When pump-turbines operate under off-design conditions, complex internal vortex flows often arise, leading to hydraulic instabilities and increased energy losses. Traditional hydraulic loss evaluation methods fail to quantitatively link vortex structures to energy dissipation mechanisms, limiting their ability to guide design improvements. To address this gap, this study proposes a Rortex-enhanced hydraulic loss evaluation framework to systematically analyze vortex dynamics and associated energy dissipation across wide-load turbine-mode operating conditions. By decomposing the dissipation terms in the mean turbulent kinetic energy equation, the framework identifies rigid vorticity-induced dissipation, shear vorticity-induced dissipation, and rotational-shear coupling losses. Numerical simulations, validated by experimental data, show that shear vorticity-induced dissipation dominates overall hydraulic losses, primarily along blade surfaces and boundary layers. Although rigid vorticity-induced dissipation contributes less overall, it increases significantly at lower loads and is concentrated around vortex peripheries due to shear interactions. Energy losses within vortex cores are predominantly driven by rotational-shear coupling effects, strongly correlated with the enstrophy of the pseudo Lamb vector curl term (ERCT). These findings suggest that suppressing ERCT-driven vortex formation and mitigating rotational-shear interactions through optimized blade designs could effectively reduce energy losses and enhance the hydraulic stability and efficiency of pump-turbines.

In the rust removal process via ultra-high-pressure (henceforth UHP) water jet, nozzle translation and rotation significantly affect the jet profile, thereby influencing rust removal performance. Unfortunately, the research on UHP water jets in motion is still rare, lacking systematic theoretical analysis. To address this, 3D numerical simulations incorporating cavitation effect, multiphase flow, and liquid compressibility are conducted to investigate the hydrodynamic performance of UHP water jetting under different translational speeds. The water-jet hydrodynamic characteristics under stationary and translating nozzle conditions are compared in terms of velocity, vortex structure, turbulent kinetic energy, pressure distribution, and impact area. Furthermore, five cases related to different translational speeds are analyzed to examine the influence of motion on jet behavior. Results show that the stationary jets exhibit axisymmetric velocity profiles and vortex structures. When the translational motion is introduced, the jet deflects opposite to the nozzle movement, triggering enhanced shear layer instability. This kind instability serves as a primary driver for the emergences of increased turbulence, vortex deformation, and pressure asymmetry. Consequently, the turbulent kinetic energy distribution broadens and decays more rapidly downstream. Pressure distributions become asymmetric, with high-pressure zones forming upstream and more intense pressure decaying downstream. The effective impact area expands significantly under moderate wall-shear-stress thresholds, but becomes less uniform at higher velocities. An optimal translational speed of approximately 20 m/s can be identified, offering the best trade-off between cleaning performance and energy efficiency. This study provides theoretical support and practical guidance for advancing rust removal technology via UHP water jetting.

Efficient thermal management in confined spaces is a critical requirement in various fields, including biomedical applications like ureteroscopy, where precise temperature regulation is essential for patient safety and procedural efficacy. This study investigates the flow and heat transfer characteristics of copper-water nanofluids within a two-dimensional channel cavity, emphasizing the combined effects of convection and nanoparticle dynamics. Using the finite element method, the research systematically evaluates the influence of key parameters, including Reynolds number (Re), Grashof number (Gr), and nanoparticle volume fraction (ϕ), on thermal and flow behaviours. Quantitative analysis revealed that as Gr increased, isotherms aligned more horizontally due to enhanced buoyancy-driven convection, with a corresponding increase in the Nusselt number on heated walls by up to 85%. Conversely, an increase in ϕ reduced the Nusselt number by approximately 12%, highlighting the trade-off between nanoparticle-induced viscosity and thermal conductivity. The study also found that kinetic energy increased linearly with both Re and Gr, demonstrating intensified fluid motion and turbulence within the cavity. These findings are significant for optimizing fluid dynamics and thermal efficiency in medical procedures like ureteroscopy, where effective cooling and irrigation systems are critical. The insights into nanoparticle effects and convection mechanisms provide a foundation for designing energy-efficient and thermally optimized systems in biomedical devices and beyond. Future work should focus on experimental validation and exploring hybrid nanoparticle formulations to further enhance system performance.

