permeability 0.2.0

A python package for calculating permeability

permeability

中文 English

A Python package for calculating permeability of porous media using the seepage distance method, derived from the Darcy's law-based formula, with capillary pressure correction via the Young-Laplace equation:

$$ K = \frac{L^2 \cdot \mu \cdot \phi}{2 \cdot t \cdot \Delta P} $$

$$ t = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot \Delta P} $$

$$ z(t) = \sqrt{\frac{2 \cdot K \cdot \Delta P \cdot t}{\mu \cdot \phi}} $$

$$ p_c = \frac{2 \cdot \gamma \cdot \cos(\theta)}{r} $$

Capillary-Corrected Forms

When capillary pressure $p_c$ is considered, $\Delta P$ is replaced by $(\Delta P + p_c)$:

$$ K = \frac{L^2 \cdot \mu \cdot \phi}{2 \cdot t \cdot (\Delta P + p_c)} $$

$$ t = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot (\Delta P + p_c)} $$

$$ z(t) = \sqrt{\frac{2 \cdot K \cdot (\Delta P + p_c) \cdot t}{\mu \cdot \phi}} $$

$$ \Delta P = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot t} $$

$$ \Delta P = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot t} - p_c $$

Symbol	Meaning	Unit
$K$	Permeability	m²
$L$	Sample thickness along flow direction	m
$\mu$	Dynamic viscosity of fluid	Pa·s
$\phi$	Porosity of porous medium	dimensionless
$t$	Time for fluid to fully penetrate sample	s
$\Delta P$	Constant pressure difference across sample	Pa
$z$	Infiltration front position	m
$p_c$	Capillary pressure	Pa
$\gamma$	Surface tension of the liquid	N/m
$\theta$	Contact angle (wetting angle)	Degrees
$r$	Equivalent pore radius	m

This package also provides an MCP server that exposes these calculations as tools for AI assistants like Claude.

---

Installation

Using pip

pip install permeability

Using uv (recommended)

uv add permeability

---

Quick Start

Calculate Permeability from Experimental Data

from permeability.permeability import Seepage

# Experimental parameters
K = Seepage.calculate_permeability(
    L=0.003,   # Sample thickness: 3 mm
    mu=0.192,  # Fluid viscosity: 0.192 Pa·s
    phi=0.445, # Porosity: 0.445
    t=100,     # Penetration time: 100 s
    dP=1e4     # Pressure difference: 10,000 Pa
)
print(f"Permeability: {K:.3e} m²")  # ~3.84e-13 m²

Predict Infiltration Time

t = Seepage.calculate_infiltration_time(
    L=0.003,
    mu=0.192,
    phi=0.445,
    K=3.8448e-13,
    dP=1e4
)
print(f"Infiltration time: {t:.2f} s")  # ~100.00 s

Calculate Infiltration Front Position

# Single time point
z = Seepage.calculate_infiltration_front_position(
    K=1.284e-13,
    mu=0.192,
    phi=0.642,
    dP=1e5,
    t=120
)
print(f"Front position: {z:.4f} m")  # ~0.0050 m

# Multiple time points (for graphing)
import numpy as np
z_array = Seepage.calculate_infiltration_front_position_with_multiple_time(
    K=1.284e-13,
    mu=0.192,
    phi=0.642,
    dP=1e5,
    t=np.array([0, 30, 120])
)
print(f"Front positions: {z_array}")  # [0.0, 0.0025, 0.005]

Calculate Capillary Pressure

from permeability.permeability.Capillary import calculate_capillary_pressure

# Capillary pressure via Young-Laplace equation
p_c = calculate_capillary_pressure(
    gamma=0.072,  # Surface tension of water: 0.072 N/m
    theta=30,     # Contact angle: 30 degrees
    r=1e-6        # Pore radius: 1 µm
)
print(f"Capillary pressure: {p_c:.2f} Pa")  # ~124.71 Pa

