In this paper, a four-stroke engine diesel was employed to investigate the effects of different fuel mixture ratios of diesel and ethanol on engine performance and emission characteristics in terms of cylinder temperature, heat release rate, brake power, brake thermal efficiency, brake specific fuel consumption, and cylinder pressure. The corresponding simulation model of diesel engine was developed by AVL-Fire coupled CHEMKIN code, and an improved chemical kinetics mechanism containing 34 reactions and 19 species was employed to simulate the fuel spray process and combustion process. The simulation model was validated by experimental results under 100% and 50% load conditions and used to simulate the combustion process of diesel engine fueled with pure diesel and diesel–ethanol blends with 10%, 20%, and 30% ethanol by volume, respectively. The results showed that the increase of ethanol content in the blended fuel had a certain negative impact on the performance characteristic of diesel engine and significantly improved the emission characteristic of the engine. With the ethanol proportion in the blended fuel increased to 10%, 20%, and 30%, the brake thermal efficiency of the engine increased by 2.24%, 4.33%, and 6.37% respectively. However, the brake-specific fuel consumption increased by 1.56%, 3.49%, and 5.74% and the power decreased by 1.58%, 3.46%, and 5.54% respectively. In addition, with the ethanol proportion in the blended fuel increased to 10%, 20%, and 30%, the carbon monoxide emission decreased by 34.69%, 47.60%, and 56.58%, and the soot emission decreased by 7.83%, 15.24%, and 22.52% respectively. Finally, based on the combining fuzzy and grey correlation theory, nitrogen oxide emission has the highest correlation with engine power and brake-specific fuel consumption. The values reach 0.9103 and 0.8945 respectively. It shows that nitrogen oxide emission and cylinder pressure have a significant relationship on engine power and brake-specific fuel consumption.

The current process of vibratory stress relief is limited by centrifugal force produced by eccentric motor since the excitation frequency is mostly less than 200 Hz. The workpiece with high structural stiffness cannot generate enough dynamic stress by the traditional method. In the present work, the vibratory residual stress using pure lateral resonance and the theory of coupled lateral-torsional resonance are compared by the finite element method. Due to the interaction between lateral vibration and torsional vibration, the torsional vibration generated by external lateral excitation can induce coupled lateral-torsional resonance. The large enough dynamic stress is obtained to reduce the residual stress. The principle to reduce residual stress is analyzed by the Unified strength theory. Numeric solutions are obtained to describe the parametric condition to design the device of vibratory stress relief using coupled lateral-torsional resonance. The operating principle of the related device is introduced.

The time-varying mesh stiffness excitation of helical gears impacts the vibration state of the rotor–bearing systems, while the existence of mechanical dynamic eccentricity makes the rotor–bearing dynamics equation a system of parametric excitation. To address this situation, the time-varying mesh stiffness of the helical gear is substituted into the coupled bending–torsion–axial dynamic equation of the rotor–bearing system. By considering dynamic eccentricity, the rotor’s vibration displacement response is calculated. The unified strength theory is introduced to compute the complex stress state. The study’s results indicate that time-varying stiffness significantly influences the system’s vibration characteristics, with the equivalent stress values exceeding those under twin-shear stress. This finding demonstrates the advantage of using the unified strength theory under high-load conditions, providing an essential reference for optimizing the dynamic performance of high-speed helical gear transmission systems.

This paper presents an intelligent bearing fault diagnosis framework that combines Gabor wavelet filtering, FastICA and a VGGSENet-BiGRU-SimpleAttention network. Guided by characteristic bearing fault frequencies, a multi-scale Gabor filter bank and Hilbert envelope analysis enhance weak transient impacts in vibration signals, while FastICA adaptively separates fault-related components from noise. The fused time–frequency and independent feature set is then fed into a hybrid deep model, where VGGSENet extracts local spatial features, BiGRU models temporal dependencies and SimpleAttention emphasizes critical fault patterns. Experiments on the public CWRU dataset under complex working conditions show 100% classification accuracy and improved efficiency compared with several state-of-the-art CNN, RNN and Transformer-based methods, demonstrating the effectiveness and robustness of the proposed approach for industrial bearing fault diagnosis.

According to the engineering application of vibration stress relief (VSR) based on lateral and torsional coupling, the controlled single-phase series motor is used to create alternating electromagnetic torque, which excites the torsional vibration of the shaft. After adding the expression of the electromagnetic torque to the multi-dof dynamic equation of the lateral and torsional coupling, the dynamic response of lateral and torsional coupling can be calculated. The result has value in the application of VSR based on lateral and torsional coupling.