Secondary-air systems (SASs) are critical for maintaining material integrity and optimizing thermal performance in gas turbines (GTs) and related energy equipment. This work introduces an end-to-end framework that couples high-fidelity numerical simulation (NS) with an attention-augmented 1D-CNN (AM-1D-CNN) surrogate and gradient-based optimization to maximize SAS cooling efficiency under realistic bleed-air limits. First, a steady Reynolds averaged Navier–Stokes (RANS) model was validated against extensive experimental data (including LES spot checks at extreme operating points), achieving close agreement between time-averaged Nusselt number predictions and measured values. Next, 632 RANS cases were generated, spanning a wider-than-experimental range of secondary-air mass flows (0.11-2.17 kg/s), inlet temperatures (318.85-500 K), and rotor speeds (Re φ=4.65×105-1.4×106). Two neural architectures (MLP and 1D-CNN) were trained on normalized inputs; the 1D-CNN outperformed the MLP, and embedding a squeeze-and-excitation attention module (AM-1D-CNN) further boosted test-set R2 by 3.65% and reduced RMSE by 31.48%. Permutation-importance (PI) analysis identified secondary-air mass flow, secondary air temperature, and rotor-surface temperature as the dominant predictors. Response-surface modeling then showed that increasing mass flow strongly enhances Nusselt number, while rotor temperature exerts a modest negative influence. To avoid unrealistically large mass-flow solutions, a penalty term was added to the objective, guiding the optimizer toward low secondary-air mass flows that still maximize cooling. Ultimately, optimal boundary conditions were determined within the feasible parameter range. Detailed Computational Fluid Dynamics (CFD) visualizations confirm that the optimized flow not only cools more efficiently but also promotes stable impingement without excessive separation. This framework delivers a rapid, physics-informed pathway to SAS boundary-condition design and establishes a quantitative foundation for future GT cooling-system innovation. Highlights Proposed AM-1D-CNN model improves heat transfer efficiency prediction. Attention mechanism enhances prediction accuracy compared to MLP and 1D-CNN. Response surface and gradient optimization identify optimal boundary conditions. Synergistic effect between key parameters enhances system performance. Optimization process identifies optimal boundary conditions for efficiency.

The flood discharge head is getting higher in high dam construction, threatening the safety of hydraulic engineering. Accurate prediction of the nappe trajectory is needed to help guarantee safety through downstream strategies. However, it is still challenging to accurately obtain the motion trajectory of the aerated jet flow due to dramatic aeration and deformation. This study proposed a compounding method to improve the simulation of high-speed aerated jet flows, combining the advantages of the traditional grid method (jet flow aeration characteristics simulation) and meshless method (broken droplet hydraulic characteristics simulation). The proposed compounding method was verified using test data of a horizontal jet flow with six different initial velocities (ranging from 10 to 20 m/s). The simulated trajectory distances were all in good agreement with the measured data, and the maximum error was less than 3.0% considering air resistance. The air resistance largely influenced the aerated jet trajectory patterns, resulting in a larger flow velocity in the jet centre and a relatively smaller flow velocity at the upper and lower edges. In addition, the thickness of the jet flow increased significantly with increasing initial jet velocity.

The aerodynamic performance of compressor blades is highly sensitive to variations in streamwise curvature near the leading edge. Inadequate curvature may lead to the formation of separation bubbles or even stall, posing challenges to the blade profile design. Inspired by the spanwise tubercles on humpback whale flippers, this study proposes the implementation of streamwise curvature-based bionic protuberances near the leading edge of the NASA Stator 37 blade profile, which aims to mitigate flow separation while preserving a smooth global profile curvature. The methodology involves first optimizing the global curvature distribution of the blade profile using Bezier curves, followed by localized curvature amplification at the junction of the leading edge and the blade profile to construct bionic protuberances. A systematic numerical investigation is conducted to evaluate the impact of the curvature amplification ratio on aerodynamic performance. Results indicate that appropriate curvature amplification effectively extends the stable operating range and improves performance at moderate and high incidence angles. Specifically, a curvature amplification ratio of 103 increases the stable operating range by 23.4%; at an incidence angle of 6°, a ratio of 82.4 significantly mitigates the adverse pressure gradient at the leading edge, reducing the total pressure loss coefficient by 57.8%; and at 10° (near-stall condition), a ratio of 136.9 effectively suppresses flow separation, reducing the trailing-edge boundary layer thickness by 53.6%.

Understanding the underlying mechanisms behind cavitation-induced noise plays a crucial role in promoting sustainable marine technologies and mitigating underwater acoustic pollution. To address the difficulty of capturing nonlinear flow-acoustic interactions under cavitating conditions, a novel hybrid method, this study proposes a novel hybrid method based on the DDES turbulence model and developed a numerical prediction method for cavitation noise combining three-dimensional implicit vortex sound theory with cavitation. The method is validated by experiment, with head and efficiency prediction errors less than 4.2% and noise level deviations within 4.8%. Results show that: as the cavitation number drops, vapour cavities extend along the blade passage and contribute to intensified vortex structures near the impeller’s entrance. Pressure pulsation frequencies are 0.167fBPF at the impeller and 1fBPF at the volute, with strong dynamic-static interference near the volute tongue. Flow-induced noise exhibits a discrete distribution, with peak sound pressure levels at 1fBPF in the low-frequency range and 6–7fBPF in the mid-to-high frequencies. Bubble pulsation amplifies noise at σ = 0.07, 0.062, and 0.060, while severe flow blockage at σ = 0.051 reduces noise levels. Noise sources are primarily concentrated near the volute tongue and impeller-volute junction, where impeller rotation induces a ‘jet wake’-like pattern, and impeller motion significantly influences noise source distribution near the volute tongue. The synergy angle analysis reveals efficient acoustic radiation near the impeller inlet. These findings provide theoretical support for low-noise design and cavitation noise control in hydraulic machinery.