Permeability with Capillary Correction

# Calculate permeability considering capillary pressure
K_corrected = Seepage.calculate_permeability(
    L=0.003,
    mu=0.192,
    phi=0.445,
    t=100,
    dP=1e4,
    p_c=124.71  # Capillary pressure (Pa)
)
print(f"Corrected permeability: {K_corrected:.3e} m²")

Calculate Pressure Difference

# Calculate the pressure difference applied across the sample
dP = Seepage.calculate_pressure_difference(
    L=0.003,
    mu=0.192,
    phi=0.445,
    K=3.8448e-13,
    t=100
)
print(f"Pressure difference: {dP:.2f} Pa")  # ~10000.00 Pa

# With capillary correction
dP_corrected = Seepage.calculate_pressure_difference(
    L=0.003,
    mu=0.192,
    phi=0.445,
    K=3.8448e-13,
    t=100,
    p_c=124.71  # Capillary pressure (Pa)
)
print(f"Corrected pressure difference: {dP_corrected:.2f} Pa")

Unit Conversion

from permeability.utils.UnitConverter import darcy2m2, m22darcy

# Convert m² to Darcy
darcy = m22darcy(m2K=3.8448e-13)
print(f"Permeability: {darcy:.3f} Darcy")  # ~0.390 Darcy

# Convert Darcy to m²
m2 = darcy2m2(darcyK=0.3896)
print(f"Permeability: {m2:.3e} m²")  # ~3.845e-13 m²

Anisotropic Permeability Tensor

For orthotropic materials (e.g., woven composites), the PermeabilityTensor class represents a diagonal permeability tensor $K = \text{diag}(K_x, K_y, K_z)$.

from permeability.permeability.AnisotropicTensor import PermeabilityTensor

# Construct from principal values (orthotropic)
tensor = PermeabilityTensor.from_principal_values(

    Kx=1e-12,  # Warp/fiber direction (m²)
    Ky=5e-13,  # Weft direction (m²)
    Kz=1e-13   # Through-thickness direction (m²)
)

# Isotropic tensor
iso_tensor = PermeabilityTensor.from_isotopic(K=1e-12)

# Transversely isotropic (e.g., unidirectional fiber bundles)
trans_tensor = PermeabilityTensor.from_transversely_isotropic(
    K_in_plane=1e-12,
    K_out_plane=1e-13
)

Tensor Properties

# 3x3 diagonal matrix
print(tensor.tensor)

# Average in-plane permeability (Kx + Ky) / 2
print(f"In-plane avg: {tensor.in_plane_average:.3e} m²")

# Anisotropy ratio β = in_plane_avg / Kz
print(f"Anisotropy ratio: {tensor.anisotropy_ratio:.2f}")

# Degree of anisotropy (0 = isotropic, 1 = fully anisotropic)
print(f"Degree of anisotropy: {tensor.degree_of_anisotropy:.3f}")

# Export to dictionary
print(tensor.to_dict())

Darcy Velocity in Anisotropic Media

import numpy as np

# Darcy velocity: v = -(1/μ) · K · ∇p
grad_p = np.array([1000, 500, 100])  # Pressure gradient (Pa/m)
v = tensor.darcy_velocity(grad_p=grad_p, mu=0.192)
print(f"Darcy velocity: {v} m/s")

Effective Permeability in a Direction

# Along principal axes
print(tensor.effective_permeability_in_direction(direction='x'))
print(tensor.effective_permeability_in_direction(direction='xy'))  # in-plane avg

# In an arbitrary direction
n = np.array([1, 1, 0])  # arbitrary direction vector
print(tensor.effective_permeability_in_direction(direction_vector=n))

Anisotropic Darcy Flux

The anisotropic_darcy_flux function computes Darcy flux and related quantities in anisotropic media.

