Tube hydroforming (THF) is an advanced technology with the advantages of lightweight and integrity, which can be used to manufacture hollow structural components. The process of THF is influenced by many factors, among which the matching relation between the internal pressure and axial feed, i.e., loading paths, is particularly important. In this article, a hybrid method is proposed to optimize loading paths of THF. Firstly, a three-layer back-propagation artificial neural network (BP-ANN) is built, and 200 samples from finite element (FE) simulations are applied to train and test the artificial neural network (ANN). Then genetic algorithm (GA) is adopted to search the optimal loading paths in the specified bounds of the design variables by using the trained ANN as the solver of the objective function and constraint functions. After 59 iterations, the optimal loading paths are obtained. Finally, the verified experiments are performed on the special hydroforming press. The results show that the proposed method can effectively search the optimal loading paths of THF and remarkably improve the quality of the final formed parts.

The wind–sand climate prevalent in the central and western regions of Inner Mongolia results in significant damage to wind turbine blade coatings due to sand erosion. This not only leads to a decline in power generation but also poses safety risks. This study replicated the wind–sand environment of Alashan and numerically simulated the erosion and wear process of the blade coatings of a 1.5 MW horizontal axis wind turbine under rotational conditions using the DPM model. Additionally, erosion tests were conducted on the operating wind rotor in a wind tunnel. The simulation results demonstrate that sand particle trajectories in the rotating domain are influenced by vortex, incoming wind speed, and sand particle size. For small-sized sand particles, variations in wind speed do not substantially alter the number of particles in contact with the wind turbine blades. However, alterations in the momentum of these particles lead to changes in the impact force on the coating surface. Conversely, the change of wind speed will not only alter the number of large-size sand particles in contact with the wind rotor but also modify the impact force on the coating surface. Furthermore, after impacting the blade, small sand particles continue to move along an approximate helical trajectory with the airflow, while large-size sand particles swiftly rebound. Through statistical analysis of erosion pits on the blade surface after the erosion experiments, it was observed that, in comparison among the leading edge, windward side, trailing edge, and leeward side, the leading edge presents the greatest number of erosion pits, whereas the leeward side has the fewest. Along the spanwise direction, the 0.7R-blade tip segment exhibits the highest count, while the blade root-0.3R section displays the fewest number of pits. The wear morphology of the blade coating was observed from the blade root to tip. The leading edge coating exhibits a range from shallow pits to coating flaking and deeper gouge pits. On the windward side, the coating displays wear patterns varying from tiny cutting pits to cutting marks, and then to gouge pits and coating flaking. Erosion morphology of the trailing edge evolves from only minor scratches to spalling pits, further deepening and enlarging. These research findings provide a basis for the study of zoning-adapted coating materials for wind turbine blades in wind–sand environments.

Due to the large number of waterborne bacteria presenting in drinking water, their rapid and accurate identification has become a global priority. The surface plasmon resonance (SPR) biosensor with prism (BK7)-silver(Ag)-MXene(Ti₃T₂Cx)-graphene- affinity-sensing medium is examined in this paper, in which the sensing medium includes pure water, vibrio cholera (V. cholera), and escherichia coli (E. coli). For the Ag-affinity-sensing medium, the maximum sensitivity is obtained by E. coli, followed by V. cholera, and the minimum is pure water. Based on the fixed-parameter scanning (FPS) method, the highest sensitivity is 246.2 °/RIU by the MXene and graphene with monolayer, and with E. coli sensing medium. Therefore, the algorithm of improved differential evolution (IDE) is obtained. By the IDE algorithm, after three iterations, the maximum fitness value (sensitivity) of the SPR biosensor achieves 246.6 °/RIU by using the structure of Ag (61 nm)-MXene (monolayer)-graphene (monolayer)-affinity (4 nm)-E. coli. Compared with the FPS and differential evolution (DE) algorithm, the highest sensitivity is more accurate and efficient, and with fewer iterations. The performance optimization of multilayer SPR biosensors provides an efficient platform.

In order to reduce the power consumption of the round baler and improve the quality of the bale, the compression and relaxation characteristics of alfalfa were investigated with respect to moisture content, feeding speed, and steel roll speed. Experimental trials were performed by using a steel-roll fixed chamber round baler rotary compression test platform. Three moisture contents were prepared, 18%, 21%, and 24%. The feeding speeds were set at 1.11 m·s-1, 1.39 m·s-1, and 1.67 m·s-1, and the steel roll speeds were 106 r·min-1, 126 r·min-1, and 146r·min-1, respectively. The experimental data for these trails were collected and three compression models and a generalized Maxwell model were fitted to the pressure, density, or time data. The maximum compression pressure decreased with the increase of moisture content and increased with the increase of feeding speed, while the steel roll speed had no significant effect on the maximum compression pressure. The stress relaxation time decreased with the increase of moisture content, feeding speed and steel roll speed. The equilibrium stress decreased with the increase of the steel roll speed and moisture content and increased with the increase of the feeding speed. All compression models had a good fit for the experimental data.