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
from pathlib import Path
#Own functions and classes
import Grain
#-------------------------------------------------------------------------------
#User
#-------------------------------------------------------------------------------
def All_parameters():
'''
This function is called in main.py to have all the parameters needed in the simulation.
Input :
Nothing
Output :
an algorithm dictionnary (a dictionnary)
a material dictionnary (a dictionnary)
a sample dictionnary (a dictionnary)
a sollicitation dictionnary (a dictionnary)
'''
#---------------------------------------------------------------------------
#Sample parameters
#spatial discretisation
x_min = -230
x_max = 230
nx = 180
x_L = np.linspace(x_min,x_max,nx)
y_min = -130
y_max = 130
ny = 100
y_L = np.linspace(y_min,y_max,ny)
#approximatively the number of vertices for one grain during DEM simulation
grain_discretisation = 20
dict_sample = {
'x_L' : x_L,
'y_L' : y_L,
'grain_discretisation' : grain_discretisation,
'Emec_M' : np.array(np.zeros((len(y_L),len(x_L)))),
'Eche_M' : np.array(np.zeros((len(y_L),len(x_L)))),
'Ed_M' : np.array(np.zeros((len(y_L),len(x_L))))
}
#---------------------------------------------------------------------------
#Algorithm parameters
np_proc = 4 #number of processor used
n_t_PFDEM = 200 #number of cycle PF-DEM
#Time step for phase field
n_t_PF = 8
dt_PF_init = 0.3
dt_PF_level1 = dt_PF_init/2
dt_PF_level2 = dt_PF_level1/2
dt_PF_level3 = dt_PF_level2/2
#criteria to switch level
Ed_level1 = 10
Ed_level2 = 20
Ed_level3 = 25
#Visual parameters
c_min = 0
c_max = 0.08
#Margin for sphericity study
sphericity_margin = 0.05
#Discretisation to find the inscribing (number of nodes in one direction)
n_spatial_inscribing = 100
#List of plot to do
# Config, C_at_P, Diff_Solute, dt, Ed, Eta_c, Init_Current_Shape, Kc, Movie (need Config to work), Sint_MinEtai, Sphericity, sum_Ed
L_flag_plot = ['Config', 'C_at_P', 'Diff_Solute', 'dt', 'Sphericity', 'Kc', 'Movie', 'sum_Ed']
#Find a simulation ame
template = 'PS_Long_Run' #template of the name of the simulation
SaveData = False #Save data or not
foldername = 'Data_2G_ACS' #name of the folder where data are saved
if SaveData :
i_run = 1
folderpath = Path('../'+foldername+'/'+template+'_'+str(i_run))
while folderpath.exists():
i_run = i_run + 1
folderpath = Path('../'+foldername+'/'+template+'_'+str(i_run))
namefile = template+'_'+str(i_run)
else :
namefile = template
dict_algorithm = {
'c_min' : c_min,
'c_max' : c_max,
'sphericity_margin' : sphericity_margin,
'n_spatial_inscribing' : n_spatial_inscribing,
'np_proc' : np_proc,
'SaveData' : SaveData,
'namefile' : namefile,
'dt_PF' : dt_PF_init,
'dt_PF_init' : dt_PF_init,
'dt_PF_level1' : dt_PF_level1,
'dt_PF_level2' : dt_PF_level2,
'dt_PF_level3' : dt_PF_level3,
'Ed_level1' : Ed_level1,
'Ed_level2' : Ed_level2,
'Ed_level3' : Ed_level3,
'n_t_PF' : n_t_PF,
'foldername' : foldername,
'n_t_PFDEM' : n_t_PFDEM,
'L_flag_plot' : L_flag_plot
}
#---------------------------------------------------------------------------
#External sollicitation parameters
overlap_target = 10 #overlap verified before each phase field iteration
chi = 0.5 #coefficient applied to the chemical energy
dict_sollicitation = {
'overlap_target' : overlap_target,
'chi' : chi
}
#---------------------------------------------------------------------------
#Material parameters
#phase field
width_int = math.sqrt((x_L[4]-x_L[0])**2+(y_L[4]-y_L[0])**2)
Mobility = 1 #L1, L2 in .i
#Diffusion of etai
kappa_eta = 0.01
#Energy barrier of etai
Energy_barrier = 20*kappa_eta/(width_int)**2
#Diffusion of c, the solute generated by the dissolution
kappa_c = 50
method_to_compute_kc = 'dilation' #dilation or interpolation
#Definition of the evolution of the penalty term inside a grain (for interpolation)
tau_kappa_c = 5 #kc = kc0 e(-(R_i-d)/R_i/tau_kappa_c)
#Define the spring value
Y = 70*10**9*10**(-6*2)
nu = 0.3
dict_material = {
'w' : width_int,
'M' : Mobility,
'kappa_eta' : kappa_eta,
'kappa_c' : kappa_c,
'tau_kappa_c' : tau_kappa_c,
'method_to_compute_kc' : method_to_compute_kc,
'Energy_barrier' : Energy_barrier,
'Y' : Y,
'nu' : nu
}
#---------------------------------------------------------------------------
return dict_algorithm, dict_material, dict_sample, dict_sollicitation
#-------------------------------------------------------------------------------
def Add_2grains(dict_material,dict_sample):
'''
Generate two grains in the sample.
