Search

The wettability mechanism of soil–water interfaces is of significant importance in geotechnical engineering. However, the effect of different contact angles on unsaturated sand soil behavior has been less understood. In this study, the wetting behavior of nano water droplets on various silica substrates is investigated using molecular dynamics. Seventeen groups of simulation systems with different interaction potential energies ($\varepsilon _{\mathrm{Si}} = 0.008$, 0.04, 0.2, 0.4, 0.6, 0.8, 1, 2 kcal/mol) and temperatures ($T = 273$, 298, 323, 353 K) are conducted. The results show that the contact angles varies intensively with interaction potential energies from 108.5° to 18.1°, which indicates a transition from hydrophobic to hydrophilic and wettability enhancement along with the increase of interaction potential energy. Simulation results also show that contact angles increase with the increase of temperature, whatever the hydrophobic or hydrophilic of the silica surface. Such phenomena are interpreted from the perspective of microstructure, along with the performance of macrostructure. In addition, results show that the contact angles are independent of the thickness and width (length) of silica substrate.

The virtual coupling technology of urban rail trains offers an effective solution to unbalanced temporal and spatial distribution of passenger flow. Based on the key requirements for urban rail trains, namely punctuality, comfort and sufficient time for platform operations, this study proposed a cooperative speed planning algorithm for virtual coupling, featuring information centralized decision-making, following an investigation into train traction and braking characteristics, vehicle kinematics and dynamics models. This algorithm was further devised to incorporate constraint conditions, objective functions, and optimal solutions, and then used to calculate the planning sequence of displacements, speeds, accelerations, traction or braking forces, jerks and operating conditions during the operation of two coupled trains over time. In subsequent field experiments, the arrival time differences of the coupled trains were less than 3 s, stopping time differences between the preceding and following trains were less than 2 s, jerks were maintained at less than 0.1 m/s3, and smooth switching was achieved between traction and braking conditions, demonstrating the efficacy of the proposed algorithm in facilitating safe, punctual, comfortable and efficient operation of coupled trains. The study outcomes lay a solid foundation for future engineering applications.

Advanced driving assistance system (ADAS) in autonomous driving vehicles usually obtains local relative positioning information only by identifying lane lines, and how to avoid collisions when there are jumps in lane lines and encountering obstacles is a key technology that ADAS needs to solve urgently. Therefore, this paper proposes a local dynamic trajectory planning method based on relative positioning. First, the grid sampling is performed in the vehicle curve coordinate system, and the candidate path set in the vehicle coordinate system is obtained after coordinate transformation. Then, the path set is filtered through the collision check and evaluation function to obtain the optimal path without collision. Secondly, the maximum planning speed is adaptively adjusted according to the curvature and lateral deviation of the optimal path, and the speed curve is obtained by the improved trapezoidal speed planning method. Finally, the speed curve is smoothed by the gradient descent algorithm to avoid sudden acceleration. The simulation and real vehicle test results show that the algorithm adopted in this paper can stably correct deviation, avoid obstacles and follow the car in the local relative positioning scene with curves. In the scenario without obstacles, the maximum lateral deviation of the algorithm in the simulation and real vehicle experiment is reduced by 36.7% and 28.6% respectively, the average lateral acceleration is reduced by 29.6% and 46.4% respectively, and the travelling comfort and vehicle safety are high.

This study investigated the time-optimal low-thrust interplanetary transfer problem, and proposes a fast estimation method for guessing the initial costate and optimal transfer time based on a surrogate model, and applied it to the problem of the 11th Global Trajectory Optimization Competition (GTOC 11). Two core methods are proposed in this paper: (1) a fast generation method called the neighbor point iteration algorithm (NPIA) is presented for rapidly generating low-thrust databases with high efficiency and accuracy; and (2) deep neural networks (DNNs) are adopted to learn the state–costate pairs of low-thrust databases, and the surrogate network can quickly estimate the initial costate and optimal transfer time of the low-thrust interplanetary problem. Experiments verified the proposed method and investigated the influence of network structure, learning rate, and loss function on the accuracy of network estimation. The effects of database generation and network estimation were compared based on three transfer scenarios: coplanar, non-coplanar, and arbitrary orbital transfer. In addition, the application case study showed that the proposed method can quickly obtain the time-optimal low-thrust solution to GTOC 11's interplanetary transfer, which achieves high precision and meets the terminal constraints. [ABSTRACT FROM AUTHOR]