Module User
@author: Alexandre Sac–Morane alexandre.sac-morane@uclouvain.be
This is the file where the user can change the different parameters for the simulation.
Expand source code
# -*- coding: utf-8 -*-
"""
@author: Alexandre Sac--Morane
alexandre.sac-morane@uclouvain.be
This is the file where the user can change the different parameters for the simulation.
"""
#-------------------------------------------------------------------------------
#Librairy
#-------------------------------------------------------------------------------
import math
import numpy as np
#-------------------------------------------------------------------------------
#User
#-------------------------------------------------------------------------------
def All_parameters():
"""
This function is called in main() to have all the parameters needed in the simulation
Input :
Nothing
Output :
an algorithm dictionnary (a dict)
a geometry dictionnary (a dict)
an initial condition dictionnary (a dict)
a material dictionnary (a dict)
a sample dictionnary (a dict)
a sollicitations dictionnary (a dict)
"""
#---------------------------------------------------------------------------
#Geometric parameters
#approximatively the number of vertices for one grain during DEM simulation
grain_discretisation = 60 # = grain_discretisation_square
N_grain = 300 #total number of grains
frac_dissolved = 0.15 #V_soluble/V_total
#Disk
R_mean = 350 #µm radius to compute the grain distribution. Then recomputed
L_R = [1.2*R_mean,1.1*R_mean,0.9*R_mean,0.8*R_mean] #from larger to smaller
L_percentage_R = [1/6,1/3,1/3,1/6] #distribution of the different radius
#Recompute the mean radius
R_mean = 0
for i in range(len(L_R)):
R_mean = R_mean + L_R[i]*L_percentage_R[i]
#Square
Dimension_mean = 300 #µm radius
L_Dimension = [1.2*Dimension_mean,1.1*Dimension_mean,0.9*Dimension_mean,0.8*Dimension_mean] #from larger to smaller
L_percentage_Dimension = [1/6,1/3,1/3,1/6] #distribution of the different radius
#Recompute the mean dimension
Dimension_mean = 0
for i in range(len(L_Dimension)):
Dimension_mean = Dimension_mean + L_Dimension[i]*L_percentage_Dimension[i]
#Compute number of grain (square or disk)
N_grain_square = int(N_grain*frac_dissolved*math.pi*R_mean**2/(frac_dissolved*math.pi*R_mean**2 + Dimension_mean*Dimension_mean*(1-frac_dissolved)))
N_grain_disk = N_grain - N_grain_square
#write dict
dict_geometry = {
'N_grain_disk' : N_grain_disk,
'R_mean' : R_mean,
'L_R' : L_R,
'L_percentage_R' : L_percentage_R,
'grain_discretisation' : grain_discretisation,
'N_grain_square' : N_grain_square,
'Dimension_mean' : Dimension_mean,
'L_Dimension' : L_Dimension,
'L_percentage_Dimension' : L_percentage_Dimension,
'grain_discretisation_square' : grain_discretisation,
}
#---------------------------------------------------------------------------
#Material parameters
Y = 70*(10**9)*(10**6)*(10**(-12)) #Young Modulus µN/µm2
nu = 0.3 #Poisson's ratio
rho = 2500*10**(-6*3) #density kg/µm3
rho_surf_disk = 4/3*rho*R_mean #kg/µm2
rho_surf_square = rho*Dimension_mean #kg/µm2
mu_friction_gg = 0.5 #grain-grain
mu_friction_gw = 0 #grain-wall
coeff_restitution = 0.2 #1 is perfect elastic
# PF parameters
M_pf = 1 # mobility
kc_pf = 3 #graident coefficient
#write dict
dict_material = {
'Y' : Y,
'nu' : nu,
'rho' : rho,
'rho_surf_disk' : rho_surf_disk,
'rho_surf_square' : rho_surf_square,
'mu_friction_gg' : mu_friction_gg,
'mu_friction_gw' : mu_friction_gw,
'coeff_restitution' : coeff_restitution,
'M_pf' : M_pf,
'kc_pf' : kc_pf
}
#---------------------------------------------------------------------------
