Road feeling simulation is one of the key technologies for the development of the steer-by-wire system. In order to improve the quality of the road feeling simulation, the road feeling torque of the steer-by-wire system was designed based on the principle of steering resisting torque generation of the traditional steering system and the method of calculating the dynamics model. In order to reduce the torque pulsation of the road feeling motor caused by uncertain system parameters and sensor noise, a vector control strategy for the road feeling motor based on the active disturbance rejection control algorithm was designed, and the control parameters in the active disturbance rejection control algorithm were optimized using the particle swarm optimization algorithm. Based on the steering wheel middle position maneuvering stability test and the steering-effort test, the road feeling simulation effect was verified. The results show that the active disturbance rejection control based on particle swarm optimization algorithm has strong adaptability, and can effectively realize the accurate simulation of road feeling. It can effectively reduce the burden on the driver. The relevant research can provide a reference for the design of the steer-by-wire.

When a train set operates at high speeds, significant vibration and noise are generated in the outer door due to external airflow and road surface excitation, which directly impacts ride stability and train operation safety. To address the significant vibration of the thin-walled backplate structure of the outer door at the first five modal frequencies, particle damping was applied to mitigate vibration and noise. Firstly, a discrete element model of the particle damper for the outer door was established to analyze system energy consumption through simulations under various filler parameters. Based on these simulation results, an optimized design of the filler parameters was conducted, and experiments were performed to assess sound insulation with each filler material. The results show that the installation of particle dampers achieves a sound insulation improvement of 5.33 dB, significantly optimizing the radiated noise from the outer doors. These findings demonstrate the effectiveness of particle damping for vibration and noise reduction in the outer door of a moving train set.

In order to address issues such as high noise, low brightness, and blurred details in low-light conditions, a new algorithm named UMDCEAD-NET, integrating zero-reference depth curves for low-light image enhancement and denoising, was developed. The algorithm's design was initially centered around a feature extraction network, employing a U-Net architecture as the backbone network. To enhance the feature extraction capabilities and preserve more detailed image information, Mobile-Net was integrated into the downsampling phase of the U-Net backbone. Subsequently, to address the issue of inadequate lighting and pixel-level image degradation, the extracted features underwent iteration using depth curve estimation (LE-curve), in conjunction with depth separable convolution, which served to reduce the network model's parameter count. Furthermore, five non-reference loss functions were engineered to bolster the algorithm's generalization capabilities and its retention of detail under varying lighting conditions. The enhanced image was then subjected to noise reduction in tandem with AD-NET(attentional denoising network), thereby diminishing the noise and aligning the image more closely with human visual perception. Experimental outcomes demonstrated that the proposed algorithm achieved an average PSNR (peak signal-to-noise ratio) of 22.29 on the public dataset Zero-DCE, which exceeded the performance of the Zero-DCE++ algorithm by 32%. Additionally, on the public dataset LOL, the algorithm attained an average PSNR of 21.15, outperforming the SGZ algorithm by 3%. These results indicate that the algorithm effectively mitigates noise in enhanced images, enriching the detail information in both dark and bright regions, and significantly improving image quality compared to other mainstream algorithms.

Manual grinding with hand-held tools generates a lot of dust, currently, it is common to use negative pressure trapping device to limit the dust in the confined space, and make the dusty airflow directed through the dust removal device to be purified to solve the problem of dust dissemination. In order to solve the problem of ineffective ventilation and protection facilities in an aluminum grinding workshop, which led to the uncontrolled spread of dust between operations, a structural design of a “U-shaped slit” dust collection device for the grinding process of small aluminum parts was proposed.Finite element simulation and analysis method was used to investigate the dust prevention and control effect of the device. The results show that: when the device on both sides of the slit width is 2 cm, the rear side of the slit width is 5 cm, the air volume is 3 600 m³/h, and the internal airflow channel structure is an asymmetric slanting closure structure, the control surface of the wind speed is more uniform, average wind speed of personnel breathing zone reaches 1.3 m/s, in the same time, the noise of the internal airflow channel is relatively small and the dust particle trapping effect is better. It is concluded that the size of the device's “U-shaped slit” and the internal airflow channel structure design can effectively capture the dust generated during the sanding process, reduce the concentration of dust in the workplace, and provide a reference for the protection of dust in the sanding place.

In response to the low detection accuracy and high model complexity of existing road damage detection algorithms in complex environments, a lightweight road damage detection algorithm named LDC-YOLOv5 (lightweight deformable convolution YOLOv5) was proposed based on YOLOv5.To address the complexity of real road surface damages, a lightweight feature extraction module was designed using Deformable Conv (deformable convolution) and Depthwise Conv (depthwise convolution) to replace the C3 module in the original network backbone, enabling convolutional kernels to focus on irregular crack damages and enhancing feature extraction for damage detection. To reduce algorithm complexity in the feature fusion stage, a lightweight feature fusion module was constructed using GhostConv to replace the C3 module in the original network neck, lowering network parameters and complexity. Additionally, to prevent missed detections caused by uneven lighting and shadow obstruction, a lightweight attention mechanism, TripletAttention, was introduced in the backbone network to improve the algorithm's understanding of damage information and context. Experiments conducted on the IEEE open dataset RDD2022 and the Kaggle open dataset Road Damage demonstrate that, compared to YOLOv5s, the proposed LDC-YOLOv5 achieves a 1.4% and 4.2% improvement in mAP50 on the two datasets, respectively, with only 67.6% of the model parameters of YOLOv5s.