Input :
a material dictionnary (a dictionnary)
a sample dictionnary (a dictionnary)
Output :
Nothing but the sample dictionnary gets a new grains configuration (a list)
'''
#grain 1
radius = 100
center = np.array([np.mean(dict_sample['x_L'])-radius,np.mean(dict_sample['y_L'])])
grain_1 = Grain.Grain(1,radius,center,dict_material,dict_sample)
#grain 2
radius = 100
center = np.array([np.mean(dict_sample['x_L'])+radius,np.mean(dict_sample['y_L'])])
grain_2 = Grain.Grain(2,radius,center,dict_material,dict_sample)
#add element in dict
dict_sample['L_g'] = [grain_1, grain_2]
#-------------------------------------------------------------------------------
def Add_S0(dict_sample, dict_sollicitation):
'''
Compute the initial intersection surface between two disk particles.
See https://calculis.net/q/aire-intersection-disques-35
Input :
a sample dictionnary (a dictionnary)
Output :
Nothing but the sample dictionnary gets a new entry (a float)
'''
#notation from the forum
R = dict_sample['L_g'][0].r_mean
R_prime = dict_sample['L_g'][1].r_mean
D = R + R_prime - dict_sollicitation['overlap_target']
d = (R**2 + D**2 - R_prime**2)/2/D
d_prime = D - d
#Part from particle 1
S1 = R**2*math.acos(d/R)-d*math.sqrt(R**2-d**2)
#Part from particle 2
S2 = R_prime**2*math.acos(d_prime/R_prime)-d_prime*math.sqrt(R_prime**2-d_prime**2)
#add element in dictionnary
dict_sample['S_int_0'] = S1 + S2
#-------------------------------------------------------------------------------
def Add_solute(dict_sample):
'''
Generate solute in the sample.
Input :
a sample dictionnary (a dictionnary)
Output :
Nothing but the sample dictionnary gets a new solute configuration (a n_y x n_x numpy array)
'''
#solute
solute_M = np.zeros((len(dict_sample['y_L']),len(dict_sample['x_L'])))
#add element in dict
dict_sample['solute_M'] = solute_M
#-------------------------------------------------------------------------------
def Add_alpha_emec(dict_sample, dict_sollicitation):
'''
Compute the coefficient alpha applied to the mechanical energy.
Input :
a sample dictionnary (a dictionnary)
a sollicitation dictionnary (a dictionnary)
Output :
Nothing but the sollicitation dictionnary gets a new attribut (a float)
'''
alpha = 0.2*dict_sample['S_int_0']
#update dict
dict_sollicitation['alpha'] = alpha
#-------------------------------------------------------------------------------
def Criteria_StopSimulation(dict_algorithm):
'''
Define a stop criteria for the PFDEM simulation.
The simulation stops if the number of iterations reaches a maximum value.
Input :
an algorithm dictionnary (a dictionnary)
Output :
The result depends on the fact if the stop criteria is reached or not (a bool)
'''
Criteria_Verified = False
if dict_algorithm['i_PFDEM'] >= dict_algorithm['n_t_PFDEM']:
Criteria_Verified = True
return Criteria_VerifiedFunctions
def Add_2grains(dict_material, dict_sample)-
Generate two grains in the sample.
