In this research, the cavitating flow around a NACA0015 (National Advisory Committee for Aeronautics) hydrofoil obtained by the large-eddy simulation method is analyzed using the proper orthogonal decomposition (POD) theory. Various fundamental mechanisms have been investigated thoroughly, including the reentrant jet behavior, pressure gradient mechanism, vortex dynamics, and dynamic properties of the hydrofoil. The influence of the vortex dynamics, pressure mechanism, and temporal/spatial evolution is revealed. The POD decomposition indicates that the first four dominant POD modes occupy 97.4% of the entire energy. Based on the vortex force field extracted from the first four single POD modes, it is found that the lift-and-drag characteristics in the cavitating flow are determined by the specific spatial distribution of mode vortex structures. In addition, the coupling of velocity pulsations and pressure fluctuations is carried out to obtain the POD modal pressure gradient field, which reveals that the pressure gradient has a close connection with the cavity evolution. Furthermore, the vortex force and pressure gradient are reconstructed using the first four modes, 17 modes, and 160 modes, which indicates that the low-order POD modes without the impact of small-scale structures and noise can clearly capture the fundamental aspects of the flow field.

The short-term scheduling of pumped-storage hydropower plants is characterised by high dimensionality and nonlinearity and is subject to multiple operational constraints. This study proposes an intelligent scheduling framework that integrates an Atomic Orbital Search (AOS)-optimised Long Short-Term Memory (LSTM) network with the Deep Deterministic Policy Gradient (DDPG) algorithm to minimise water consumption during the generation period while satisfying constraints such as system load and safety states. Firstly, the AOS-LSTM model simultaneously optimises the number of hidden neurons, batch size, and training epochs to achieve high-precision fitting of unit flow–efficiency characteristic curves, reducing the fitting error by more than 65.35% compared with traditional methods. Subsequently, the high-precision fitted curves are embedded into a Markov decision process to guide DDPG in performing constraint-aware load scheduling. Under a typical daily load scenario, the proposed scheduling framework achieves fast inference decisions within 1 s, reducing water consumption by 0.85%, 1.78%, and 2.36% compared to standard DDPG, Particle Swarm Optimisation, and Dynamic Programming methods, respectively. In addition, only two vibration-zone operations and two vibration-zone crossings are recorded, representing a reduction of more than 90% compared with the above two traditional optimisation methods, significantly improving scheduling safety and operational stability. The results validate the proposed method’s economic efficiency and reliability in high-dimensional, multi-constraint pumped-storage scheduling problems and provide strong technical support for intelligent scheduling systems.

In the study of horizontal axis tidal turbines (HATTs), extensive research has been conducted on wake flow fields, yet the relationship between vortices and energy loss has not been thoroughly elucidated. To better investigate the mechanism of wake energy loss and enhance the efficiency of tidal current turbines, this study is based on entropy generation theory, exploring the relationship between energy loss and vortex structures in HATTs, as well as the spatial distribution of wake energy loss under different tip speed ratio (TSR) conditions. Furthermore, a quantitative analysis of the variation in wake energy loss is conducted. The results indicate that the total energy loss initially decreases and then increases with the increase in TSR, which is consistent with the interval where the power coefficient first increases and then decreases with TSR. Tip vortices are the primary factors affecting the distribution of energy loss in the wake region. The entropy production rate (EPR) curve in the wake region exhibits three peaks, with EPR in the wake region showing a centrally symmetric distribution along the x axis. Hub vortices and separation vortices only cause energy loss in a small section behind the blades and do not spread to the main wake region, thus having minimal impact on the primary wake region. Near the optimal operating condition, the maximum peak and major area of the EPR curve occur in the middle section of the wake region, while at lower and higher TSRs, the maximum peak and major area of the EPR curve appear in the front section of the wake region.

Ventilated cavitation is a complex flow phenomenon that involves multi-phase flow, coupled vortex evolution, and turbulent pulsation. This paper employs the large eddy simulation method to systematically investigate the ventilated cavitation flow of an underwater vehicle equipped with a disk cavitator. The results indicate that the ventilation rate (Cq) significantly influences the cavity morphology and drag. It was observed that the closure mode of the ventilated cavity transitioned consistently with increasing Cq under Fr = 10.2, shifting from a reentrant jet to a twin vortex tube. Additionally, the key factors contributing to the formation of the twin vortex tube have been identified, which is the effects of gravity and local adverse pressure gradient. Furthermore, mechanical properties in twin vortex closure mode were analyzed, finding that the vehicle's drag and stability were influenced by the shedding of vortex structures and the dynamic deformation of the gas–liquid interface. Finally, the distribution characteristics of the modal vortex force were analyzed using the proper orthogonal decomposition method, providing a theoretical foundation for optimizing the lift and drag distribution of the vehicle.