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# -*- encoding=utf-8 -*-
#*************************************************************************
# Copyright (C) 2010 by Bruno Chareyre *
# bruno.chareyre_at_grenoble-inp.fr *
# *
# This program is free software; it is licensed under the terms of the *
# GNU General Public License v2 or later. See file LICENSE for details. *
#*************************************************************************/
## This script details the simulation of a triaxial test on sphere packings using Yade
## See the associated pdf file for detailed exercises
## the algorithms presented here have been used in published papers, namely:
## * Chareyre et al. 2002 (http://www.geosyntheticssociety.org/Resources/Archive/GI/src/V9I2/GI-V9-N2-Paper1.pdf)
## * Chareyre and Villard 2005 (https://yade-dem.org/w/images/1/1b/Chareyre&Villard2005_licensed.pdf)
## * Scholtès et al. 2009 (http://dx.doi.org/10.1016/j.ijengsci.2008.07.002)
## * Tong et al.2012 (http://dx.doi.org/10.2516/ogst/2012032)
##
## Most of the ideas were actually developped during my PhD.
## If you want to know more on micro-macro relations evaluated by triaxial simulations
## AND if you can read some french, it is here: http://tel.archives-ouvertes.fr/docs/00/48/68/07/PDF/Thesis.pdf
from yade import pack
############################################
### DEFINING VARIABLES AND MATERIALS ###
############################################
# The following 5 lines will be used later for batch execution
nRead = readParamsFromTable(
num_spheres=1000, # number of spheres
compFricDegree=30, # contact friction during the confining phase
key='_triax_base_', # put you simulation's name here
unknownOk=True
)
from yade.params import table
num_spheres = table.num_spheres # number of spheres
key = table.key
targetPorosity = 0.43 #the porosity we want for the packing
compFricDegree = table.compFricDegree # initial contact friction during the confining phase (will be decreased during the REFD compaction process)
finalFricDegree = 30 # contact friction during the deviatoric loading
rate = -0.02 # loading rate (strain rate)
damp = 0.2 # damping coefficient
stabilityThreshold = 0.01 # we test unbalancedForce against this value in different loops (see below)
young = 5e6 # contact stiffness
mn, mx = Vector3(0, 0, 0), Vector3(1, 1, 1) # corners of the initial packing
## create materials for spheres and plates
O.materials.append(FrictMat(young=young, poisson=0.5, frictionAngle=radians(compFricDegree), density=2600, label='spheres'))
O.materials.append(FrictMat(young=young, poisson=0.5, frictionAngle=0, density=0, label='walls'))
## create walls around the packing
walls = aabbWalls([mn, mx], thickness=0, material='walls')
wallIds = O.bodies.append(walls)
## use a SpherePack object to generate a random loose particles packing
sp = pack.SpherePack()
clumps = False #turn this true for the same example with clumps
if clumps:
## approximate mean rad of the futur dense packing for latter use
volume = (mx[0] - mn[0]) * (mx[1] - mn[1]) * (mx[2] - mn[2])
mean_rad = pow(0.09 * volume / num_spheres, 0.3333)
## define a unique clump type (we could have many, see clumpCloud documentation)
c1 = pack.SpherePack([((-0.2 * mean_rad, 0, 0), 0.5 * mean_rad), ((0.2 * mean_rad, 0, 0), 0.5 * mean_rad)])
## generate positions and input them in the simulation
sp.makeClumpCloud(mn, mx, [c1], periodic=False)
sp.toSimulation(material='spheres')
O.bodies.updateClumpProperties() #get more accurate clump masses/volumes/inertia
else:
sp.makeCloud(mn, mx, -1, 0.3333, num_spheres, False, 0.95, seed=1) #"seed" make the "random" generation always the same
O.bodies.append([sphere(center, rad, material='spheres') for center, rad in sp])
#or alternatively (higher level function doing exactly the same):
#sp.toSimulation(material='spheres')
############################
### DEFINING ENGINES ###
############################
triax = TriaxialStressController(
## TriaxialStressController will be used to control stress and strain. It controls particles size and plates positions.
## this control of boundary conditions was used for instance in http://dx.doi.org/10.1016/j.ijengsci.2008.07.002
maxMultiplier=1. + 2e4 / young, # spheres growing factor (fast growth)
finalMaxMultiplier=1. + 2e3 / young, # spheres growing factor (slow growth)
