential of intelligent algorithms in optimizing the solution of complex system problems has gradually emerged. Intelligent algorithms are not only able to rapidly optimize a large number of nonlinear and multidimensional parametric problems, but are also able to self-learn and improve with the help of historical data [18; 19; 20], which provides new ideas for solving the limitations of traditional methods in hydrodynamic noise prediction and control [21]. In recent years, intelligent algorithms such as data-driven optimization algorithms and heuristic optimization algorithms have become more widely used in the field of noise computation and optimization in complex fluid dynamics [22]. However, the formation of hydrodynamic noise involves the coupling of multiple physical fields. This interdisciplinary field encompasses several areas, including fluid mechanics, acoustics, and structural dynamics. Consequently, research in intelligent algorithms faces numerous challenges. For instance, there is a pressing need to ascertain effective methodologies for leveraging existing datasets for algorithm training, as well as for addressing the coupling between disparate models. Moreover, there is an urgent need to ensure the accuracy of results and the efficiency of calculations. To further cultivate advancements in this domain, it is imperative to engage in a comprehensive discourse on the present state of intelligent algorithms in the realm of hydrodynamic noise, the prevailing challenges, and the prospective avenues for growth and evolution.