Input : a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new grains configuration (a list)Expand source code
def Add_2grains(dict_material,dict_sample): ''' Generate two grains in the sample. Input : a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new grains configuration (a list) ''' #grain 1 radius = 100 center = np.array([np.mean(dict_sample['x_L'])-radius,np.mean(dict_sample['y_L'])]) grain_1 = Grain.Grain(1,radius,center,dict_material,dict_sample) #grain 2 radius = 100 center = np.array([np.mean(dict_sample['x_L'])+radius,np.mean(dict_sample['y_L'])]) grain_2 = Grain.Grain(2,radius,center,dict_material,dict_sample) #add element in dict dict_sample['L_g'] = [grain_1, grain_2] def Add_S0(dict_sample, dict_sollicitation)-
Compute the initial intersection surface between two disk particles. See https://calculis.net/q/aire-intersection-disques-35
Input : a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new entry (a float)Expand source code
def Add_S0(dict_sample, dict_sollicitation): ''' Compute the initial intersection surface between two disk particles. See https://calculis.net/q/aire-intersection-disques-35 Input : a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new entry (a float) ''' #notation from the forum R = dict_sample['L_g'][0].r_mean R_prime = dict_sample['L_g'][1].r_mean D = R + R_prime - dict_sollicitation['overlap_target'] d = (R**2 + D**2 - R_prime**2)/2/D d_prime = D - d #Part from particle 1 S1 = R**2*math.acos(d/R)-d*math.sqrt(R**2-d**2) #Part from particle 2 S2 = R_prime**2*math.acos(d_prime/R_prime)-d_prime*math.sqrt(R_prime**2-d_prime**2) #add element in dictionnary dict_sample['S_int_0'] = S1 + S2 def Add_alpha_emec(dict_sample, dict_sollicitation)-
Compute the coefficient alpha applied to the mechanical energy.
Input : a sample dictionnary (a dictionnary) a sollicitation dictionnary (a dictionnary) Output : Nothing but the sollicitation dictionnary gets a new attribut (a float)Expand source code
def Add_alpha_emec(dict_sample, dict_sollicitation): ''' Compute the coefficient alpha applied to the mechanical energy. Input : a sample dictionnary (a dictionnary) a sollicitation dictionnary (a dictionnary) Output : Nothing but the sollicitation dictionnary gets a new attribut (a float) ''' alpha = 0.2*dict_sample['S_int_0'] #update dict dict_sollicitation['alpha'] = alpha def Add_solute(dict_sample)-
Generate solute in the sample.
Input : a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new solute configuration (a n_y x n_x numpy array)Expand source code
def Add_solute(dict_sample): ''' Generate solute in the sample. Input : a sample dictionnary (a dictionnary) Output : Nothing but the sample dictionnary gets a new solute configuration (a n_y x n_x numpy array) ''' #solute solute_M = np.zeros((len(dict_sample['y_L']),len(dict_sample['x_L']))) #add element in dict dict_sample['solute_M'] = solute_M def All_parameters()-
This function is called in main.py to have all the parameters needed in the simulation.
Input : Nothing Output : an algorithm dictionnary (a dictionnary) a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) a sollicitation dictionnary (a dictionnary)Expand source code
def All_parameters(): ''' This function is called in main.py to have all the parameters needed in the simulation. Input : Nothing Output : an algorithm dictionnary (a dictionnary) a material dictionnary (a dictionnary) a sample dictionnary (a dictionnary) a sollicitation dictionnary (a dictionnary) ''' #--------------------------------------------------------------------------- #Sample parameters #spatial discretisation x_min = -230 x_max = 230 nx = 180 x_L = np.linspace(x_min,x_max,nx) y_min = -130 y_max = 130 ny = 100 y_L = np.linspace(y_min,y_max,ny) #approximatively the number of vertices for one grain during DEM simulation grain_discretisation = 20 dict_sample = { 'x_L' : x_L, 'y_L' : y_L, 'grain_discretisation' : grain_discretisation, 'Emec_M' : np.array(np.zeros((len(y_L),len(x_L)))), 