thickness=0,
## switch stress/strain control using a bitmask. What is a bitmask, huh?!
## Say x=1 if stess is controlled on x, else x=0. Same for for y and z, which are 1 or 0.
## Then an integer uniquely defining the combination of all these tests is: mask = x*1 + y*2 + z*4
## to put it differently, the mask is the integer whose binary representation is xyz, i.e.
## "100" (1) means "x", "110" (3) means "x and y", "111" (7) means "x and y and z", etc.
stressMask=7,
internalCompaction=True, # If true the confining pressure is generated by growing particles
)
newton = NewtonIntegrator(damping=damp)
O.engines = [
ForceResetter(),
InsertionSortCollider([Bo1_Sphere_Aabb(), Bo1_Box_Aabb()]),
InteractionLoop([Ig2_Sphere_Sphere_ScGeom(), Ig2_Box_Sphere_ScGeom()], [Ip2_FrictMat_FrictMat_FrictPhys()], [Law2_ScGeom_FrictPhys_CundallStrack()]),
## We will use the global stiffness of each body to determine an optimal timestep (see https://yade-dem.org/w/images/1/1b/Chareyre&Villard2005_licensed.pdf)
GlobalStiffnessTimeStepper(active=1, timeStepUpdateInterval=100, timestepSafetyCoefficient=0.8),
triax,
TriaxialStateRecorder(iterPeriod=100, file='WallStresses' + table.key),
newton
]
if nRead == 0:
yade.qt.Controller(), yade.qt.View()
## UNCOMMENT THE FOLLOWING SECTIONS ONE BY ONE
## DEPENDING ON YOUR EDITOR, IT COULD BE DONE
## BY SELECTING THE CODE BLOCKS BETWEEN THE SUBTITLES
## AND PRESSING CTRL+SHIFT+D
#######################################
### APPLYING CONFINING PRESSURE ###
#######################################
#the value of (isotropic) confining stress defines the target stress to be applied in all three directions
triax.goal1 = triax.goal2 = triax.goal3 = -10000
#while 1:
#O.run(1000, True)
##the global unbalanced force on dynamic bodies, thus excluding boundaries, which are not at equilibrium
#unb=unbalancedForce()
#print 'unbalanced force:',unb,' mean stress: ',triax.meanStress
#if unb<stabilityThreshold and abs(-10000-triax.meanStress)/10000<0.001:
#break
#O.save('confinedState'+key+'.yade.gz')
#print "### Isotropic state saved ###"
###################################################
### REACHING A SPECIFIED POROSITY PRECISELY ###
###################################################
### We will reach a prescribed value of porosity with the REFD algorithm
### (see http://dx.doi.org/10.2516/ogst/2012032 and
### http://www.geosyntheticssociety.org/Resources/Archive/GI/src/V9I2/GI-V9-N2-Paper1.pdf)
#import sys #this is only for the flush() below
#while triax.porosity>targetPorosity:
## we decrease friction value and apply it to all the bodies and contacts
#compFricDegree = 0.95*compFricDegree
#setContactFriction(radians(compFricDegree))
#print "\r Friction: ",compFricDegree," porosity:",triax.porosity,
#sys.stdout.flush()
## while we run steps, triax will tend to grow particles as the packing
## keeps shrinking as a consequence of decreasing friction. Consequently
## porosity will decrease
#O.run(500,1)
#O.save('compactedState'+key+'.yade.gz')
#print "### Compacted state saved ###"
##############################
### DEVIATORIC LOADING ###
##############################
##We move to deviatoric loading, let us turn internal compaction off to keep particles sizes constant
#triax.internalCompaction=False
## Change contact friction (remember that decreasing it would generate instantaneous instabilities)
#setContactFriction(radians(finalFricDegree))
##set stress control on x and z, we will impose strain rate on y
#triax.stressMask = 5
##now goal2 is the target strain rate
#triax.goal2=rate
## we define the lateral stresses during the test, here the same 10kPa as for the initial confinement.
#triax.goal1=-10000
#triax.goal3=-10000
##we can change damping here. What is the effect in your opinion?
#newton.damping=0.1
##Save temporary state in live memory. This state will be reloaded from the interface with the "reload" button.
#O.saveTmp()
#####################################################
### Example of how to record and plot data ###
#####################################################
#from yade import plot
### a function saving variables
#def history():
#plot.addData(e11=-triax.strain[0], e22=-triax.strain[1], e33=-triax.strain[2],
#ev=-triax.strain[0]-triax.strain[1]-triax.strain[2],
#s11=-triax.stress(triax.wall_right_id)[0],
#s22=-triax.stress(triax.wall_top_id)[1],
#s33=-triax.stress(triax.wall_front_id)[2],
#i=O.iter)
#if 1:
## include a periodic engine calling that function in the simulation loop
#O.engines=O.engines[0:5]+[PyRunner(iterPeriod=20,command='history()',label='recorder')]+O.engines[5:7]
##O.engines.insert(4,PyRunner(iterPeriod=20,command='history()',label='recorder'))
#else:
## With the line above, we are recording some variables twice. We could in fact replace the previous
## TriaxialRecorder
## by our periodic engine. Uncomment the following line:
#O.engines[4]=PyRunner(iterPeriod=20,command='history()',label='recorder')
#O.run(100,True)
### declare what is to plot. "None" is for separating y and y2 axis
#plot.plots={'i':('e11','e22','e33',None,'s11','s22','s33')}
### the traditional triaxial curves would be more like this:
##plot.plots={'e22':('s11','s22','s33',None,'ev')}
## display on the screen (doesn't work on VMware image it seems)
#plot.plot()
##### PLAY THE SIMULATION HERE WITH "PLAY" BUTTON OR WITH THE COMMAND O.run(N) #####
## In that case we can still save the data to a text file at the the end of the simulation, with:
#plot.saveDataTxt('results'+key)
##or even generate a script for gnuplot. Open another terminal and type "gnuplot plotScriptKEY.gnuplot:
#plot.saveGnuplot('plotScript'+key)
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