Hydrodynamic noise is induced by hydrodynamic phenomena, such as pressure fluctuations, shear layers, and eddy currents, which have a significant impact on ship performance, pumping equipment efficiency, detection accuracy, and the living environment of marine organisms. Specifically, hydrodynamic noise increases fluid resistance around the hull, reduces speed and fuel efficiency, and affects the stealthiness of military vessels; whereas, in pumping equipment, noise generation is usually accompanied by energy loss and mechanical vibration, resulting in reduced efficiency and accelerated wear and tear of the equipment. Traditional physical experiments, theoretical modeling, and numerical simulation methods occupy a key position in hydrodynamic noise research, but each have their own limitations: physical experiments are limited by experimental conditions, which make it difficult to comprehensively reproduce the characteristics of the complex flow field; theoretical modeling appears to be simplified and idealized to cope with the multiscale noise mechanism; and numerical simulation methods, although accurate, are deficient in the sense that they are computationally expensive and difficult to adapt to complex boundary conditions. In recent years, intelligent algorithms represented by data-driven algorithms and heuristic algorithms have gradually emerged, showing great potential for development in hydrodynamic noise optimization applications. To this end, this paper systematically reviews progress in the application of intelligent algorithms in hydrodynamic noise research, focusing on their advantages in the optimal design of noise sources, noise prediction, and control strategy optimization. Meanwhile, this paper analyzes the problems of data scarcity, computational efficiency, and model interpretability faced in the current research, and looks forward to the possible improvements brought by hybrid methods, including physical information neural networks, in future research directions. It is hoped that this review can provide useful references for theoretical research and practical engineering applications involving hydrodynamic noise, and point the way toward further exploration in related fields.

Inspired by the Tesla valve, the island-type fishway is a novel design whose biological performance remains unelucidated. This study integrated hydraulic experiments, CFD modeling, and 3D computer vision to investigate the passage performance and swimming behavior of juvenile silver carp (Hypophthalmichthys molitrix). The results confirmed high biological feasibility, with upstream success rates exceeding 70%. The island and arc-baffle configuration create a heterogeneous flow field with an S-shaped main flow and low-velocity zones; each island unit contributes 8.9% to total energy dissipation. Critically, fish utilize a multi-dimensional navigation strategy to avoid high-velocity cores: temporally adopting an intermittent “rest-burst” pattern for energetic recovery; horizontally following an “Ω”-shaped bypass trajectory; and vertically preferring the bottom boundary layer. Passage failure was primarily linked to suboptimal path selection near the high-velocity main flow. These findings demonstrate that fishway effectiveness depends less on bulk hydraulic parameters and more on the spatial connectivity of hydraulic refugia aligning with fish behavioral traits. This study provides a scientific basis for optimizing eco-friendly hydraulic structures.

Pumping systems serve as critical fluid transportation facilities, accounting for a significant proportion of global electricity consumption. In engineering applications, parallel pumping operation is a standard solution to meet large flow and variable operating/load demands. Accordingly, an energy-optimal control strategy for parallel pumping systems is proposed, where optimal frequency switching points are used to schedule pump start/stop decisions and their timing. This strategy is based on the principle of equal-speed operation for identical pumps, and its solution constitutes a complex non-linear constrained optimization problem. To efficiently solve this problem, a customized hybrid intelligent optimization algorithm (ABCG) is developed. By redesigning the initialization, boundary handling, and search operators, the algorithm improves robustness in global optimization. Experimental results indicate that the proposed ABCG algorithm ranks first in the Friedman mean rank tests on benchmark functions and demonstrates rapid convergence. Furthermore, based on this strategy, the optimal frequency switching points were quantitatively determined to be 39 Hz (for single-to-dual pump transition) and 43 Hz (for dual-to-triple pump transition). Compared to traditional sequential operation, the new strategy achieves a maximum relative energy-saving rate of 13.05%, validating the feasibility and effectiveness of this active switching control scheme in practical industrial processes.

Cavitation-induced high-frequency pressure responses are closely associated with vibration, noise, and erosion in marine and hydraulic machinery, but elevated high-frequency energy may originate from either intermittent collapse-related events or sustained broadband disturbances. This study develops an energy–impulsiveness joint diagnostic framework to identify and differentiate collapse-related response regions in a slit Venturi flow. Normalized high-frequency energy quantifies band-limited energy accumulation, while peak spectral kurtosis characterizes temporal intermittency. Large-eddy simulation, pressure measurements, and high-speed imaging are combined for validation and interpretation. The numerical model predicts the steady pressure drop with a maximum relative error below 7%, and the dominant shedding frequency differs from the experiment by 5.3%. Two spatially separated response regions are identified. S5–S6 exhibits simultaneous enhancement of energy and impulsiveness, associated with re-entrant-jet-driven rupture and rapid vapor-cloud contraction. S8–S10 maintains elevated energy but weaker impulsiveness, corresponding to cloud transport, shear-induced stretching, and sustained fragmentation. Event-aligned vapor-fraction analysis shows that upstream contraction precedes delayed downstream responses, indicating that they represent dynamically connected stages of a single cavitation-cloud evolution process. The framework provides a quantitative basis for spatial response classification and pressure-sensor placement in confined cavitating flows.

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