confining pressure is not stable

Hi All,

I'm trying to use the Hertz constitutive model to simulate the triaxial tests.
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I use the code from the example. [1]
Here is the code:
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from yade import pack

############################################
### DEFINING VARIABLES AND MATERIALS ###
############################################
# The following 5 lines will be used later for batch execution
nRead=readParamsFromTable(
 num_spheres=20000,# 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 = 0.368 #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.+9e4/young, # spheres growing factor (fast growth)
 finalMaxMultiplier=1.+2e3/young, # spheres growing factor (slow growth)
 thickness = 0,
 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()]
  [Ip2_FrictMat_FrictMat_MindlinPhys( betan =0.2 , betas = 0.2, krot = 0.2 , eta = 0.5 )],
  [Law2_ScGeom_MindlinPhys_Mindlin()]
 ),
 GlobalStiffnessTimeStepper(active=1,timeStepUpdateInterval=100,timestepSafetyCoefficient=0.8),
 triax,
 TriaxialStateRecorder(iterPeriod=100,file='WallStresses'+table.key),
 newton
]

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

#the value of (isotropic) confining stress defines the target stress to be applied in all three directions
triax.goal1=triax.goal2=triax.goal3=-100000

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(-100000-triax.meanStress)/100000<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 ###
##############################

##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=-100000
triax.goal3=-100000

##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)
 plot.saveDataTxt('calibration-hz.txt')

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:
  O.engines[4]=PyRunner(iterPeriod=20,command='history()',label='recorder')

O.run(100000,True)

#plot.plots={'e22':('s11','s22','s33',None,'ev')}

## display on the screen (doesn't work on VMware image it seems)
#plot.plot()
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I used these two models: [Law2_ScGeom_FrictPhys_CundallStrack()] and [Law2_ScGeom_MindlinPhys_Mindlin()]
these two models run without errors.
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For both models, I set the confining pressure to 100kPa.
As for the Cundall model, the confining pressure can keep constant. However, for the hertz model, the confining pressures in the x and z direction are not the same. you can find the results here [2].
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All the code is the same except for the constitutive model. I want to know why the confining pressure is not constant.

best,
Yong

References: [1] https://gitlab.com/yade-dev/trunk/-/blob/master/examples/triax-tutorial/script-session1.py
[2] https://www.dropbox.com/sh/twdl0kfx6bfo8mt/AABamjTzasooR7rum0KXc0b4a?dl=0