Triaxial Test

# -*- coding: utf-8 -*-

from yade import pack

############################################
###   DEFINING VARIABLES AND MATERIALS   ###
############################################

# The following 5 lines will be used later for batch execution
nRead=utils.readParamsFromTable(
    num_spheres=10000,# number of spheres
    compFricDegree = 0, # contact friction during the confining phase
    unknownOk=True
)
from yade.params import table

num_spheres=table.num_spheres # number of spheres
targetPorosity = 0.382 #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 = 35 # contact friction during the deviatoric loading
rate=0.02 # loading rate (strain rate)
damp=0.2 # damping coefficient
stabilityThreshold=0.01
key='_triax_base_' # simulation's name here
young=2e6 # contact stiffness (initial value) =5e6
mn,mx=Vector3(0,0,0),Vector3(0.01,0.01,0.01) # corners of the initial packing
thick = 0.01 # l'épaisseur des plaques


## créer les matériaux pour les sphères et plaques
O.materials.append(FrictMat(young=young,poisson=0.3,frictionAngle=radians(compFricDegree),density=3000,label='spheres'))
O.materials.append(FrictMat(young=young,poisson=0.5,frictionAngle=0,density=0,label='walls'))

## create walls around the packing
walls=utils.aabbWalls([mn,mx],thickness=thick,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 / on utilisera cette methode dans l'avenir
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')
else:
 sp.makeCloud(mn,mx,-1,0.3333,num_spheres,False, 0.95)
 O.bodies.append([utils.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=ThreeDTriaxialEngine(
    maxMultiplier=1.+2e4/young, # spheres growing factor (fast growth)
    finalMaxMultiplier=1.+2e3/young, # spheres growing factor (slow growth)
    thickness = thick,
    stressControl_1 = False, #switch stress/strain control
    # strainControl_1 = True, temporary switch off because I have no idea what it is about.
    stressControl_2 = False,
    stressControl_3 = False,
    ## The stress used for (isotropic) internal compaction
    sigma_iso = 100000, # Pa
    ## Independant stress values for anisotropic loadings
    sigma1=300000, # to create the q=s11-s33=200kPa=200000
    sigma2=100000,
    sigma3=100000,
    internalCompaction=True, # The confining pressure is generated by growing particles !!!!!!!!!
    Key=key, # passed to the engine so that the output file will have the correct name
)

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()]
    ),
    GlobalStiffnessTimeStepper(active=1,timeStepUpdateInterval=100,timestepSafetyCoefficient=0.8),
    triax,
    TriaxialStateRecorder(iterPeriod=100,file='WallStresses'+key),
    newton
]

#Display spheres with 2 colors for seeing rotations better
Gl1_Sphere.stripes=0
if nRead==0: yade.qt.Controller(), yade.qt.View()

#######################################
###   APPLYING CONFINING PRESSURE   ###
#######################################

while 1:
  O.run(1000, True)
  ##the global unbalanced force on dynamic bodies, thus excluding boundaries, which are not at equilibrium
  unb=unbalancedForce()
  ##average stress
  ##note: triax.stress(k) returns a stress vector, so we need to keep only the normal component
  meanS=(triax.stress(triax.wall_right_id)[0]+triax.stress(triax.wall_top_id)[1]+triax.stress(triax.wall_front_id)[2])/3
  print 'unbalanced force:',unb,' mean stress: ',meanS
  if unb<stabilityThreshold and abs(meanS-triax.sigma_iso)/triax.sigma_iso<0.001:
    break

O.save('confinedState'+key+'.yade.gz')
print "###      Isotropic state saved      ###"

###################################################
###   REACHING A SPECIFIED POROSITY PRECISELY   ###
###################################################

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   ###
##############################

## Deviatoric loading, turn internal compaction off to keep particles sizes constant
triax.internalCompaction=False

## Change contact friction (remember that decreasing it would generate instantaneous instabilities)
triax.setContactProperties(finalFricDegree)

##set independant stress control on each axis
triax.stressControl_1=triax.stressControl_2=triax.stressControl_3=True
## We turn all these flags true, else boundaries will be fixed
triax.wall_bottom_activated=True
triax.wall_top_activated=True
triax.wall_left_activated=True
triax.wall_right_activated=True
triax.wall_back_activated=True
triax.wall_front_activated=True


##If we want a triaxial loading at imposed strain rate, let's assign srain rate instead of stress
triax.stressControl_2=0 #we are tired of typing "True" and "False", we use implicit conversion from integer to boolean
triax.strainRate2=rate
triax.strainRate1=100*rate
triax.strainRate3=100*rate

## Damping (initial value = 0.1)
newton.damping=0.5

##Save temporary state in live memory. This state will be reloaded from the interface with the "reload" button.
O.saveTmp()

###########################
###    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],
            q=triax.stress(triax.wall_right_id)[0]-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)

### plot 1
# plot.plots={'i':('e11','e22','e33',None,'s11','s22','s33')}
### the traditional triaxial curves
# plot.plots={'e11':('q')}
plot.plots={'e11':('ev')}
## display on the screen
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)