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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