Modeling Segregation in Granular Flows.

Area of Science:

• Physics
• Engineering
• Computational Science

Background:

• Dense granular flows present complex behavior, including segregation, making accurate modeling challenging.
• Previous research relied on discrete element methods (DEM) or simplified continuum models, limiting parametric studies.
• Understanding granular flow dynamics is crucial for industrial processes and natural hazard assessment.

Purpose of the Study:

• To develop and validate a continuum transport equation-based framework for predicting segregation in dense granular flows.
• To enable comprehensive parametric studies of granular flow outcomes by leveraging continuum models.
• To provide a validated theoretical approach applicable to both industrial and natural granular flow scenarios.

Main Methods:

• Extensive comparisons between computer simulations (discrete element methods - DEM), experiments, and continuum models.
• Development of a continuum framework using transport equations and phenomenological constitutive equations.
• Three-way validation of the proposed model against experimental data and DEM simulations across various flow conditions and mixtures.

Main Results:

• The developed continuum framework accurately predicts segregation in dense granular flows.
• Discrete element method (DEM) simulations provide detailed insights into granular flow and segregation mechanisms.
• The framework demonstrates robust predictive capabilities across diverse industrial and natural settings.

Conclusions:

• Accurate continuum models for dense granular flow and segregation are now achievable.
• The proposed framework offers a powerful tool for parametric studies, overcoming limitations of computational cost.
• The validated approach has broad applicability and potential for straightforward extensions to more complex scenarios.