#Sample definition
Lenght_mean = (R_mean*N_grain_disk + Dimension_mean/2*N_grain_square)/N_grain #mean characteristic lenght
#Box définition
x_box_min = 0 #µm
x_box_max = 2*Lenght_mean*math.sqrt(N_grain/0.6) #µm 0.6 from Santamarina, 2014 to avoid boundaries effect
y_box_min = 0 #µm
#write dict
dict_sample = {
'x_box_min' : x_box_min,
'x_box_max' : x_box_max,
'y_box_min' : y_box_min
}
#---------------------------------------------------------------------------
#Algorithm parameters
#Phase field
dt_PF = 0.01 #s time step during MOOSE simulation
n_t_PF = 10 #number of iterations PF-DEM
MovePF_selector = 'DeconstructRebuild' #Move PF
n_local = 40 #number of node inside local PF simulation
dx_local = min(2*min(dict_geometry['L_R']),min(dict_geometry['L_Dimension']))/(n_local-1)
dy_local = min(2*min(dict_geometry['L_R']),min(dict_geometry['L_Dimension']))/(n_local-1)
#add into material dict from this data
w = 4*math.sqrt(dx_local**2+dy_local**2)
double_well_height = 10*dict_material['kc_pf']/w/w
dict_material['w'] = w
dict_material['double_well_height'] = double_well_height
#DEM parameters
dt_DEM_crit = math.pi*min(min(L_Dimension)/2,min(L_R))/(0.16*nu+0.88)*math.sqrt(rho*(2+2*nu)/Y) #s critical time step from O'Sullivan 2011
dt_DEM = dt_DEM_crit/8 #s time step during DEM simulation
factor_neighborhood = 1.5 #margin to detect a grain into a neighborhood
i_update_neighborhoods = 500 #the frequency of the update of the neighborhood of the grains and the walls
Spring_type = 'Ponctual' #Kind of contact
#Stop criteria of the DEM
i_DEM_stop = 3000 #maximum iteration for one DEM simulation
Ecin_ratio = 0.0002
n_window_stop = 50
dk0_stop = 0.05
dy_box_max_stop = 0.5
#PF-DEM
n_t_PFDEM = 30 #number of cycle PF-DEM
#Number of processor
np_proc = 4
#Debugging
Debug = True #plot configuration before and after DEM simulation
Debug_DEM = False #plot configuration inside DEM
i_print_plot = 200 #frenquency of the print and plot (if Debug_DEM) in DEM step
clean_memory = True #delete Data, Input, Output at the end of the simulation
SaveData = True #save simulation
main_folder_name = 'Data_Santamarina' #where data are saved
template_simulation_name = 'frac_'+str(int(frac_dissolved*100))+'_run_' #template of the simulation name
#write dict
dict_algorithm = {
'dt_PF' : dt_PF,
'n_t_PF' : n_t_PF,
'dt_DEM_crit' : dt_DEM_crit,
'n_local' : n_local,
'dx_local' : dx_local,
'dy_local' : dy_local,
'dt_DEM' : dt_DEM,
'i_update_neighborhoods': i_update_neighborhoods,
'i_DEM_stop' : i_DEM_stop,
'Ecin_ratio' : Ecin_ratio,
'n_window_stop' : n_window_stop,
'dk0_stop' : dk0_stop,
'dy_box_max_stop' : dy_box_max_stop,
'n_t_PFDEM' : n_t_PFDEM,
'MovePF_selector' : MovePF_selector,
'Spring_type' : Spring_type,
'np_proc' : np_proc,
'Debug' : Debug,
'Debug_DEM' : Debug_DEM,
'SaveData' : SaveData,
'main_folder_name' : main_folder_name,
'template_simulation_name' : template_simulation_name,
'i_print_plot' : i_print_plot,
'factor_neighborhood' : factor_neighborhood,
'clean_memory' : clean_memory
}
#---------------------------------------------------------------------------
#Initial condition parameters
n_generation = 2 #number of grains generation
#/!\ Work only for 2 /!\
factor_ymax_box = 2.5 #margin to generate grains
N_test_max = 5000 # maximum number of tries to generate a grain without overlap
i_DEM_stop_IC = 3000 #stop criteria for DEM during IC
Debug_DEM_IC = False #plot configuration inside DEM during IC
i_print_plot_IC = 200 #frenquency of the print and plot (if Debug_DEM_IC) for IC
dt_DEM_IC = dt_DEM_crit/5 #s time step during IC