from permeability.permeability.AnisotropicTensor import (
    PermeabilityTensor,
    anisotropic_darcy_flux
)

tensor = PermeabilityTensor.from_principal_values(
    Kx=1e-12, Ky=5e-13, Kz=1e-13
)

result = anisotropic_darcy_flux(
    tensor=tensor,
    grad_p=np.array([1000, 500, 100]),  # Pa/m
    mu=0.192,                            # Pa·s
    area_normal=np.array([1, 0, 0])      # Optional: unit normal of cross-section
)
print(f"Darcy velocity: {result['darcy_velocity']} m/s")
print(f"Flux magnitude: {result['flux_magnitude']:.3e} m/s")
print(f"Velocity angle from gradP: {result['velocity_angle_from_gradP_deg']:.2f}°")
print(f"Area flux: {result['area_flux']:.3e} m³/s per m²")

---

MCP Server

The package includes a Model Context Protocol (MCP) server that exposes permeability calculations as AI-accessible tools. After installing the package, start the server with:

Starting the Server

Once installed, simply run:

# Default port 8000
permeability_mcp

# Custom port
permeability_mcp --port 8080

The server starts via HTTP/SSE transport (http://localhost:8000 by default).

If you prefer to try it without installing, you can also use:

uvx --from permeability permeability_mcp

Configuration for AI Assistants

For Claude Desktop, add to your claude_desktop_config.json:

{
  "mcpServers": {
    "permeability": {
      "command": "permeability_mcp",
      "args": ["--port", "8080"]
    }
  }
}

For Cline (VS Code extension), add to your MCP settings:

{
  "mcpServers": {
    "permeability": {
      "command": "permeability_mcp",
      "args": ["--port", "8080"]
    }
  }
}

Available Tools

Once the MCP server is running, AI assistants can call:

Tool	Description
calculate_permeability_by_seepage_distance	Calculate $K$ (optionally with capillary correction via $p_c$)
calculate_infiltration_time	Calculate $t$ (optionally with capillary correction via $p_c$)
calculate_infiltration_front_position	Calculate $z(t)$ (optionally with capillary correction via $p_c$)
calculate_infiltration_front_position4multiple_times	Calculate $z(t)$ for multiple time points (optionally with capillary correction via $p_c$)
calculate_capillary_pressure	Calculate $p_c = 2\gamma\cos(\theta),/,r$ (Young-Laplace equation)
calculate_pressure_difference	Calculate $\Delta P$ (optionally with capillary correction via $p_c$)
darcy_m2_converter	Convert Darcy to m² and/or m² to Darcy (bidirectional)
calculate_darcy_flux_tool	Compute Darcy flux in anisotropic media; accepts permeability tensor from principal values, isotropic, or transversely isotropic

Each tool accepts the same parameters as the Python API.

---

API Reference

Seepage.calculate_permeability(L, mu, phi, t, dP, p_c=None)

Calculate permeability from seepage distance experiment data. When $p_c$ is provided, the capillary-corrected form is used.

$$ K = \frac{L^2 \cdot \mu \cdot \phi}{2 \cdot t \cdot \Delta P} \qquad\text{or}\qquad K = \frac{L^2 \cdot \mu \cdot \phi}{2 \cdot t \cdot (\Delta P + p_c)} $$

Parameter	Type	Description
L	float	Sample length/thickness along flow direction (m)
mu	float	Dynamic viscosity of fluid (Pa·s)
phi	float	Porosity of porous medium ($0 < \phi \le 1$)
t	float	Total time for fluid to fully penetrate sample (s)
dP	float	Constant pressure difference across sample (Pa)
p_c	float, optional	Capillary pressure for correction (Pa)

Returns: float — Permeability $K$ (m²)

---

Seepage.calculate_infiltration_time(L, mu, phi, K, dP, p_c=None)

Predict the time required for fluid to fully penetrate a sample. When $p_c$ is provided, the capillary-corrected form is used.

$$ t = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot \Delta P} \qquad\text{or}\qquad t = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot (\Delta P + p_c)} $$

Parameter	Type	Description
L	float	Sample length/thickness along flow direction (m)
mu	float	Dynamic viscosity of fluid (Pa·s)
phi	float	Porosity of porous medium ($0 < \phi \le 1$)
K	float	Permeability (m²)
dP	float	Constant pressure difference across sample (Pa)
p_c	float, optional	Capillary pressure for correction (Pa)

Returns: float — Infiltration time $t$ (s)