'Eche_M' : np.array(np.zeros((len(y_L),len(x_L)))), 'Ed_M' : np.array(np.zeros((len(y_L),len(x_L)))) } #--------------------------------------------------------------------------- #Algorithm parameters np_proc = 4 #number of processor used n_t_PFDEM = 200 #number of cycle PF-DEM #Time step for phase field n_t_PF = 8 dt_PF_init = 0.3 dt_PF_level1 = dt_PF_init/2 dt_PF_level2 = dt_PF_level1/2 dt_PF_level3 = dt_PF_level2/2 #criteria to switch level Ed_level1 = 10 Ed_level2 = 20 Ed_level3 = 25 #Visual parameters c_min = 0 c_max = 0.08 #Margin for sphericity study sphericity_margin = 0.05 #Discretisation to find the inscribing (number of nodes in one direction) n_spatial_inscribing = 100 #List of plot to do # Config, C_at_P, Diff_Solute, dt, Ed, Eta_c, Init_Current_Shape, Kc, Movie (need Config to work), Sint_MinEtai, Sphericity, sum_Ed L_flag_plot = ['Config', 'C_at_P', 'Diff_Solute', 'dt', 'Sphericity', 'Kc', 'Movie', 'sum_Ed'] #Find a simulation ame template = 'PS_Long_Run' #template of the name of the simulation SaveData = False #Save data or not foldername = 'Data_2G_ACS' #name of the folder where data are saved if SaveData : i_run = 1 folderpath = Path('../'+foldername+'/'+template+'_'+str(i_run)) while folderpath.exists(): i_run = i_run + 1 folderpath = Path('../'+foldername+'/'+template+'_'+str(i_run)) namefile = template+'_'+str(i_run) else : namefile = template dict_algorithm = { 'c_min' : c_min, 'c_max' : c_max, 'sphericity_margin' : sphericity_margin, 'n_spatial_inscribing' : n_spatial_inscribing, 'np_proc' : np_proc, 'SaveData' : SaveData, 'namefile' : namefile, 'dt_PF' : dt_PF_init, 'dt_PF_init' : dt_PF_init, 'dt_PF_level1' : dt_PF_level1, 'dt_PF_level2' : dt_PF_level2, 'dt_PF_level3' : dt_PF_level3, 'Ed_level1' : Ed_level1, 'Ed_level2' : Ed_level2, 'Ed_level3' : Ed_level3, 'n_t_PF' : n_t_PF, 'foldername' : foldername, 'n_t_PFDEM' : n_t_PFDEM, 'L_flag_plot' : L_flag_plot } #--------------------------------------------------------------------------- #External sollicitation parameters overlap_target = 10 #overlap verified before each phase field iteration chi = 0.5 #coefficient applied to the chemical energy dict_sollicitation = { 'overlap_target' : overlap_target, 'chi' : chi } #--------------------------------------------------------------------------- #Material parameters #phase field width_int = math.sqrt((x_L[4]-x_L[0])**2+(y_L[4]-y_L[0])**2) Mobility = 1 #L1, L2 in .i #Diffusion of etai kappa_eta = 0.01 #Energy barrier of etai Energy_barrier = 20*kappa_eta/(width_int)**2 #Diffusion of c, the solute generated by the dissolution kappa_c = 50 method_to_compute_kc = 'dilation' #dilation or interpolation #Definition of the evolution of the penalty term inside a grain (for interpolation) tau_kappa_c = 5 #kc = kc0 e(-(R_i-d)/R_i/tau_kappa_c) #Define the spring value Y = 70*10**9*10**(-6*2) nu = 0.3 dict_material = { 'w' : width_int, 'M' : Mobility, 'kappa_eta' : kappa_eta, 'kappa_c' : kappa_c, 'tau_kappa_c' : tau_kappa_c, 'method_to_compute_kc' : method_to_compute_kc, 'Energy_barrier' : Energy_barrier, 'Y' : Y, 'nu' : nu } #--------------------------------------------------------------------------- return dict_algorithm, dict_material, dict_sample, dict_sollicitation def Criteria_StopSimulation(dict_algorithm)-
Define a stop criteria for the PFDEM simulation.
The simulation stops if the number of iterations reaches a maximum value.
Input : an algorithm dictionnary (a dictionnary) Output : The result depends on the fact if the stop criteria is reached or not (a bool)Expand source code
def Criteria_StopSimulation(dict_algorithm): ''' Define a stop criteria for the PFDEM simulation. The simulation stops if the number of iterations reaches a maximum value. Input : an algorithm dictionnary (a dictionnary) Output : The result depends on the fact if the stop criteria is reached or not (a bool) ''' Criteria_Verified = False if dict_algorithm['i_PFDEM'] >= dict_algorithm['n_t_PFDEM']: Criteria_Verified = True return Criteria_Verified