Ecin_ratio_IC = 0.0005
factor_neighborhood_IC = 1.5 #margin to detect a grain into a neighborhood
i_update_neighborhoods_gen = 20 #the frequency of the update of the neighborhood of the grains and the walls during IC generations
i_update_neighborhoods_com = 100 #the frequency of the update of the neighborhood of the grains and the walls during IC combination
#write dict
dict_ic = {
'n_generation' : n_generation,
'i_update_neighborhoods_gen': i_update_neighborhoods_gen,
'i_update_neighborhoods_com': i_update_neighborhoods_com,
'factor_ymax_box' : factor_ymax_box,
'i_DEM_stop_IC' : i_DEM_stop_IC,
'Debug_DEM' : Debug_DEM_IC,
'dt_DEM_IC' : dt_DEM_IC,
'Ecin_ratio_IC' : Ecin_ratio_IC,
'i_print_plot_IC' : i_print_plot_IC,
'factor_neighborhood_IC' : factor_neighborhood_IC,
'N_test_max' : N_test_max
}
#---------------------------------------------------------------------------
#External sollicitations
Vertical_Confinement_Linear_Force = Y*2*R_mean/1000 #µN/µm used to compute the Vertical_Confinement_Force
Vertical_Confinement_Force = Vertical_Confinement_Linear_Force*(x_box_max-x_box_min) #µN
gravity = 0 #µm/s2
#Add energy to dissolved grain
Dissolution_Energy = 0.2
#write dict
dict_sollicitations = {
'Dissolution_Energy' : Dissolution_Energy,
'Vertical_Confinement_Force' : Vertical_Confinement_Force,
'gravity' : gravity
}
#---------------------------------------------------------------------------
return dict_algorithm, dict_geometry, dict_ic, dict_material, dict_sample, dict_sollicitations
#-------------------------------------------------------------------------------
def Criteria_StopSimulation(dict_algorithm):
"""
Criteria to stop simulation (PF and DEM).
Input :
an algorithm dictionnary (a dict)
Output :
a Booean (True if the simulation must be stopped)
"""
Criteria_Verified = False
if dict_algorithm['i_PF'] >= dict_algorithm['n_t_PFDEM']:
Criteria_Verified = True
return Criteria_VerifiedFunctions
def All_parameters()-
This function is called in main() to have all the parameters needed in the simulation
Input : Nothing Output : an algorithm dictionnary (a dict) a geometry dictionnary (a dict) an initial condition dictionnary (a dict) a material dictionnary (a dict) a sample dictionnary (a dict) a sollicitations dictionnary (a dict)Expand source code
def All_parameters(): """ This function is called in main() to have all the parameters needed in the simulation Input : Nothing Output : an algorithm dictionnary (a dict) a geometry dictionnary (a dict) an initial condition dictionnary (a dict) a material dictionnary (a dict) a sample dictionnary (a dict) a sollicitations dictionnary (a dict) """ #--------------------------------------------------------------------------- #Geometric parameters #approximatively the number of vertices for one grain during DEM simulation grain_discretisation = 60 # = grain_discretisation_square N_grain = 300 #total number of grains frac_dissolved = 0.15 #V_soluble/V_total #Disk R_mean = 350 #µm radius to compute the grain distribution. Then recomputed L_R = [1.2*R_mean,1.1*R_mean,0.9*R_mean,0.8*R_mean] #from larger to smaller L_percentage_R = [1/6,1/3,1/3,1/6] #distribution of the different radius #Recompute the mean radius R_mean = 0 for i in range(len(L_R)): R_mean = R_mean + L_R[i]*L_percentage_R[i] #Square Dimension_mean = 300 #µm radius L_Dimension = [1.2*Dimension_mean,1.1*Dimension_mean,0.9*Dimension_mean,0.8*Dimension_mean] #from larger to smaller L_percentage_Dimension = [1/6,1/3,1/3,1/6] #distribution of the different radius #Recompute the mean dimension Dimension_mean = 0 for i in