---

Seepage.calculate_infiltration_front_position(K, mu, phi, dP, t, p_c=None)

Calculate the infiltration front position at a given time. When $p_c$ is provided, the capillary-corrected form is used.

$$ z(t) = \sqrt{\frac{2 \cdot K \cdot \Delta P \cdot t}{\mu \cdot \phi}} \qquad\text{or}\qquad z(t) = \sqrt{\frac{2 \cdot K \cdot (\Delta P + p_c) \cdot t}{\mu \cdot \phi}} $$

Parameter	Type	Description
K	float	Permeability (m²)
mu	float	Dynamic viscosity of fluid (Pa·s)
phi	float	Porosity of porous medium ($0 < \phi \le 1$)
dP	float	Constant pressure difference across sample (Pa)
t	float	Time elapsed (s)
p_c	float, optional	Capillary pressure for correction (Pa)

Returns: float — Infiltration front position $z$ (m)

---

Seepage.calculate_infiltration_front_position_with_multiple_time(K, mu, phi, dP, t, p_c=None)

Calculate the infiltration front position at multiple time points (useful for plotting). When $p_c$ is provided, the capillary-corrected form is used.

$$ z(t) = \sqrt{\frac{2 \cdot K \cdot \Delta P \cdot t}{\mu \cdot \phi}} \qquad\text{or}\qquad z(t) = \sqrt{\frac{2 \cdot K \cdot (\Delta P + p_c) \cdot t}{\mu \cdot \phi}} $$

Parameter	Type	Description
K	float	Permeability (m²)
mu	float	Dynamic viscosity of fluid (Pa·s)
phi	float	Porosity of porous medium ($0 < \phi \le 1$)
dP	float	Constant pressure difference across sample (Pa)
t	numpy.ndarray	Array of time values (s)
p_c	float, optional	Capillary pressure for correction (Pa)

Returns: numpy.ndarray — Array of infiltration front positions $z$ (m)

---

Seepage.calculate_pressure_difference(L, mu, phi, K, t, p_c=None)

Calculate the pressure difference across a sample. When $p_c$ is provided, the capillary-corrected form is used.

$$ \Delta P = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot t} \qquad\text{or}\qquad \Delta P = \frac{\mu \cdot \phi \cdot L^2}{2 \cdot K \cdot t} - p_c $$

Parameter	Type	Description
L	float	Sample length/thickness along flow direction (m)
mu	float	Dynamic viscosity of fluid (Pa·s)
phi	float	Porosity of porous medium ($0 < \phi \le 1$)
K	float	Permeability (m²)
t	float	Total time for fluid to fully penetrate sample (s)
p_c	float, optional	Capillary pressure for correction (Pa)

Returns: float — Pressure difference $\Delta P$ (Pa)

---

calculate_capillary_pressure(gamma, theta, r)

Calculate capillary pressure using the Young-Laplace equation.

$$ p_c = \frac{2 \cdot \gamma \cdot \cos(\theta)}{r} $$

Parameter	Type	Description
gamma	float	Surface tension of the liquid (N/m)
theta	float	Contact angle / wetting angle (Degrees)
r	float	Equivalent pore radius (m)

Returns: float — Capillary pressure $p_c$ (Pa)

---

darcy2m2(darcyK)

Convert permeability from Darcy to m².

Parameter	Type	Description
darcyK	float	Permeability in Darcy

Returns: float — Permeability in m²

---

m22darcy(m2K)

Convert permeability from m² to Darcy.

Parameter	Type	Description
m2K	float	Permeability in m²

Returns: float — Permeability in Darcy

---

PermeabilityTensor(Kx, Ky, Kz)

Anisotropic permeability tensor for 2D woven composites. Represents a diagonal permeability tensor $K = \text{diag}(K_x, K_y, K_z)$.

Parameter	Type	Description
Kx	float	Permeability in x-direction (warp/fiber direction) (m²)
Ky	float	Permeability in y-direction (weft direction) (m²)
Kz	float	Permeability in z-direction (through-thickness) (m²)

Class Methods

PermeabilityTensor.from_principal_values(Kx, Ky, Kz)

Construct tensor from principal direction permeabilities (orthotropic materials).

