Question #697846 on Yade changed:
https://answers.launchpad.net/yade/+question/697846
Shyam Kasi posted a new comment:
Thanks for your suggestions.
When I used distributeMass I was getting an error message saying the script is
not responding.
I tried changing the particle size distribution but none of those works.
Here below I am presenting my code.
from yade import pack
############################################
### DEFINING VARIABLES AND MATERIALS ###
############################################
# The following 5 lines will be used later for batch execution
nRead=readParamsFromTable(
num_spheres=5e6,# number of spheres
compFricDegree =43.22, # 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 =0.199 #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 =43.22 # contact friction during the deviatoric loading
rate=-0.01 # loading rate (strain rate)
damp=0.4 # damping coefficient
stabilityThreshold=0.01 # we test unbalancedForce against this value in
different loops (see below)
young=58e6 # contact stiffness
mn,mx=Vector3(0,0,0),Vector3(0.005,0.005,0.005) # corners of the initial packing
## create materials for spheres and plates
O.materials.append(FrictMat(young=young,poisson=0.3,frictionAngle=radians(compFricDegree),density=2655.6,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,psdSizes=[0,0.000075,0.00015,0.000212,0.000425,0.0006],psdCumm=[0,3.88e-3,403.88e-3,0.7184,0.95554,1],porosity=0.2,distributeMass=True)
#"seed" make the "random" generation always #the same
#sp.makeCloud(mn,mx,psdSizes=[0,0.000075,0.00015,0.000212,0.000425,0.0006],psdCumm=[0,0.1,0.3,0.62,0.92554,1],porosity=0.2)
#sp.makeCloud(mn,mx,rMean=0.00333/2,rRelFuzz=0,num=2000,porosity=0.2)
#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=False,
max_vel=0.001 # If true the confining pressure is generated by growing
particles
)
newton=NewtonIntegrator(gravity=(0,-9.81,0),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=-50000
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(-50000-triax.meanStress)/50000<0.01:
break
O.save('confinedState'+key+'.yade.gz')
print "### Isotropic state saved ###"
O.pause()
###################################################
### 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 ###"
O.pause()
##############################
### 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=-50000
triax.goal3=-50000
##we can change damping here. What is the effect in your opinion?
newton.damping=0.4
##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)
O.engines=O.engines[0:5]+[PyRunner(iterPeriod=2000,command='history()',label='recorder')]+O.engines[5:7]
#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=2000,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':'s22'}
## 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)
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