range(len(L_Dimension)): Dimension_mean = Dimension_mean + L_Dimension[i]*L_percentage_Dimension[i] #Compute number of grain (square or disk) N_grain_square = int(N_grain*frac_dissolved*math.pi*R_mean**2/(frac_dissolved*math.pi*R_mean**2 + Dimension_mean*Dimension_mean*(1-frac_dissolved))) N_grain_disk = N_grain - N_grain_square #write dict dict_geometry = { 'N_grain_disk' : N_grain_disk, 'R_mean' : R_mean, 'L_R' : L_R, 'L_percentage_R' : L_percentage_R, 'grain_discretisation' : grain_discretisation, 'N_grain_square' : N_grain_square, 'Dimension_mean' : Dimension_mean, 'L_Dimension' : L_Dimension, 'L_percentage_Dimension' : L_percentage_Dimension, 'grain_discretisation_square' : grain_discretisation, } #--------------------------------------------------------------------------- #Material parameters Y = 70*(10**9)*(10**6)*(10**(-12)) #Young Modulus µN/µm2 nu = 0.3 #Poisson's ratio rho = 2500*10**(-6*3) #density kg/µm3 rho_surf_disk = 4/3*rho*R_mean #kg/µm2 rho_surf_square = rho*Dimension_mean #kg/µm2 mu_friction_gg = 0.5 #grain-grain mu_friction_gw = 0 #grain-wall coeff_restitution = 0.2 #1 is perfect elastic # PF parameters M_pf = 1 # mobility kc_pf = 3 #graident coefficient #write dict dict_material = { 'Y' : Y, 'nu' : nu, 'rho' : rho, 'rho_surf_disk' : rho_surf_disk, 'rho_surf_square' : rho_surf_square, 'mu_friction_gg' : mu_friction_gg, 'mu_friction_gw' : mu_friction_gw, 'coeff_restitution' : coeff_restitution, 'M_pf' : M_pf, 'kc_pf' : kc_pf } #--------------------------------------------------------------------------- #Sample definition Lenght_mean = (R_mean*N_grain_disk + Dimension_mean/2*N_grain_square)/N_grain #mean characteristic lenght #Box définition x_box_min = 0 #µm x_box_max = 2*Lenght_mean*math.sqrt(N_grain/0.6) #µm 0.6 from Santamarina, 2014 to avoid boundaries effect y_box_min = 0 #µm #write dict dict_sample = { 'x_box_min' : x_box_min, 'x_box_max' : x_box_max, 'y_box_min' : y_box_min } #--------------------------------------------------------------------------- #Algorithm parameters #Phase field dt_PF = 0.01 #s time step during MOOSE simulation n_t_PF = 10 #number of iterations PF-DEM MovePF_selector = 'DeconstructRebuild' #Move PF n_local = 40 #number of node inside local PF simulation dx_local = min(2*min(dict_geometry['L_R']),min(dict_geometry['L_Dimension']))/(n_local-1) dy_local = min(2*min(dict_geometry['L_R']),min(dict_geometry['L_Dimension']))/(n_local-1) #add into material dict from this data w = 4*math.sqrt(dx_local**2+dy_local**2) double_well_height = 10*dict_material['kc_pf']/w/w dict_material['w'] = w dict_material['double_well_height'] = double_well_height #DEM parameters dt_DEM_crit = math.pi*min(min(L_Dimension)/2,min(L_R))/(0.16*nu+0.88)*math.sqrt(rho*(2+2*nu)/Y) #s critical time step from O'Sullivan 2011 dt_DEM = dt_DEM_crit/8 #s time step during DEM simulation factor_neighborhood = 1.5 #margin to detect a grain into a neighborhood i_update_neighborhoods = 500 #the frequency of the update of the neighborhood of the grains and the walls Spring_type = 'Ponctual' #Kind of contact #Stop criteria of the DEM i_DEM_stop = 3000 #maximum iteration for one DEM simulation Ecin_ratio = 0.0002 n_window_stop = 50 dk0_stop = 0.05 dy_box_max_stop = 0.5 #PF-DEM n_t_PFDEM = 30 #number of cycle PF-DEM #Number of processor np_proc = 4 #Debugging Debug = True #plot configuration before and after DEM simulation Debug_DEM = False #plot configuration inside DEM i_print_plot = 200 #frenquency of the print and plot (if Debug_DEM) in DEM step clean_memory = True #delete Data, Input, Output at the end