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.

In a regenerative air sweeper, airflow and dust particles entering the system are filtered and recirculated within the system. The uncirculated portion of the exhaust air in the system spreads to the ambient air, and PM_2.5 dust in the air can poison the environment and adversely affect human health. The development of an airflow control system to reduce road dust emissions and improve air quality was the main contribution of this study. A regenerative air sweeper airflow control system is designed to direct the air from the centrifugal fan back into the pickup head to fully absorb the dust particles and balance the positive and negative air pressures inside the pickup head. The modeling and analysis of the dust control system were performed using an experimental test rig system. A mathematical model of the fundamental parameters of the regenerative air sweeper and dust control system was established. Computational fluid dynamics (CFD) ANSYS was used for the analysis to determine the direction of airflow via the suction and inlet ducts. The discrete particle model (DPM) accurately predicted particle trajectories and measured the suction efficiency of particles of different shapes and types. By controlling the circulating harmful air flow in the system, the amount of PM_2.5 released into the atmosphere was reduced by 90%. The suction efficiency of the 200 $\mathsf{\mu }\mathrm{m}$ sized sand particles was higher than 95%. The results provide theoretical and methodological assistance for the development of improved road sweeper dust control systems.

Adaptive brake force distribution (BFD) is critical for the safety and efficiency of electric commercial vehicles (ECVs), yet the integration of thermal constraints, real-time control, and stability requirements remains fragmented. Following PRISMA 2020, a systematic search in IEEE Xplore and Web of Science (2016–2026) was performed, yielding 156 core studies classified into five thematic groups: fundamentals, MPC-based control, PSO, integrated frameworks, and validation methods. The analysis reveals a tripartite fragmentation: 27 MPC studies neglect thermal constraints; 22 PSO studies lack real-time adaptability; 52 studies target commercial vehicles, but none validate front–rear redistribution under concurrent thermal and stability constraints. Only two studies address thermal-aware distribution, and seven consider pneumatic delay compensation. Publication volume accelerates, indicating rising interest. The literature treats thermal management, real-time redistribution, and stability-constrained control as separate problems, leaving a critical gap: online correction of the ideal distribution curve under coupled thermal-stability constraints for ECVs is absent. To fill this gap, a hierarchical MPC-PSO framework is proposed, where PSO (1–5 Hz) adaptively tunes MPC weights based on vehicle state, and MPC (20–50 Hz) executes optimal control integrating regenerative, magnetic, and eddy current braking. This research aims to achieve two main objectives: to propose a new integrated framework that addresses the overlooked interrelationship between the stability constraints of ECVs and thermal constraints and to systematically reveal the state of integrative constraints on adaptive brake force distribution in electric commercial vehicles, with a focus on real-time control and stability requirements.