Airflow Velocity Designing for Air Classifier of Manufactured Sand Based on CPFD Method

Airflow classification is the key technology for the dry separation of manufactured sand. To solve the problem of low separation accuracy and poor gradation grade, the classification process of manufactured sand under different inlet and outlet airflow velocities changes in the multi-air inlet classifier is simulated by using Barracuda based on Computational Particle Fluid Dynamics (CPFD) method. The influence of various airflow velocity in air inlets and outlet on the sand classification is analyzed. The optimal combination of airflow velocity that meets the design goals is obtained. The results show that the airflow velocity and location of the air inlet and outlet have a significant impact on medium-grained (0.15~1.18 mm) and fine-grained (0.075~0.3 mm) sand. Adjusting the airflow velocity at air inlet 2 and air outlet can most effectively change the overall sand separation effect, while 41 m/s (inlet 2) and 6 m/s (outlet) would be the best velocity combination.

Network Structure Impacts the Synchronization of Collective Beliefs

People’s beliefs are influenced by interactions within their communities. The propagation of this influence through conversational social networks should impact the degree to which community members synchronize their beliefs. To investigate, we recruited a sample of 140 participants and constructed fourteen 10-member communities. Participants first rated the accuracy of a set of statements (pre-test) and were then provided with relevant evidence about them. Then, participants discussed the statements in a series of conversational interactions, following pre-determined network structures (clustered/non-clustered). Finally, they rated the accuracy of the statements again (post-test). The results show that belief synchronization, measuring the increase in belief similarity among individuals within a community from pre-test to post-test, is influenced by the community’s conversational network structure. This synchronization is circumscribed by a degree of separation effect and is equivalent in the clustered and non-clustered networks. We also find that conversational content predicts belief change from pre-test to post-test.

The Selective Transport of Ions in Charged Nanopore with Combined Multi-Physics Fields

The selective transport of ions in nanopores attracts broad interest due to their potential applications in chemical separation, ion filtration, seawater desalination, and energy conversion. The ion selectivity based on the ion dehydration and steric hindrance is still limited by the very similar diameter between different hydrated ions. The selectivity can only separate specific ion species, lacking a general separation effect. Herein, we report the highly ionic selective transport in charged nanopore through the combination of hydraulic pressure and electric field. Based on the coupled Poisson–Nernst–Planck (PNP) and Navier–Stokes (NS) equations, the calculation results suggest that the coupling of hydraulic pressure and electric field can significantly enhance the ion selectivity compared to the results under the single driven force of hydraulic pressure or electric field. Different from the material-property-based ion selective transport, this method endows the general separation effect between different kinds of ions. Through the appropriate combination of hydraulic pressure and electric field, an extremely high selectivity ratio can be achieved. Further in-depth analysis reveals the influence of nanopore diameter, surface charge density and ionic strength on the selectivity ratio. These findings provide a potential route for high-performance ionic selective transport and separation in nanofluidic systems.