Published on: August 15, 2017
Updated on: July 1, 2021
Every numerical method relies on the accurate choice of models, solver settings, and material parameters in order to be able to mimic real-world behavior.
This is not different for Discrete Elements Method (DEM) simulations, where the calibration of the material interaction parameters is a key step of all projects.
At least friction and restitution coefficients between particles and particles and boundaries have to be adjusted in order to reproduce the bulk flow behavior.
When extra forces come to play, such as adhesion forces, additional parameters have to be selected.
In general, the calibration process consists of reproducing simple experimental tests and verifying the matching between numerical results and experimental data. As such, multiple simulations have to be run in order to find the set of parameters that best fit the experimental values.
Metamodel of Optimal Prognosis (MOP) for the simulated angle of repose, which is an average over the pile and depends on the dynamic and the rolling friction.
The integration between ANSYS Optislang and Rocky DEM can boost the calibration process by combining the powerful parametric modeling capabilities of ANSYS Workbench with the robust design optimization (RDO) methods of Opstislang.
By using optiSLang plug-in, the input parameters selection and variation are automatic, based on sensitivity analysis and meta-modeling techniques, reducing the number of cases to be run and allowing the user to confidently adjust the interaction parameters for the project.
Want to know more about Rocky DEM – ANSYS optiSLang?
Curious about the possibilities of this interface and how it can improve your design? Watch this free webinar and find out more about the integration.
You will also know how easy it is to use sensitivity analysis and meta-modeling techniques to find the best fit of particle data with the experimental data.
