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In this study, we investigate the effect of rail vibrations on the lubrication and efficiency of a splash-lubricated gearbox; specifically, the gearbox of a rail-transit vehicle. A high-fidelity 3D numerical model of the gearbox in a moving reference system is described, as well as computational fluid dynamics (CFD) simulations of the gearbox with different vibration directions. The effects of rotational speed, oil-immersion depth, and different oil-injection volume rates on lubrication and efficiency are discussed. We propose a method of evaluating the internal lubrication condition of a splash-lubricated gearbox and quantitatively compare the effects of different operating parameters on lubrication and efficiency. Finally, our experiment to verify the feasibility of the simulation method is described. The results show that with vibration, the churning loss and oil supply for the bearings are significantly higher than those under static conditions. In addition, among different vibrational directions, lateral vibration has the greatest influence on the lubrication condition and efficiency of the gearbox. For the studied railway-vehicle gearbox, the best lubrication condition is achieved at a rotational speed of 1600 r/min and an oil-immersion depth of two times the tooth height (2.0h). Rotational speed is the operating parameter that has the most significant effect on the lubrication and efficiency of the gearbox.

As an inverse problem, particle reconstruction in tomographic particle image velocimetry attempts to solve a large-scale underdetermined linear system using an optimization technique. The most popular approach, the multiplicative algebraic reconstruction technique (MART), uses entropy as an objective function in the optimization. All available MART-based methods are focused on improving the efficiency and accuracy of particle reconstruction. However, those methods do not perform very well on dealing with ghost particles in highly seeded measurements. In this report, a new technique called dual-basis pursuit (DBP), which is based on the basis pursuit technique, is proposed for tomographic particle reconstruction. A template basis is introduced as a prioriknowledge of a particle intensity distribution combined with a correcting basis to enable a full span of the solution space of the underdetermined linear system. A numerical assessment test with 2D synthetic images indicated that the DBP technique is superior to MART method, can completely recover a particle field when the number of particles per pixel (ppp) is less than 0.15, and can maintain a quality factor Qof above 0.8 for pppup to 0.30. Unfortunately, the DBP method is difficult to utilize in 3D applications due to the cost of its excessive memory usage. Therefore, a dual-basis MART was designed that performed better than the traditional MART and can potentially be utilized for 3D applications.

This study presents a multi-objective optimization model to solve aircraft landing problem within a predetermined time window. Minimizing total fuel cost, minimizing total delays, maximizing throughput, and maximizing fairness are considered to construct the multi-objective optimization model. According to the different weights of objectives, the first-come-first-serve and simulated annealing are used to solve the multi-objective optimization problem. The algorithms’ feasibility is analysed and a suggestion about how to get satisfactory solutions for all stakeholders is given through the expert evaluation method. The results show that the simulated annealing can get better solution than the first-come-first-serve when the number of flights exceeds 10.