PermeabilityTensor.from_isotopic(K)

Construct an isotropic permeability tensor where $K_x = K_y = K_z = K$.

PermeabilityTensor.from_transversely_isotropic(K_in_plane, K_out_plane)

Construct a transversely isotropic permeability tensor where $K_x = K_y = K_\text{in-plane}$, $K_z = K_\text{out-plane}$ (suitable for unidirectional fiber bundles).

Properties

Property	Return Type	Description
tensor	numpy.ndarray	Full 3x3 diagonal tensor matrix
in_plane_average	float	Average in-plane permeability $(K_x + K_y),/,2$ (m²)
anisotropy_ratio	float	Anisotropy ratio $\beta = \text{in_plane_avg},/,K_z$
degree_of_anisotropy	float	Degree of anisotropy (0 = isotropic, 1 = fully anisotropic)

Methods

darcy_velocity(grad_p, mu)

Calculate Darcy velocity vector $v = -(1/\mu) \cdot K \cdot \nabla p$.

Parameter	Type	Description
grad_p	numpy.ndarray	Pressure gradient vector (Pa/m), shape (3,)
mu	float	Dynamic viscosity (Pa·s)

Returns: numpy.ndarray — Darcy velocity vector (m/s), shape (3,)

effective_permeability_in_direction(direction=None, direction_vector=None)

Calculate effective permeability in a specified direction. For a direction unit vector $n$, $K_\text{eff} = n^T \cdot K \cdot n$.

Parameter	Type	Description
direction	str, optional	One of 'x', 'y', 'z', 'xy' (in-plane), or 'avg'
direction_vector	numpy.ndarray, optional	Arbitrary unit vector of shape (3,)

Returns: float — Effective permeability in the specified direction (m²)

rotate(R)

Rotate the permeability tensor by rotation matrix $R$: $K' = R \cdot K \cdot R^T$.

Parameter	Type	Description
R	numpy.ndarray	Orthogonal rotation matrix (3x3)

Returns: PermeabilityTensor or FullTensor — Rotated tensor (diagonal if off-diagonal terms negligible)

to_dict()

Export tensor data as a dictionary with keys: Kx, Ky, Kz, in_plane_average, anisotropy_ratio, degree_of_anisotropy.

Returns: dict

---

FullTensor(matrix)

Full 3x3 symmetric permeability tensor with off-diagonal components, used after coordinate rotation.

Parameter	Type	Description
matrix	numpy.ndarray	Full 3x3 symmetric tensor matrix

Properties

Property	Return Type	Description
principal_values	tuple	Principal permeability values $(K_1, K_2, K_3)$, descending order
principal_directions	numpy.ndarray	Principal permeability directions (eigenvectors), shape (3, 3)

Methods

to_principal_tensor()

Convert to diagonal PermeabilityTensor in the principal coordinate system using eigenvalues.

Returns: PermeabilityTensor

---

anisotropic_darcy_flux(tensor, grad_p, mu, area_normal=None)

Compute Darcy flux and related quantities for anisotropic media.

Parameter	Type	Description
tensor	PermeabilityTensor	Anisotropic permeability tensor
grad_p	numpy.ndarray	Pressure gradient vector (Pa/m)
mu	float	Dynamic viscosity (Pa·s)
area_normal	numpy.ndarray, optional	Unit normal vector of cross-section area for flux computation

Returns: dict with keys:

Key	Description
darcy_velocity	Darcy velocity vector (m/s)
flux_magnitude	Magnitude of Darcy velocity (m/s)
velocity_angle_from_gradP_deg	Angle between velocity and pressure gradient (degrees)
area_flux	Volumetric flux per unit area (m³/s per m²), only if area_normal provided

---

Development

Setup

git clone https://github.com/1Vewton/permeability.git
cd permeability
uv sync

Running Tests

uv run pytest

Code Style

uv run flake8

---

License

This project is licensed under the GNU General Public License v3.0. See the LICENSE file for details.