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[Shyam Kasi <question697846@xxxxxxxxxxxxxxxxxxxxx>]

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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