µSpectre

Project µSpectre aims at providing an open-source platform for efficient FFT-based continuum mesoscale modelling. This README contains only a small quick start guide. Please refer to the full documentation for more help.

Quick start

To install µSpectre, run

pip install muSpectre

Note that on most platforms this will install a binary wheel, that was compiled with a minimal configuration. To compile for your specific platform use

pip install -v --no-binary muSpectre muSpectre

which will compile the code. Monitor output for the compilation options printed on screen. µSpectre will autodetect various options and report which ones were enabled.

Simple usage example

The following is a simple example for using µSpectre through its convenient Python interface

#!/usr/bin/env python3

import numpy as np
import muSpectre as µ

# setting the geometry
nb_grid_pts = [51, 51]
center = np.array([r//2 for r in nb_grid_pts])
incl = nb_grid_pts[0]//5

lengths = [7., 5.]
formulation = µ.Formulation.small_strain

# creating the periodic cell
rve = µ.SystemFactory(nb_grid_pts,
                      lengths,
                      formulation)
hard = µ.material.MaterialLinearElastic1_2d.make(
    rve, "hard", 10e9, .33)
soft = µ.material.MaterialLinearElastic1_2d.make(
    rve, "soft",  70e9, .33)


# assign a material to each pixel
for i, pixel in enumerate(rve):
    if np.linalg.norm(center - np.array(pixel),2)<incl:
        hard.add_pixel(pixel)
    else:
        soft.add_pixel(pixel)

tol = 1e-5
cg_tol = 1e-8

# set macroscopic strain
Del0 = np.array([[.0, .0],
                 [0,  .03]])
if formulation == µ.Formulation.small_strain:
    Del0 = .5*(Del0 + Del0.T)
maxiter = 401
verbose = 2

solver = µ.solvers.SolverCG(rve, cg_tol, maxiter, verbose=False)
r = µ.solvers.newton_cg(rve, Del0, solver, tol, verbose)
print("nb of {} iterations: {}".format(solver.name(), r.nb_fev))

You can find more examples using both the python and the c++ interface in the examples and tests folder.

Funding

This development is funded by the Swiss National Science Foundation within an Ambizione Project and by the European Research Council within Starting Grant 757343.