Computational Prediction of Water Flow C

This study presents a mathematical model to predict water-flow characteristics, specifically focusing on primary and secondary velocities within spiral separators. The objective is to enhance the understanding of flow behavior using the volume of Fluid (VOF) approach alongside various turbulence models including k-ε, RNG k-ε, SST k-ω, and RSM. The methodology involves computational simulations that are validated against experimental data derived from an LD9 coal spiral separator. Results indicate that primary velocity increases along the spiral trough as the radial distance from the central column increases, while air friction at the free surface decreases the primary velocity. Comparisons between predicted values and measured data demonstrate strong agreement, suggesting high accuracy of the models. Among the turbulence models employed, the Reynolds Stress Model (RSM) emerges as the most accurate, while the SST k-ω model exhibits the lowest accuracy. This research contributes valuable insights to the modeling of fluid dynamics in spiral separators, underscoring the potential for improved design and efficiency in mineral processing applications.