kabs

kabs

kabs computes the absolute (Darcy) permeability of a porous material from its 3D tomographic image using the Lattice Boltzmann Method (LBM). Given a binary voxel image of the pore space, it solves single-phase incompressible creeping flow, returning results in lattice units or physical units.

The LBM implementation is adapted from Taichi-LBM3D (DOI) by Jianhui Yang.

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Installation

git clone https://github.com/PMEAL/kabs.git
cd kabs
pip install -e .

Key dependencies: taichi (GPU/CPU acceleration), numpy, pyevtk. Optional: porespy (used in the examples below to generate synthetic images).

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Quick start

import taichi as ti
import porespy as ps
from kabs import solve_flow, compute_permeability

ti.init(arch=ti.cpu)  # use ti.gpu for GPU acceleration

# Generate a synthetic test image (1 = pore, 0 = solid)
im = ps.generators.cylinders([200, 200, 200], r=10, porosity=0.7).astype(int)

# Run the LBM simulation and get a FlowResult back
result = solve_flow(im, direction="x")

# Optionally save to a VTR file for later inspection
result.export_to_vtk("sample")

# Compute permeability directly from the FlowResult
results = compute_permeability(result)
print(f"k = {results['k_lu']:.4e} voxels²")

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Common usage patterns

GPU acceleration

Taichi supports CUDA, Metal, and Vulkan backends. Switch by changing ti.init:

ti.init(arch=ti.gpu)   # picks the best available GPU backend
ti.init(arch=ti.cuda)  # CUDA explicitly

Physical units

Pass the voxel size dx_m (in metres) to compute_permeability to get results in m² and milliDarcy:

result = solve_flow(im, direction="x")
results = compute_permeability(
    result,
    dx_m=2.85e-6,   # 2.85-micron voxels, typical for micro-CT
)
print(f"k = {results['k_mD']:.2f} mD")
print(f"k = {results['k_m2']:.4e} m²")

Convergence

By default solve_flow stops early once the velocity field has converged to within a relative tolerance of 1e-3 (i.e. delta|v| / |v| < 1e-3). The actual number of steps run is printed and reflected in the auto-generated VTR filename.

# Tighten or loosen the tolerance
result = solve_flow(im, direction="x", tol=1e-4)  # tighter
result = solve_flow(im, direction="x", tol=1e-2)  # faster, coarser

# Disable early stopping and always run n_steps
result = solve_flow(im, direction="x", n_steps=5000, tol=None)
compute_permeability(result)

The convergence check fires every log_every steps (default 500), so the true stopping point is rounded to that interval.

To save the converged result to a VTR file, call export_to_vtk on the returned object:

result = solve_flow(im, direction="x", tol=1e-4)
result.export_to_vtk("sample")  # writes sample-<step>-x.vtr

Full permeability tensor

For anisotropic materials, run all three directions:

results = {}
for ax in ("x", "y", "z"):
    result = solve_flow(im, direction=ax)
    results[ax] = compute_permeability(result, dx_m=2.85e-6)

print(f"Kx={results['x']['k_mD']:.2f}  Ky={results['y']['k_mD']:.2f}  Kz={results['z']['k_mD']:.2f}  mD")

Loading results from a VTR file

solve_flow returns a FlowResult you can use immediately, but if you have a previously saved .vtr file you can reload it with read_flow_vtr:

from kabs import read_flow_vtr, compute_permeability

result = read_flow_vtr("sample-1000-x.vtr")
results = compute_permeability(result, dx_m=2.85e-6)

Memory-efficient sparse storage

For images with a high solid fraction, enable sparse storage so only pore voxels are allocated in GPU memory:

result = solve_flow(im, direction="x", sparse=True)

Return values (permeability)

compute_permeability returns a dict:

Key	Description
porosity	Pore volume fraction (dimensionless)
u_darcy	Darcy (superficial) velocity [lattice units]
u_pore	Mean pore-space velocity [lattice units]
k_lu	Permeability in lattice units (voxels²)
k_m2	Permeability in m² (None if dx_m not given)
k_mD	Permeability in milliDarcy (None if no dx_m)

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How it works

1. A pressure-driven flow is imposed by fixing density (ρ_in = 1.00, ρ_out = 0.99) on opposite faces of the domain along the chosen axis; the other four faces are periodic.

2. The D3Q19 MRT-LBM collision operator evolves the distribution functions to steady state. Solid voxels use bounce-back boundary conditions.

3. Darcy’s law is applied to the converged velocity field:

   K = u_D · μ / |∇P|

   where u_D is the volume-averaged (Darcy) velocity and |∇P| = Δρ · c_s² / L with c_s² = 1/3 for D3Q19.

4. The result in lattice units is scaled to m² (or milliDarcy) using the physical voxel size dx_m.