# Effective Medium Theory Mapping¶

This example uses Self Consistent Effective Medium Theory to estimate the electrical conductivity of a mixture of two phases of materials. Given the electrical conductivity of each of the phases ($$\sigma_0$$, $$\sigma_1$$), the SimPEG.Maps.SelfConsistentEffectiveMedium map takes the concentration of phase-1 ($$\phi_1$$) and maps this to an electrical conductivity.

This mapping is used in chapter 2 of:

Heagy, Lindsey J.(2018, in prep) Electromagnetic methods for imaging subsurface injections. University of British Columbia

author

@lheagy

import numpy as np
import matplotlib.pyplot as plt
from SimPEG import Maps
from matplotlib import rcParams
rcParams['font.size'] = 12


## Conductivities¶

Here we consider a mixture composed of fluid (3 S/m) and conductive particles which we will vary the conductivity of.

sigma_fluid = 3
sigma1 = np.logspace(1, 5, 5) # look at a range of particle conductivities
phi = np.linspace(0.0, 1, 1000) # vary the volume of particles


## Construct the Mapping¶

We set the conductivity of the phase-0 material to the conductivity of the fluid. The mapping will then take a concentration (by volume), of phase-1 material and compute the effective conductivity

scemt = Maps.SelfConsistentEffectiveMedium(sigma0=sigma_fluid, sigma1=1)


## Loop over a range of particle conductivities¶

We loop over the values defined as sigma1 and compute the effective conductivity of the mixture for each concentration in the phi vector

sige = np.zeros([phi.size, sigma1.size])

for i, s in enumerate(sigma1):
scemt.sigma1 = s
sige[:, i] = scemt * phi


Out:

/Users/lindseyjh/git/python_symlinks/SimPEG/Maps.py:896: UserWarning: Maximum number of iterations reached
warnings.warn('Maximum number of iterations reached')


## Plot the effective conductivity¶

The plot shows the effective conductivity of 5 difference mixtures. In all cases, the conductivity of the fluid, $$\sigma_0$$, is 3 S/m. The conductivity of the particles is indicated in the legend

fig, ax = plt.subplots(1, 1, figsize=(7, 4), dpi=350)

ax.semilogy(phi, sige)
ax.grid(which="both", alpha=0.4)
ax.legend(["{:1.0e} S/m".format(s) for s in sigma1])
ax.set_xlabel("Volume fraction of proppant $\phi$")
ax.set_ylabel("Effective conductivity (S/m)")

plt.tight_layout() Total running time of the script: ( 0 minutes 0.515 seconds)

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