Numerical Simulation Study of a Pusher Feed Classifier Based on RNG-DPM Method

Abstract

The classifier is an essential tool for the development of contemporary engineering technology. The application of classifiers is to categorize mixed-sized particles into multi-stage uniform particle sizes. In current studies, the particles in the classifier obtain their initial velocity when feeding. The classification effect is impacted by the inability to precisely control the initial state of the particles. To solve this problem, a pusher feed classifier was designed in this study, and a numerical simulation was performed to investigate its flow field characteristics and classification performance using the RNG-DPM method. A pusher is utilized to achieve particle feeding without initial velocity and to precisely control the initial state of the particles in the classification flow field. A newly developed two-way air inlet structure is designed to provide a superimposed flow field and enable the five-stage classification. Our results show that this pusher feed classifier has the best classification effect when the vertical airflow velocity is 10 m/s and the horizontal airflow velocity is 3 m/s. Meanwhile, the classification size ratio (CSR) from outlet 1 to outlet 5 was 1.24, 0.55, 0.45, 0.39, and 0.15, respectively.