Meshless Modeling: Navigating LIGGGHTS and OpenFOAM’s mppicFoam on cloudHPC
When it comes to simulating the movement of matter, there isn’t a “one size fits all” approach. If you are modeling a landslide, a breaking wave, or a conveyor belt filled with minerals, the physics engine you choose determines the accuracy of your results.
Two of the most powerful particle-based methods today are Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH). While they both look like “balls moving in a box,” they are fundamentally different under the hood.
DEM vs. SPH: Spotting the Difference
Look at the image provided. It shows a series of discrete particles representing a fluid or granular volume.
1. Discrete Element Method (DEM)
In DEM, each sphere represents a real physical object—like a grain of sand, a tablet, or a rock.
- The Physics: It focuses on contact. When two particles touch, the solver calculates the friction, overlap, and bouncing force.
- Best For: Granular flows, mixing powders, soil mechanics, and bulk material handling (conveyor belts, hoppers).
- The Look: In a DEM simulation, you see individual particles maintaining their identity; they don’t “merge” or behave like a continuous liquid.
2. Smoothed Particle Hydrodynamics (SPH)
In SPH, each sphere is a computational point used to approximate a continuous medium (like water or air).
- The Physics: It is “meshless” fluid dynamics. Instead of calculating collisions, it uses a kernel function to smooth out properties (like density and pressure) across neighboring particles.
- Best For: Free-surface flows (waves, splashing), high-deformation impacts (bird strikes), and complex sloshing.
- The Look: As seen in the image, the particles are often used to visualize the flow field. Notice the “v Magnitude” scale; in SPH, we care about how these points collectively represent the velocity and pressure of a single fluid body.
Methodology: Running These on cloudHPC
Running high-resolution particle simulations requires massive CPU power. cloudHPC provides a pre-configured environment to run these complex solvers without needing your own supercomputer. Here are the two primary methodologies available:
Methodology A: Granular Mastery with LIGGGHTS (DEM)
LIGGGHTS is an open-source DEM engine known for its industrial-strength capabilities.
- Capabilities: It handles complex wall geometries (imported via STL), moving meshes, and heat transfer between particles.
- Use Case: If you need to simulate how a “dam break” of gravel behaves or how pharmaceutical pills fill a bottle, LIGGGHTS is the tool.
- Cloud Advantage: Since DEM scales with the number of particles, you can utilize cloudHPC’s multi-node MPI environment to run millions of particles simultaneously.
Methodology B: Fluid-Particle Coupling with mppicFoam (OpenFOAM)
If your simulation involves particles carried by a fluid (like sand in a pipe or smoke in the air), the mppicFoam solver within OpenFOAM is the gold standard.
- How it Works: It uses the Multiphase Particle-in-Cell (MP-PIC) method. This is an Eulerian-Lagrangian approach where the fluid is treated as a continuum (Eulerian) and the particles are tracked as parcels (Lagrangian).
- Capabilities: It is incredibly efficient for dense particle-laden flows because it models particle collisions as a “stress gradient” rather than calculating every single individual contact.
- Use Case: Cyclone separators, fluidized beds, and chemical reactors.
Which one should you choose?
| Feature | LIGGGHTS (DEM) | mppicFoam (OpenFOAM) |
| Primary State | Solid / Granular | Fluid + Solid (Multiphase) |
| Interaction | Explicit contact & friction | Collision modeling via stress gradients |
| Complexity | High (each contact is computed) | Scalable (parcels reduce overhead) |
| Common Task | Hopper discharge, soil mixing | Gasification, exhaust filtration |
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