Aerodynamic principles and the assumption of axial inextensibility of a two-dimensional flexible plate were used to derive a nonlinear theoretical model of flag flutter, investigate and analyze the coupled motion characteristics of flexible flag and airflow in nature and wind energy collection fields, and examine the effects of length, mass ratio, and wind speed on its motion characteristics. The flag oscillation process in the wind was numerically simulated using the bidirectional fluid-structure coupling method and the overlapping mesh methodology, from which the features of the surrounding flow field and the motion behavior of the flag inside it were determined. The findings indicate that while swing displacement increases and subsequently declines with wind speed, the crucial flutter wind speed lowers as flag length increases. The chirp frequency decreases as the mass ratio increases, and the Strahl number is less affected. With the predefined dimensions of the flag, at low wind speeds, both the displacement and frequency of the swing are low. However, when the wind speed exceeds the critical vibration threshold, a significant vibration phenomenon occurs. Changes in surrounding pressure and velocity are caused by the flag-encircling vortex as it progresses through phases of formation, shedding, and disappearing. Numerical simulation techniques based on the overlapping mesh methodology successfully address the deformation problem of flexible flags. Theoretical and numerical simulations can be verified and analyzed with this method.

Machine learning models, widely applied in landslide susceptibility assessment due to their powerful feature extraction capabilities, are continuously evolving in their algorithms to address the common issue of low accuracy. The GCNN (group convolutional neural network) model was introduced into landslide susceptibility assessment, and its results were compared with those of various common machine learning models to comprehensively evaluate the adaptability of these models in this field. Taking Hebei Province as the research area, 16 influencing factors were selected from three aspects: triggering factors, pregnant disaster environment, and susceptible bodies. GCNN model and other common machine learning models—CNN (convolutional neural network), Logistic (logistic regression), RF (random forest), and SVM (support vector machine)—were constructed to build corresponding susceptibility assessment models. The research area is divided into four categories of landslide susceptibility zones, and the accuracy of the zoning is comprehensively evaluated. The study indicates that compared with the other four machine learning models, the GCNN model achieves higher scores in various confusion matrix indicators and is more suitable for landslide susceptibility zoning. The resulting zoning of landslide susceptibility is consistent with the actual occurrence of landslide points, indicating a more accurate delineation of landslide-prone areas.

To solve the problem of large steady-state error and poor parameter robustness of MPFC system predicted by traditional model of PMSM (permanent magnet synchronous motor), a multi-voltage vector selection method based on stator flux prediction error vector analysis was proposed. Firstly, the multi-voltage vector selection criteria for determining the region where the flux error vector is located were established by dividing the sectors according to the axis in the two-phase stationary coordinate system. Then, the predicted value of stator flux and the value function of stator flux in two-phase stationary coordinate system were used to calculate the action time of each voltage vector. In addition, a discrete sliding mode stator flux observer considering the mismatch of resistance and inductance parameters was designed, which further improves the parameter robustness of the system. Finally, the effectiveness and feasibility of the proposed predictive stator flux control method are verified by simulation and experiments. The proposed method still has good steady-state performance under the condition of system parameter mismatch, and significantly reduces the stator flux and electromagnetic torque ripple.

It will raise their feedwater temperature and then improve their operating economy to add No.0 HPH (high-pressure heater) in thermal power units. A steam-water distribution model for a thermal system coupled with No.0 HPH was established based on the mass and energy conservation laws. And a variable-condition calculation method for its heat economics was also proposed. Furthermore, a derivative characterization approach was developed to represent the heat economics impact of No.0 HPH extracted steam flowrate, which realize the quantitative evaluation before and after coupling with No.0 HPH. Taking a 600 MW unit for example to carry out calculations and analysis, it indicates that the index calculation error of the method proposed in this paper is less than 0.15%, which has high precision and can be used for thermal economic evaluation of large thermal power units coupled with No. 0 HPH. Furthermore, after coupling No.0 HPH, the feedwater temperature can be lifted by 19.1~32.3 ℃, the cycle heat efficiency raised by 0.18%~0.2%, and the heat consumption rate decreased by 29.3~34.6 kJ/(kW·h). Moreover, the sacrificial internal work will be even smaller, and the heat consumption decrease be even larger if the turbine load was smaller.

Aiming at the problem of coaxial wheel partial wear of HX high-power electric locomotive in China, the locomotive dynamics model was established based on the dynamics software SIMPACK, and the damage function prediction method based on wear number was used to analyze the influence of different coaxial sequence, wheel diameter difference, curve radius and other conditions on wheel tread damage. The results show that when there is wheel diameter difference in one axle or multiple axles, the influence on tread damage of 2-axle and 3-axle wheels is greater, and the influence on 1-axle wheels is less. For different values of wheel diameter difference, with the increase of wheel diameter difference, the impact of rolling contact fatigue degree on the right wheel tread of 1-axle is small, and the wear degree of the left wheel tread increases. The damage degree of wheel on both sides of 2-axle is reduced; the cracks of 3-axle left wheel are accentuated. Under left curve condition and R400 condition, wheel diameter difference has greater influence on the wear and rolling contact fatigue of each axle. Under right curve condition and R800 condition, wheel diameter difference has more influence on the wear and rolling contact fatigue of each axle. Compared with right curve condition, wheel diameter difference has more influence on wheel tread damage under left curve condition. Compared with curve radius, wheel diameter difference has less influence on wheel tread damage.