triaxial test HIEN

# -*- coding: utf-8 -*-
from yade import pack

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

# Batch execution
nRead=utils.readParamsFromTable(
    num_spheres=10000,# number of spheres len(O.bodies) to verify: 10006 = 10000 particles + 6 walls is correct
    compFricDegree = 24, # contact friction during the confining phase (1)
    unknownOk=True,
    isoForce=80000, # stress for the isotropic compression phase (1)
    conStress=200000 # confinement stress, for the deviatoric loading session (2)
)
from yade.params import table

num_spheres=table.num_spheres           # number of spheres called from table
targetPorosity = 0.382                  #the porosity we want for the packing (3 specimens: (Ei,n) = (1,0.382), (2,0.387), (3,0.409) )
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=0.1                                # loading rate (strain rate)
damp=0.06                               # damping coefficient
stabilityThreshold=0.001                # initial value: 0.001
key='_triax_draine_e618_200_24'             # simulation's name here
young=356e6                             # contact stiffness k_n/Ds
mn,mx=Vector3(-0.25,-0.25,-0.25),Vector3(0.25,0.25,0.25) # corners of the initial packing
thick = 0.01                            # thickness of the plates


## create materials for spheres and plates
O.materials.append(FrictMat(young=young,poisson=0.42,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,oversizeFactor=1.5,material='walls')
wallIds=O.bodies.append(walls)

## use a SpherePack object to generate a random loose particles packing
sp=pack.SpherePack()
psdSizes=[0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.0095] # (sizes or radii of the grains vary from 2mm to 9.5mm)
#psdCumm=[1,9,25,50,69,90,95,100] # the correspondent amount (percentage) of each diameter, uncomment for yade-stable
psdCumm=[0.01,0.09,0.25,0.50,0.69,0.90,0.95,1.00] # for the code not using percentage, e.g. yade-daily

#---------------------------------------------
#psdSizes=[0.002,0.004,0.008,0.0095] # (sizes or radii of the grains vary from 2mm to 9.5mm)
#psdCumm=[1,25,95,100] # the correspondent amount (percentage) of each diameter

#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')
#else:

###################################################
#method 01
#sp.makeCloud(mn,mx,-1,0,num_spheres,False, 0.95,psdSizes,psdCumm,False,seed=1) #"seed" make the "random" generation always the same
sp.makeCloud(mn,mx,0.00575,0.6,num_spheres,False,0.95,seed=1)
#method 02
#sp.particleSD(mn,mx,0.005635,True,'triaxial_test',5000,psdSizes,psdCumm,False,seed=0)
#method 03
#sp.particleSD2(psdSizes,psdCumm,10000,False,cloudPorosity=0.95,0)

# 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=TriaxialStressController(
    maxMultiplier=1.001, # spheres growing factor (fast growth), validated only when internalCompaction = True
    finalMaxMultiplier=1.00001, # spheres growing factor (slow growth), validated only when internalCompaction = True
    thickness = thick,
    ## switch stress/strain control using a bitmask. What is a bitmask, huh?!
    ## Say x=1 if stress 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,
    #the value of confining stress for the intitial (growth) phase
    goal1=table.isoForce,
    goal2=table.isoForce,
    goal3=table.isoForce,
    max_vel=0.5, # validated only when internalCompaction = False m/s
    internalCompaction=True, # If 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()]
    ),
    ## 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=25,timestepSafetyCoefficient=0.8),
    triax,
    TriaxialStateRecorder(iterPeriod=50,file='WallStresses'+key),
    newton
]

O.save('initial'+key+'.xml')
#Display spheres with 2 colors for seeing rotations better
#Gl1_Sphere.stripes=0
#if nRead==0: yade.qt.Controller(), yade.qt.View()
print 'Number of elements: ', len(O.bodies)
print 'Box Volume: ',  triax.boxVolume
#######################################
###   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
  print 'void ratio=',triax.porosity/(1-triax.porosity), 'porosity=', triax.porosity
  print 'mean stress of engine', triax.meanStress
  if unb<stabilityThreshold and abs(meanS-table.isoForce)/table.isoForce<0.001: #0.001
#  if abs(triax.porosity-targetPorosity)<0.001:
    break

O.save('confinedState'+key+'.xml')
print "###      Isotropic state saved      ###"
print 'current porosity=',triax.porosity
print 'current void ratio=',triax.porosity/(1-triax.porosity)

###################################################
###   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+'.xml')
# print "###    Compacted state saved      ###"
# print 'current porosity=',triax.porosity
# print 'current void ratio=',triax.porosity/(1-triax.porosity)
print 'step that starts the deviatoric loading ', O.iter
##############################
###   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))
setContactFriction(radians(35))

#set stress control on x and z, we will impose strain rate on y (5)
triax.stressMask = 5
#now goal2 is the target strain rate
triax.goal2=-rate
#we assign constant stress to the other directions
triax.goal1=table.conStress
triax.goal3=table.conStress

##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()


while triax.strain[1] < 0.25:
  O.run(50); O.wait()
########################
## PLOT THE PSD CURVE ##
########################

#import matplotlib; matplotlib.rc('axes',grid=True)
#from yade import pack
#import pylab
#pylab.plot(psdSizes,psdCumm,label='precribed mass PSD')
#sp0=pack.SpherePack();
#sp0.makeCloud(mn,mx,psdSizes=psdSizes,psdCumm=psdCumm,distributeMass=True)
#sp1=pack.SpherePack();
#sp1.makeCloud(mn,mx,psdSizes=psdSizes,psdCumm=psdCumm,distributeMass=True,num=10000)
#sp2=pack.SpherePack();
#sp2.makeCloud(mn,mx,psdSizes=psdSizes,psdCumm=psdCumm,distributeMass=True,num=20000)
#pylab.semilogx(*sp0.psd(bins=30,mass=True),label='Mass PSD of (free) %d random spheres'%len(sp0))
#pylab.semilogx(*sp1.psd(bins=30,mass=True),label='Mass PSD of (imposed) %d random spheres'%len(sp1))
#pylab.semilogx(*sp2.psd(bins=30,mass=True),label='Mass PSD of (imposed) %d random spheres (scaled down)'%len(sp2))

#pylab.legend()

## uniform distribution of size (sp3) and of mass (sp4)
#sp3=pack.SpherePack(); sp3.makeCloud(mn,mx,rMean=0.005635,rRelFuzz=0,distributeMass=False);
#sp4=pack.SpherePack(); sp4.makeCloud(mn,mx,rMean=0.005635,rRelFuzz=0,distributeMass=True);
#pylab.figure()
#pylab.plot(*(sp3.psd(mass=True)+('g',)+sp4.psd(mass=True)+('r',)))
#pylab.legend(['Mass PSD of size-uniform distribution','Mass PSD of mass-uniform distribution'])

#pylab.figure()
#pylab.plot(*(sp3.psd(mass=False)+('g',)+sp4.psd(mass=False)+('r',)))
#pylab.legend(['Size PSD of size-uniform distribution','Size PSD of mass-uniform distribution'])
#pylab.show()

#pylab.show()

#####################################################
###    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],
            p=(triax.stress(triax.wall_right_id)[0]+triax.stress(triax.wall_top_id)[1]+triax.stress(triax.wall_front_id)[2])/3000,
            q=(triax.stress(triax.wall_top_id)[1]-triax.stress(triax.wall_front_id)[2])/1000,
            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':('q')}

## 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)