Calibrating the microparameters of DEM models using the ant colony optimization algorithm and the optimal...

Area of Science:

• Computational mechanics
• Granular material simulation
• Numerical modeling

Background:

• Particle Flow Code (PFC) is a common discrete element method (DEM) for simulating granular materials.
• Traditional micro-parameter calibration for PFC is labor-intensive, user-dependent, and computationally expensive.
• Existing methods often require extensive datasets, adding complexity to the calibration process.

Purpose of the Study:

• To develop an automated and efficient framework for calibrating micro-parameters in PFC simulations.
• To overcome the limitations of trial-and-error calibration techniques.
• To enhance the accuracy and reduce the computational cost of granular material modeling.

Main Methods:

• Implementation of an ant colony optimization algorithm within a Python scripting environment.
• Integration of simulated annealing to optimize hyperparameters for the ant colony algorithm.
• Automated calibration process controlled by script, eliminating human interference and dataset requirements.

Main Results:

• The proposed ant colony optimization framework effectively calibrates PFC micro-parameters without requiring predefined datasets.
• The integration of simulated annealing significantly reduced the number of calibration iterations.
• Verification through two examples confirmed the method's effectiveness in numerical simulations.

Conclusions:

• The automated calibration framework offers a robust and efficient alternative to traditional methods for PFC micro-parameter determination.
• The combined use of ant colony optimization and simulated annealing provides a computationally advantageous approach.
• This method enhances the reliability and accessibility of discrete element method simulations for granular materials.