A computer code for analyzing nano-particle motions in an aerodynamic particle beam focusing system was developed. The code uses an accurate three-dimensional model for the Brownian diffusion of nano-particles in strongly varying pressure field in the aerodynamic lens system. Lagrangian particle trajectory analysis was performed assuming a one-way coupling model. The particle equation of motion used included drag and Brownian forces.The prediction of the 3-D model for penetration efficiency, beam divergence angle, beam diameter and radial cumulative fraction were evaluated and were compared with those of the axisymmetric models. The simulation results showed that for particle diameters less than 30 nm in helium gas, the Brownian force could significantly affect the beam focusing and particle penetration efficiency. Some potential errors in the naïve usage of axisymmetric model were discussed. It was shown that the earlier axisymmetric models lead to the incorrect mean square radial displacement of Brownian particles. The present 3-D approach, however, leads to the correct value of the radial mean square displacement of 4Dt.The effect of the inlet orifice and relaxation region on the performance of the lens system was also investigated. It was shown that the major losses of the 4 to 10 nm particles occur in the inlet orifice and relaxation region walls. For 15 to 30 nm particles, the main losses occur at the inlet orifice walls. Some alterations of the shape of the inlet orifice were examined and a new design is suggested to reduce the loss of the particles at the inlet flow control orifice.
Using open-source data, we show that despite significant reductions in global public transit during the COVID-19 pandemic, ∼20% of ridership continues during social distancing measures. Current urban transport data collection methods do not account for the distinct behavioural and psychological experiences of the population. Therefore, little is known about the travel experience of vulnerable citizens that continue to rely on public transit and their concerns over risk, safety and other stressors that could negatively affect their health and well-being. We develop a machine learning approach to augment conventional transport data collection methods by curating a population segmented Twitter dataset representing the travel experiences of ∼120,000 transit riders before and during the pandemic in Metro Vancouver, Canada. Results show a heightened increase in negative sentiments, differentiated by age, gender and ethnicity associated with public transit indicating signs of psychological stress among travellers during the first and second waves of COVID-19. Our results provide empirical evidence of existing inequalities and additional risks faced by citizens using public transit during the pandemic, and can help raise awareness of the differential risks faced by travellers. Our data collection methods can help inform more targeted social-distancing measures, public health announcements, and transit monitoring services during times of transport disruptions and closures.
To study the influence of mechanical treatments on the yield stress of chemical pulp suspensions, a traditional rheometer, coupled with local velocity measurements (ultrasonic Doppler velocimetry), was used to measure the yield stress of two types of commercial chemical pulp suspensions with different freeness values at mass concentrations (consistencies) ranging from 0.5 to 1.5%. Over the range of consistencies tested, the yield stress was found to depend on the consistency through a power law relationship for all tested samples. Moreover, the results showed that as the freeness decreased, the yield stress of hardwood suspensions increased to a maximum value then decreased. This variation in yield stress was also observed in softwood suspensions with mass concentrations above 1%. However, when the consistency was lower than 0.75%, the yield stress of softwood suspensions increased with decreasing freeness.This behaviour can be understood based on the underlying fibre properties of fibrillation, curl, and stiffness, suggesting that fibre morphology plays a significant role on the yield stress of pulp suspensions over the concentration range studied.
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