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To reveal the response characteristics and rectification mechanism of the nozzle geometry of the airflow acoustic piezoelectric generator (AAPG) to unstable and asymmetric airflow, this paper establishes a control model for modulating the airflow acoustic sounding by studying the structural design scheme for the combination of the fused airflow acoustic piezoelectric generator (AAPG) nozzle-resonant cavity-piezoelectric. Then, the sound pressure curve with sinusoidal vibration is numerically simulated at the bottom of the resonance cavity. Finally, the error between the sound pressure frequency and the theoretical control frequency is less than 7.5 % through experiments, and the portion of the sound pressure frequency and the output voltage frequency are consistent with an error of less than 4 %, verifying that the jet acoustic control model can effectively achieve acoustic control of the asymmetric jet.

Laser cladding technology was employed to prepare dual-phase CoCuNiTiX0.6 (X = Mn, Al) HEA coatings on the 45-steel substrate. The phase structure, microstructural evolution, composition analysis, and corrosion resistance of CoCuNiTiX0.6 (X = Mn, Al) HEA coatings were studied using an X-ray diffractometer, OM, SEM, EDS, and ECW. The results demonstrate that CoCuNiTiMn0.6 alloy consists of an FCC primary phase and a BCC phase. After replacing Mn with Al, CoCuNiTiAl0.6 alloy transforms into a structure dominated by the BCC phase, with a minor amount of the FCC phase. The lattice constants and cell volumes of BCC and FCC in Al-containing alloy are relatively large, but their densities are low. Both alloys have dendritic structures. The high viscosity and poor fluidity of Mn-containing alloy melt increase atomic diffusion resistance and a relatively slow occurrence of “composition undercooling” during solidification, resulting in coarse columnar crystals and pore defects. Ti and Cu exhibit the highest concentrations in the primary phase and interdendrite regions, respectively. The composition differences of Ni, Mn, and Al in different regions are relatively small. Both alloys show obvious passivation zones in 3.5wt% NaCl solution, and their Nyquist plots present a flattened semi-circular arc. The capacitive arc radius, maximum phase angle, and m value of CoCuNiTiAl0.6 HEAC are relatively large, indicating that its corrosion resistance is relatively superior, mainly due to its dense passivation film, fewer defects, and relatively uniform composition distribution.

Bearings are critical components of rotating machinery, yet reliable fault diagnosis remains challenging under complex conditions due to signal non-stationarity and data scarcity. To address these issues, this paper proposes a diagnostic framework that combines generative-adversarial data augmentation with dual-branch time–frequency representation learning to improve feature quality and fault classification. Firstly, the bearing vibration signals are transformed into two-dimensional time-frequency representations by utilizing the continuous wavelet transform. Subsequently, a multi-attention conditional deep convolutional generative adversarial network (MCDCGAN) is employed for conditional augmentation under class imbalance, integrating attention mechanisms and stabilization strategies to generate more reliable samples for minority fault classes. Finally, a dual-branch parallel time-frequency attention network (DPTAN) is designed to jointly learn temporal and spectral feature representations and then fuse them for fault classification. Experimental results on the CWRU and HIT datasets demonstrate that the proposed method achieves better performance than baseline models and maintains robustness under data imbalance and noisy conditions.

The Holmquist-Johnson-Cook constitutive model is widely adopted to analyze the dynamic response of concrete-like materials under impact and blast loading. In this study, a modified set of HJC constitutive parameters for C30 concrete was calibrated through a series of tests. Uniaxial compression tests were used to determine the uniaxial compressive strength and elastic parameters; conventional triaxial compression tests were performed to obtain the yield-surface parameters A, B, and N; uniaxial cyclic loading-unloading tests were conducted to determine the damage parameters D1 and D2; and split Hopkinson pressure bar (SHPB) tests were carried out to identify the strain-rate parameter C. Numerical validation was conducted using a finite element model of the SHPB test. The results demonstrate that the proposed HJC model parameters can effectively represent the dynamic mechanical behavior of C30 concrete under impact loading at low-to-medium strain rates. Both the stress-strain curves and failure modes derived from numerical simulations agree well with the experimental observations. These findings can provide reference for the impact- and blast-resistant design of concrete structures.

