Earlier in November 2024, below food-for-thought information was shared with almost all of the WG1 members to help achieve our goal for 2025.

Benchmarking

Problem description

• 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 to measure?

The key metrics will be

1. mixing index (same material, two colors)    @Hongyang Cheng

2. power (summation of the dashpot and friction dissipation energies)  Code

3. Torque Code

4. profile outline Code

To generate the above metrics, we aim to get post-process analysis scripts that people already have to compare between codes and to experiment. 

What we require

1. Base Case @Anthony Thornton Drum.

2. The code that you want to use and/or if you are willing to run cases. 

3. If you can help with scripts for processing the results or have suggestions.

What we need to deliver

• Officially we need to have at-least two open source codes with the implementations

  • 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 want to achieve in 2025

• Overview and comparison of different time-efficient computational strategies and methods (HPC vs Upscaling vs Surrogates)

  • Start with DEM glass beads

• Comparison with real world ground truth

Hackathon plan

Presenting the WG1 Update

• https://docs.google.com/presentation/d/1A-1bW1kANEfAay8pCeZmq3zH3Qhoo1HOjlxsja4-f0U/edit?usp=sharing

Identify available rotating drum datasets

• Experimental

  • @Ben Jenkins Is the data open-access?

• DEM

  • Datasets

    • There is data corresponding to linear spring model. Correct @Timo Plath? If yes, how can we download it?

      @Timo Plath is on it

  • Codebases to generate datasets from scratch

    • Open-source

      • MercuryDPM

        https://bitbucket.org/mercurydpm/mercurydpm/src/d10577901da712e29320de2be9249e043f0ecf8e/Drivers/RotatingDrum/?at=feature%2FPMDPM-10-CG-RotatingDrum

      • Yade
        Rotating drum: MPI, OpenMP

    • (semi/fully) Closed-source

      • GranuDigitalTwins · GitLab

        • @nicolin did the implementation in Blaze and provides Blaze binary

        • @Ben Jenkins LIGGGHTS

        • Rocky

Execution

• Split into two groups HPC and Alternative Methods (AI/ML, Surrogate models)

  • G1 - HPC by @nicolin

    • Assign tasks to complete the benchmark case of the rotating drum

  • G2 - Alternative methods by @Deepak Tunuguntla @Hongyang Cheng

    • Talk by @Timo Plath on Reduced Order DPM

    • Setup a central github repo for all the relevant ML-based algorithms for granular materials

    • To be cont.

Additional references

Different calibration techniques

@Yannick Descantes has a test case that we will use to check numerical stability in heap formation.

@Timo Plath has a script in MercuryDPM for Rotating drum and Surrogate model calibration

Summary and action points

• For this high performance comparison study, we will start with DEM using Silbert's glass spheres validated contact parameters for Linear Spring and Hertz-Mindlin

  • See Silbert’s paper and add a table of contact parameters here

  • Mono- and bi-disperse

• For speed test

  • Turn on and off energy computations

  • Turn on and off data saving

    • How long do you want the run the simulation? x seconds

    • How often do we want to save data? y times

• For surrogates

  • Generate our own datasets

Action points

• Drum size, speed,

• DEM parameters from Silbert’s paper

• How do you generate the number of particles?, particle-wall friction,

• Open-source

  • Yade - @Bruno Chareyre @Radan Ivanov @Roxana Saghafian Larijani

  • MercuryDPM - @Timo Plath @Jan-Willem Bisschop @Thomas Weinhart @Anthony Thornton @Deepak Tunuguntla

  • Liggghts-public - Hamed, @Ben Jenkins (to confirm)

• Closed-source

  • Blaze - @Rafal Kobylka @nicolin

  • Musen - Mikhail ()

• CPU Parallellisation @Bruno Chareyre @Anthony Thornton Burek

  • OpenMP

    • 1 core, 10 core, 20 core

  • MPI

    • 10 core, 100 core, 1000o cores

• GPU parallelisation @nicolin @Yannick Descantes @John P. Morrissey