Ride comfort and handling performances are known conflicts for off-road vehicles. Recent publications focus on passenger vehicles on class B and class C roads, while, for off-road vehicles, they should be able to run on rougher roads: class D, class E, or class F roads. In this paper, a quarter vehicle model with nonlinear damping is established to analyze the suspension performance of a medium off-road vehicle on the class F road. The ride comfort, road holding, and handling performance of the vehicle are indicated by the weighted root mean square (RMS) value of the vertical acceleration of the sprung mass, suspension travel, and tire deflection. To optimize these objectives, the genetic algorithm (GA), particle swarm optimization (PSO), and a genetic algorithm based on the particle swarm optimization (GA-PSO) are initiated. The efficiency and accuracy of these algorithms are compared to find the best suspension parameters. The effect of the optimized method is validated by the field test result. The ride comfort, road holding, and handling performance are improved by approximately 20%.

The influence of variable operational conditions affects the performance of particle collection and separation of a regenerative air vacuum sweeper. Therefore, the purpose of this paper was to numerically investigate the factors affecting the particle suction efficiency of the pick-up head. Using computational fluid dynamics (CFD), a model of an integrated pick-up head was developed based on the particle suction process to evaluate the particle removal performance. The realizable k-ε and discrete particle models were utilized to study the gas flow field and solid particle trajectories. The particle structure, sweeping speed, secondary airflow, pressure drop, and distance between the particle suction port and the road surface, as factors that affect the particle removal efficiency, were investigated. The results indicate that the particle suction efficiency increases with decreasing sweeper speed. Furthermore, the particle overall removal efficiency increased with a reduction in the distance between the suction port and the road surface as well as the control of the secondary airflow in the system. By increasing the airflow rate at the suction port, high efficiencies were achieved at a high sweeper speed and high particle densities. At a sweeper speed of 6–10 km/h, the results showed that the secondary airflow recirculation varied between 60 to 80 %, while the high-pressure drop ranged from 2200 to 2400 Pa, and the particle suction efficiency recorded was 95%. The numerical analysis results provide a better understanding of the particle suction process and hence could lead to an improvement in the design of the pick-up head.

Cyclone separators are used extensively in diverse applications and research domains to collect particle-laden flows. Despite the technological advances in this field, no bibliometric reports on this topic have been documented. Understanding the state of the art in this field is crucial for future research. Using bibliometric mapping techniques, this study examined the quality, quantity, and development of research on cyclone separators. Relevant data were extracted in plain text formats through search queries refined by publication year (2000–2021) and document type (article and review articles). A sample of 487 publications, limited to the Web of Science Core Collection (WoSCC) database was used for the bibliometric analysis. Data analysis was performed using RStudio software package (R Bibliometrix tool). Of the 487 publications that appeared during this period, China had the highest number, followed by the Islamic Republic of Iran, whereas chemical engineering journals dominated the cyclone separator research publications. Collaboration among the researchers was low (MCPR < 0.5000). Furthermore, the pattern of single-author publications was found to outstrip that of the multiple-author publications. The findings suggest that researchers in various parts of the world, particularly Africa and the Middle East, should route their research efforts towards this field, in light of the lack of publications from these regions on this subject. The aim of this study was to serve as a seminal reference for potential technological research directions and collaboration among researchers in this and other related fields.

Based on the requisition for an ideal precise power source for a hybrid electric emergency rescue vehicle (HE-ERV), we present an optimistic parameter matching and optimization schemes for the selection of a HE-ERV. Then, given a set of optimized power source components, they are classified into different types of HE-ERV. In this study, due to the different design objectives of different types of emergency rescue vehicles and the problems of hybrid electric vehicle parameter matching, a multi-island genetic algorithm (MIGA) and non-linear programming quadratic Lagrangian (NLPQL) is proposed for the matched parameters. The vehicle dynamic model is established based on the AVL Cruise simulation platform. The power source performance parameters are matched by theoretical analysis and coupled to the simulation platform. Finally, the optimized matched parameters are classified based on the support vector machines classification model to determine the category of the HE-ERV. The classification results showed that there is an unprecedented level for categorizing several factors of the power source parameters. This research showed that its more logical and reasonable to match HE-ERVs with medium motor/engine power output and battery capacity, as these can attain dynamic performance, extended driving range, and reduced energy consumption.