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[ehsan benabbas <question688060@xxxxxxxxxxxxxxxxxxxxx>]

Question #688060 on Yade changed:
https://answers.launchpad.net/yade/+question/688060

    Status: Needs information => Open

ehsan benabbas gave more information on the question:
Hi Jan, Thanks for your helps

This is the code:

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
]

#Display spheres with 2 colors for seeing rotations better
Gl1_Sphere.stripes=0
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)

data = []
for i in O.interactions:
   fn = i.phys.normalForce
   fs = i.phys.shearForce
   cp = i.geom.contactPoint
   normal = i.geom.normal
   b1,b2 = [O.bodies[id] for id in (i.id1,i.id2)]
   p1,p2 = [b.state.pos for b in (b1,b2)]
   branch = p2 - p1
   cp,normal,branch,fn,fs = [tuple(v) for v in (cp,normal,branch,fn,fs)] # Vector3 -> tuple
   d = dict(cp=cp,normal=normal,branch=branch,fn=fn,fs=fs)
# new data contains the information, you can save it e.g. as JSON
import json
with open("interactions.json","w") as f:
   json.dump(data,f)

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