Poromics

Poromics estimates transport properties of 3D porous material images. It is GPU-accelerated and designed to be fast and easy to use.

Supported properties:

• Tortuosity / effective diffusivity — via Julia-based FD solver (tortuosity_fd) or Taichi-based LBM D3Q7 BGK solver (tortuosity_lbm)
• Absolute permeability — via Taichi-based LBM D3Q19 MRT solver (permeability_lbm)

Installation

The Julia-based FD solver depends on Tortuosity.jl, which is installed automatically. The LBM solvers use Taichi with automatic GPU detection.

Note

We highly recommend using uv instead of pip to install poromics (or any other Python package!) as it's extremely faster. It has lots of useful features, but for all practical purposes, it is a drop-in replacement for pip.

Uv

Install uv, and then run the following command in a terminal/command prompt:

uv pip install poromics

Pip

If you prefer to use pip, run the following command in a terminal/command prompt:

pip install poromics

Basic Usage

Note

The first time you call tortuosity_fd, it will take a few minutes to install Julia and the required packages. This is a one-time setup. The LBM solvers (tortuosity_lbm, permeability_lbm) use Taichi and do not require Julia.

Tortuosity (Julia FD solver)

import porespy as ps
import poromics

im = ps.generators.blobs(shape=[100, 100, 100], porosity=0.6)
result = poromics.tortuosity_fd(im, axis=0, rtol=1e-5, gpu=True)
print(result.tau, result.D_eff)

Tortuosity (LBM solver)

result = poromics.tortuosity_lbm(im, axis=0, D=1e-9, voxel_size=1e-6)
print(result.tau, result.D_eff)

Permeability (LBM solver)

result = poromics.permeability_lbm(im, axis=0, nu=1e-6, voxel_size=1e-6)
print(result.k)

Result objects

TortuosityResult attributes: im, axis, porosity, tau, D_eff, c, formation_factor, D.

PermeabilityResult attributes: im, axis, porosity, k, u_darcy, u_pore, velocity, pressure.

Simulation solvers

For more control, use the solver classes directly:

from poromics.simulation import TransientDiffusion, TransientFlow

solver = TransientDiffusion(im, axis=0, D=1e-9, voxel_size=1e-6)
solver.run(n_steps=100_000, tol=1e-2)
print(solver.concentration.shape, solver.converged)

CLI

Warning

The CLI is still in development and not yet functional.

poromics --help

Acknowledgments

The LBM solvers are based on taichi_LBM3D by Yi-Jie Huang.

Roadmap

• Diffusional tortuosity
  • Julia FD solver (Tortuosity.jl)
  • Taichi LBM D3Q7 BGK solver
• Transient tortuosity
  • Julia FD solver (Tortuosity.jl)
• Permeability
  • Taichi LBM D3Q19 MRT solver
• Electrode tortuosity
• Julia/Taichi coexistence via subprocess isolation
• Add command-line interface (CLI) for easy usage
• Add support for sysimage creation upon installation for faster startup