Increasing heat transfer in double-pipe heat exchangers (DPHEs) is an interesting topic due to challenges that still remain to be solved, especially when working under low-to-moderate flow rate regimes where poor mixing and thermal boundary layers reduce the effectiveness of convection. Despite numerous studies conducted on the basis of metal-foam and insert-type enhancement methods, existing research mostly covers completely-filled or simplified partially-filled cases. The present work numerically investigates thermo-hydraulic performance of a countercurrent DPHE using various structured metal-foam inserts installed in the annulus region. In particular, a three-dimensional CFD model of the studied geometry was successfully validated with previously reported experimental data with deviations not exceeding ±5 % for the average Nusselt number and ±7 % for the friction factor. In simulations, hot water flows inside the inner pipe at temperature 75 °C and 3 L/min, whereas cold water enters the annulus at temperature 30 °C with flow rates of 1 to 9 L/min, equivalent to Reynolds numbers of 205-1845. In total, nine geometries were considered including a smooth basecase, fully filled foam geometry, circular ring foam baffles, continuous three-strips foam geometry, as well as five interrupted three-strips foams with 5, 7, 9, 11, and 13 interruptions, respectively. For all cases, copper foam with porosity 0.9 and pores density 40 PPI was used, while water thermophysical properties were assumed constant. It was found that inserting structured metal-foam increases heat transfer due to mixing effect and repeated disruption/regeneration of thermal boundary layer. As compared with the smooth base case, the fully filled metal-foam geometry showed the largest improvement in heat transfer performance by providing up to 15 times higher values of the average Nusselt number. Nevertheless, the interrupted strips foam designs demonstrated the best thermo-hydraulic characteristics in terms of trade-off between enhanced heat transfer and increased pressure drop penalty. In particular, the interrupted foams led to approximately 1.5-3 times higher friction factor than the smooth geometry, while performance evaluation factor PEF equaled approximately 2.8. Therefore, it can be concluded that interrupting structured metal-foam inserts could be considered promising passive enhancement approach for low-to-moderate Reynolds number DPHEs.

To investigate the effects of confining stress on the fracture behavior of rock masses under deep high-stress environments, this study employed stereolithography 3D printing technology to fabricate rock-like specimens featuring Cracked Chevron Notched Brazilian Disc (CCNBD) geometry. Static fracture tests under varying confining stress were conducted using a custom-designed confining stress loading system. The key findings are as follows: 1) Fracture toughness exhibited a linear increase with confining stress. When the confining stress reached 1.168 MPa, the fracture toughness increased to 2.86 times that of the unconfined specimen. 2) Crack propagation paths were influenced by confining stress. Under zero confinement, cracks propagated straight through the specimen. Increasing confining stress caused significant deflection in the crack propagation path. Concurrently, the fractal dimension of the fracture surface showed a positive correlation with increasing confining stress. 3) Confinement constrained lateral deformation and altered fracture characteristics. The presence of confining stress constrained lateral deformation under load. As confining stress increased, the resulting fracture surfaces became progressively rougher, and the corresponding energy release rate of the specimens demonstrated an increasing trend. These research outcomes hold significant theoretical and practical importance for enriching the understanding of fracture mechanisms in deep rock masses subjected to external loads.

Reliable operation of auxiliary equipment is critical for hydropower stations. However, data-driven diagnosis faces the “cold start” challenge due to fault sample scarcity in high-maintenance environments. We propose a few-shot diagnostic model, Siamese Cross-Attention Network (Scan-Net), combined with a transfer learning strategy. To extract discriminative features from limited data, we utilize Multi-Scale Synchrosqueezed Wavelet Transform (MSWT) for physically consistent time-frequency representations. Unlike simple concatenation, we design a dual-stream Siamese network with a bidirectional Cross-Attention mechanism that enables explicit inter-sensor feature interaction. We introduce a Deep Mutual Learning (DML) strategy with symmetric KL divergence constraints to align prediction distributions between dual branches, serving as self-supervised regularization to prevent overfitting. We establish a transfer pathway from public datasets to field equipment. Experiments show that Scan-Net achieves 96.50 % accuracy on the CWRU dataset under the 10-shot setting, and 94.43 % average accuracy in cross-load transfer. Pilot deployment at a large-scale hydropower station provides preliminary validation, with the system contributing to a reduction in routine inspection workload

This paper presents the development of a wearable system for human motion monitoring based on an inertial measurement unit (IMU). The proposed device enables real-time acquisition of angular velocity, linear acceleration, and orientation parameters of a body segment. An experimental prototype was implemented using an IMU sensor, Arduino Nano, and a data recording module. Laboratory tests focused on dorsiflexion and plantarflexion movements of the ankle joint. The results demonstrate that the system can accurately capture motion parameters and reflect changes in the Pitch angle corresponding to these movements. The proposed approach can be applied in motion analysis and rehabilitation monitoring. Future work includes integration of EMG and force sensors to extend the system functionality.