Particle-level friction coefficient of a rigid clump

Same setup as the SASA example, but the quantity of interest is the probe-reported friction coefficient mu = |F_t| / |F_n| instead of the surface area. We take the mean of mu over the sampled approach directions; since the directions are a near-uniform Fibonacci lattice on S^2, that’s the surface-averaged friction to leading order.

Numerics match sasa_calculation.py: target_overlap = 1e-10 and separation_tolerance = 1e-12, which requires x64.

Enable x64 before any other JAX work.

import jax

jax.config.update("jax_enable_x64", True)  # type: ignore[no-untyped-call]

import numpy as np

from jaxdem.utils.particle_creation import create_ga_state, create_sphere_state
from jaxdem.utils.surface_properties import compute_surface_properties

Central rigid clump + tracer sphere

central = create_ga_state(
    N=1,
    nv=20,
    dim=3,
    particle_radius=0.5,
    asperity_radius=0.1,
    particle_type="clump",
    core_type="solid",  # clumps need to be solid for the sasa protocol to be robust
    n_samples=10_000_000,  # the default; ~10M samples give decent accuracy for the clump COM
    seed=0,
    mesh_kwargs={"steps": 1_000},
)

tracer_radius = 0.05
tracer = create_sphere_state(radii=tracer_radius, dim=3)

Probe the surface

n_points = 1024

result = compute_surface_properties(
    central,
    tracer,
    target_overlap=1e-10,
    separation_tolerance=1e-12,
    n_points=n_points,
    n_orientations=1,
    n_rolls=1,
)

mu = np.asarray(result["mu"]).reshape(n_points)
print(f"mu: min={mu.min():.4f}  max={mu.max():.4f}")

mu: min=0.0004  max=1.8231

Mean friction across the sampled approach directions.

mean_friction = float(mu.mean())
print(f"mean friction = {mean_friction:.4f}")

mean friction = 0.4731