Accurate prediction of boundary layer transition and turbulence phenomena is fundamental for assessing drag, heat flux and flow noise in underwater vehicles. While transition-turbulence prediction models for air have made encouraging progress over the past few decades, advancements for water-based boundary layers have been comparatively sluggish, with existing models for air often being directly applied to water. However, these models become invalid in the presence of a temperature gradient in the water medium. This is due to the fact that, in contrast to air, the trend of the temperature effect on the flow stability characteristics of water boundary layer is completely reversed. To address this critical gap, this study proposes a local-variable-based transition-turbulence prediction model specifically tailored for underwater boundary layers. This model is developed upon the flow stability analysis and is compatible with modern computational fluid dynamics (CFD) techniques, including unstructured grids and massively parallel computing. Validation of the model is achieved by comparing its predictions with experimental data for zero-pressure-gradient flat plates, an adiabatic axisymmetric body of Power [(1977). Drag, flow transition, and laminar separation on nine bodies of revolution having different forebody shapes (Tech. Rep.). David W. Taylor Naval Ship Research and Development Center], and an axisymmetric body of Lauchle & Gurney [(1984). Laminar boundary-layer transition on a heated underwater body. Journal of Fluid Mechanics, 144, 79–101. https://doi.org/10.1017/S0022112084001518] with a heated wall. The results demonstrate that the proposed model provides accurate predictions of transition locations in underwater flows with or without temperature gradients. This work establishes a reliable and scientifically sound foundation for advancing drag reduction, noise mitigation, and flow control research of underwater vehicles.

The innovation and advancement of pipeline leakage detection technology are crucial for ensuring water supply safety and the effective utilization of resources. Currently, transient wave-based leakage detection methods face challenges such as signal attenuation in complex pipeline systems, difficulties in identifying multiple leakage points, and insufficient real-time performance, which limit their localization accuracy and reliability. Therefore, this paper revisits the fundamental principles which form the basis for transient-based leak detection methods, systematically exploring the influence of leakage on the characteristics of the frequency response function (FRF) and its intrinsic relationship with the standing wave-leak interactions through experimental verification and theoretical analysis. The experiments reveal that leakage significantly alters the peaks and amplitudes of the FRF, with factors such as leakage volume, background pressure, transient pressure, and leakage location affecting its characteristics. Changes in leakage location can cause shifts in the FRF peak patterns through the standing wave-leak interactions, this phenomenon is similar to the Bragg effect, but the mechanism is not entirely the same. This study successfully constructs a frequency-domain mathematical model of transient wave-leakage coupling, innovatively introducing the precise and efficient Trikha-Vardy-Brown (TVB) unsteady friction model combined with viscoelastic theory to accurately simulate FRF characteristics under different pipe materials and operating conditions. Additionally, the study employs the matched-field processing (MFP) method to achieve accurate estimation of the location and size of single and multiple leaks, the single leakage error is within 0.6 m for steel pipes, within 4 m for acrylic pipes, and the multiple leakage errors are all within 4 m. In conclusion, this study confirms the effectiveness and accuracy of transient wave propagation technology in pipeline leakage detection, providing a new technical approach for the rapid diagnosis and localization of water pipeline leaks.

Flapping wing micro aerial vehicles (FWMAVs) with flexible wings offer unique advantages across multiple scenarios due to their high energy efficiency and precision capabilities. This study explores the fluid-structure interaction (FSI) mechanisms and aerodynamic performance differences among three flexible wing motion patterns – passive twisting (PT), chordwise active twisting (CAT), and ‘Figure-8’ active twisting (FAT) – using a bidirectional FSI numerical simulation platform. A bionic wing model (aspect ratio AR = 3.86) and a Multiphysics-coupled framework were developed to evaluate the effects of dynamic wing torsion on lift and energy recovery. Results show that PT enhances aerodynamic performance by delaying flow separation and stabilising leading-edge vortices (LEVs). At 4 m/s, the average lift of flexible wings (FW) with PT is 6.31 times higher than that of rigid wings (RW). Active twisting strategies further improve efficiency: CAT increases average lift by 143% compared to PT at θmax = 40°, while FAT achieves 14.9% energy recovery rate through wake capture and elastic potential energy release, with an elastic energy release rate 2.79 times higher than CAT. Vortex dynamics analysis reveals that active twisting optimises lift by enhancing LEV circulation and proximity to the wing surface. CAT strengthens LEV attachment near the wing root, while FAT stabilises vortices at the wingtip. This research provides insights into energy efficiency optimisation and active control strategies for FWMAVs, highlighting the benefits of flexible deformation and intelligent motion regulation in improving aerodynamic performance and energy management.