# -*- encoding=utf-8 -*- #************************************************************************* # Copyright (C) 2010 by Bruno Chareyre * # bruno.chareyre_at_grenoble-inp.fr * # * # This program is free software; it is licensed under the terms of the * # GNU General Public License v2 or later. See file LICENSE for details. * #*************************************************************************/ ## This script details the simulation of a triaxial test on sphere packings using Yade ## See the associated pdf file for detailed exercises ## the algorithms presented here have been used in published papers, namely: ## * Chareyre et al. 2002 (http://www.geosyntheticssociety.org/Resources/Archive/GI/src/V9I2/GI-V9-N2-Paper1.pdf) ## * Chareyre and Villard 2005 (https://yade-dem.org/w/images/1/1b/Chareyre&Villard2005_licensed.pdf) ## * Scholtès et al. 2009 (http://dx.doi.org/10.1016/j.ijengsci.2008.07.002) ## * Tong et al.2012 (http://dx.doi.org/10.2516/ogst/2012032) ## ## Most of the ideas were actually developped during my PhD. ## If you want to know more on micro-macro relations evaluated by triaxial simulations ## AND if you can read some french, it is here: http://tel.archives-ouvertes.fr/docs/00/48/68/07/PDF/Thesis.pdf 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 ] 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 unbtargetPorosity: ## 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)