Water management has been an unavoidable problem for proton exchange membrane fuel cells (PEMFCs), and effective water management measures can improve the performance and extend the lifespan of the PEMFC.

Hydrogels are widely used in many applications due to their hydration environment and tunable properties (e.g., mechanical, chemical, and biocompatibility) similar to that of natural extracellular matrices. Upon exposure to some specific stimuli, hydrogels can undergo shape deformation or even state transitions, and these stimulus-responsive hydrogels are constantly being developed, with promising applications in biomedicine, micromechanics, soft actuators, and robotics. In recent years, there have been increasing studies on stimulus-responsive smart hydrogels, which incorporate driving units in previously studied high-strength hydrogels, allowing the hydrogels to not only retain their original excellent mechanical properties, but also have good driving properties. Herein, by analyzing the driving mechanism and driving modes of hydrogels, the latest progress in smart hydrogels with six different driving modes, such as temperature-driven, light-driven, pH-driven, substance-specific-driven, magnetic-driven, and hybrid-driven hydrogels, is explored. Also, the preparation, structure, and properties of hydrogels are reviewed in detail. Finally, the current challenges and future prospects for shape-deformation stimulus-responsive hydrogels are discussed.

To reduce the adverse damage caused by cavitation phenomena to the hydraulic machinery, such as surface erosion of the equipment, increased mechanical vibration, and decreased service life, this review summarizes from the aspects of cavitation instability mechanisms, cavitation prediction methods, and cavitation suppression methods. In terms of cavitation flow instability mechanisms, two main mechanisms that affect the shedding of cloud cavitation, reentrant jet, and bubbly shock wave, were thoroughly summarized. It is pointed out that the shedding behavior of the cavity is greatly influenced by the thickness of the reentrant jet relative to the cavity, and the bubbly shock wave is also one of the important factors in cavitation vortex dynamics. In terms of cavitation prediction methods, a detailed comparison and analysis were made between the traditional cavitation prediction methods based on numerical simulation and the currently popular cavitation prediction methods based on neural networks. The former mainly includes cavitation models and turbulence models, while the latter mainly summarizes the application of chain physics-informed neural network, pressure–velocity network, long short-term memory, and other neural networks in cavitation prediction. It is pointed out that artificial intelligence predictive models have advantages in model order reduction and accurate prediction of cavitation flow field feature parameters. In terms of cavitation suppression methods, active and passive cavitation suppression methods were thoroughly summarized. Finally, based on the current research status of hydrofoil cavitation prediction methods and cavitation suppression methods, this article discusses and looks forward to the direction of development.

Centrifugal pumps are important equipment in industrial production. At present, vibration signals are often used to diagnose cavitation in centrifugal pumps, but the vibration signals are easy to be disturbed and the fault characteristics are unstable to be detected. In this paper, a single stage centrifugal pump is taken as the study object, and the vibration signals of various parts of the centrifugal pump cavitation state are collected under different flow conditions. The short-time Fourier transform and one-third octave analysis are performed on the filtered signals, and the characteristic frequency of cavitation and the energy near the characteristic frequency with the development of cavitation are obtained. Based on vibration signals, the vibration root mean square (rms) and kurtosis values of different cavitation states are obtained. Flow state, kurtosis, and rms are used as input variables in the double-layer backpropagation neural network model to identify and classify the cavitation states of centrifugal pumps. The results show that the trained neural network model can accurately identify and classify the cavitation state of the centrifugal pump under the conditions of low flow rate, rated flow rate, and large flow rate, and the accuracy is more than 99.5%. This study provides a new technique for diagnosing cavitation in centrifugal pumps.

A hydrofoil physical model is established based on the surface microstructure to mitigate the detrimental effects of cavitation phenomena on hydrodynamic machinery, such as cavitation erosion or surface damage. Tangential microjet structures are arranged on the hydrofoil's surface, and the modified k-omega shear stress transport (SST k–ω) turbulence model is employed to simulate the hydrofoil numerically. This simulation aims to analyze the effects of different chordwise positions and widths of microjet structures on the cavitation flow and performance of hydrofoils. The mechanism of cavitation suppression is revealed by coupling the chordwise position and width of the microjet structures. The results indicated that the chordwise position of the microjet structures near the trailing edge of the hydrofoil has a minimal impact on the hydraulic properties. The optimal chordwise positions are 0.5c and 0.6c, with the deviation rate of the lift-drag ratio within 3%. The optimum jet width is 0.5 mm, and the cavitation suppression is approximately 15% of the prototype hydrofoil. The microjet structures with tangential jets suppress cavitation by creating obstruction and suppression of the re-entrant jet. The tangential jet ratio of 0.3 represents the most effective tangential jet hydrofoil scheme, and the addition of tangential jets produces a significant inhibitory effect on the shedding of large-scale cavitation.

Proton exchange membrane fuel cells (PEMFCs) are attracting attention for their green, energy-saving, and high-efficiency advantages, becoming one of the future development trends of renewable energy utilization. However, there are still deficiencies in the gas supply system control strategy that plays a crucial role in PEMFCs, which limits the rapid development and application of PEMFCs. This paper provides a comprehensive and in-depth review of the PEMFC air delivery system (ADS) and hydrogen delivery system (HDS) operations. For the ADS, the advantages and disadvantages of the oxygen excess ratio (OER), oxygen pressure, and their decoupling control strategies are systematically described by the following three aspects: single control, hybrid control, and intelligent algorithm control. Additionally, the optimization strategies of the flow field or flow channel for oxygen supply speeds and distribution uniformity are compared and analyzed. For the HDS, a systematic review of hydrogen recirculation control strategies, purge strategies, and hydrogen flow control strategies is conducted. These strategies contribute a lot to improving hydrogen utilization rates. Furthermore, hydrogen supply pressure is summarized from the aspects of hybrid control and intelligent algorithm control. It is hoped to provide guidance or a reference for research on the HDS as well as the ADS control strategy and optimization strategy.