Info for the Meeting HPC and Benchmarking
This email was sent to members at the end of November (Some were missing) on the objectives for this year.
Problem
We will use a tumbling drum for our benchmark case as there should be experimental data from a number of groups along with it being simple enough that most codes can simulate.
For the DEM Model we will use Hertz Mindlin as done in this paper with appropriate YM and time-step values, https://www.sciencedirect.com/science/article/pii/S0010465523004113
1] We will have a case with moderate particle numbers that we will use to calibrate against experiment.
2] We will do an additional case with a larger number of particles in a range of millions to benchmark performance
3] We will do a shape case using cubes as they are simple enough that most codes will have some representation for it. We aim to calibrate against experiment and will do a larger case as a performance study as in the case with spheres.
What we measure
The key metrics will be
A] mixing index (same material, two colors)
B] power (summation of the dashpot and friction dissipation energies)
C] torque arm Bruno Chareyre do you have a script ?
D] profile outline.We aim to get post analysis scripts that people already have to compare between codes and to experiment.
The sub tracks
The results from 1] will then be used for DEM UP and DEM ML to come up with a predictive model that you will get further communication on.
What we need to deliver
Officially we need to have at-least two open source codes Currently we have commitments for Yade and Mercury.
Beyond that any code that you want can be used as we aim to compare in a code agnostic manner.
What we require
A] Experiment data along with the configuration used.
B] The code that you want to use and/or if you are willing to run cases.
C] If you can help with scripts for processing the results or have suggestions.
CalibrationYannick Descantes has a test case that we will use to check numerical stability in heap formation.
Split into two groups HPC and Alternative Methods (AI/ML, Surrogate models)
G2 - Alternative methods by Deepak Tunuguntla Hongyang Cheng
What are the steps we need to take to achieve the benchmarking objective?
How to approach it and where to get started?