of the simulation SaveData = True #save simulation main_folder_name = 'Data_Santamarina' #where data are saved template_simulation_name = 'frac_'+str(int(frac_dissolved*100))+'_run_' #template of the simulation name #write dict dict_algorithm = { 'dt_PF' : dt_PF, 'n_t_PF' : n_t_PF, 'dt_DEM_crit' : dt_DEM_crit, 'n_local' : n_local, 'dx_local' : dx_local, 'dy_local' : dy_local, 'dt_DEM' : dt_DEM, 'i_update_neighborhoods': i_update_neighborhoods, 'i_DEM_stop' : i_DEM_stop, 'Ecin_ratio' : Ecin_ratio, 'n_window_stop' : n_window_stop, 'dk0_stop' : dk0_stop, 'dy_box_max_stop' : dy_box_max_stop, 'n_t_PFDEM' : n_t_PFDEM, 'MovePF_selector' : MovePF_selector, 'Spring_type' : Spring_type, 'np_proc' : np_proc, 'Debug' : Debug, 'Debug_DEM' : Debug_DEM, 'SaveData' : SaveData, 'main_folder_name' : main_folder_name, 'template_simulation_name' : template_simulation_name, 'i_print_plot' : i_print_plot, 'factor_neighborhood' : factor_neighborhood, 'clean_memory' : clean_memory } #--------------------------------------------------------------------------- #Initial condition parameters n_generation = 2 #number of grains generation #/!\ Work only for 2 /!\ factor_ymax_box = 2.5 #margin to generate grains N_test_max = 5000 # maximum number of tries to generate a grain without overlap i_DEM_stop_IC = 3000 #stop criteria for DEM during IC Debug_DEM_IC = False #plot configuration inside DEM during IC i_print_plot_IC = 200 #frenquency of the print and plot (if Debug_DEM_IC) for IC dt_DEM_IC = dt_DEM_crit/5 #s time step during IC Ecin_ratio_IC = 0.0005 factor_neighborhood_IC = 1.5 #margin to detect a grain into a neighborhood i_update_neighborhoods_gen = 20 #the frequency of the update of the neighborhood of the grains and the walls during IC generations i_update_neighborhoods_com = 100 #the frequency of the update of the neighborhood of the grains and the walls during IC combination #write dict dict_ic = { 'n_generation' : n_generation, 'i_update_neighborhoods_gen': i_update_neighborhoods_gen, 'i_update_neighborhoods_com': i_update_neighborhoods_com, 'factor_ymax_box' : factor_ymax_box, 'i_DEM_stop_IC' : i_DEM_stop_IC, 'Debug_DEM' : Debug_DEM_IC, 'dt_DEM_IC' : dt_DEM_IC, 'Ecin_ratio_IC' : Ecin_ratio_IC, 'i_print_plot_IC' : i_print_plot_IC, 'factor_neighborhood_IC' : factor_neighborhood_IC, 'N_test_max' : N_test_max } #--------------------------------------------------------------------------- #External sollicitations Vertical_Confinement_Linear_Force = Y*2*R_mean/1000 #µN/µm used to compute the Vertical_Confinement_Force Vertical_Confinement_Force = Vertical_Confinement_Linear_Force*(x_box_max-x_box_min) #µN gravity = 0 #µm/s2 #Add energy to dissolved grain Dissolution_Energy = 0.2 #write dict dict_sollicitations = { 'Dissolution_Energy' : Dissolution_Energy, 'Vertical_Confinement_Force' : Vertical_Confinement_Force, 'gravity' : gravity } #--------------------------------------------------------------------------- return dict_algorithm, dict_geometry, dict_ic, dict_material, dict_sample, dict_sollicitations def Criteria_StopSimulation(dict_algorithm)-
Criteria to stop simulation (PF and DEM).
Input : an algorithm dictionnary (a dict) Output : a Booean (True if the simulation must be stopped)Expand source code
def Criteria_StopSimulation(dict_algorithm): """ Criteria to stop simulation (PF and DEM). Input : an algorithm dictionnary (a dict) Output : a Booean (True if the simulation must be stopped) """ Criteria_Verified = False if dict_algorithm['i_PF'] >= dict_algorithm['n_t_PFDEM']: Criteria_Verified = True return Criteria_Verified