#pointtoplot = 5
#if list_of_species_toplot !=[]:
#module.setInteractivePlot(" module ",list_of_species_toplot,pointtoplot,plotfrequency=30)
#module.setInteractiveSpatialPlot([ExpectedOutput('Concentration','Cl',format='table',unit='molal', name='Cl')])
module.run()
outTr = module.getOutput("Tr")
coordinates = outTr[-1][1].getColumn(0)
#print "coordinates", outTr[-1][1].getColumn(0)
#print "values", outTr[-1][1].getColumn(-1)
numericalSolution = outTr[-1][1].getColumn(-1)
analFunction = AnalyticalFunction1D_Tr(outTr[-1][1].getColumn(0)[0:100], C0, 0.0, finalTime, darcy, De + alpha*darcy)
analyticalSolution = analFunction.eval()
#print analyticalSolution
epsilonT = 3.E-2
normTr = normMaxListComparison(numericalSolution[1:15],analyticalSolution[1:15])
#for i in range(1,20):
# print i,numericalSolution[i],analyticalSolution[i]
#print " norm max ",normTr
#print "~~~~~~~~~~~~~~~"
#print " Calcite outputs~"
#print "~~~~~~~~~~~~~~~~"
#outCalcite = module.getOutput("Calcite")[-1][1].getColumn(-1)
#
#for ind in range (1,20):
# print coordinates[ind],outCalcite[ind]
#
ok = 1
if normTr < epsilonT:
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print "~ End of the stable sample case ~"
#
# Simulation
#
test.run()
#
effectiveYoungModulus = test.usE
previousEffectiveYoungModulus = [
1.0436943432360708e-06, 1.0378803775150806e-06, 1.032145715051025e-06,
1.026443038371557e-06, 1.020814421590689e-06, 1.0152107651586925e-06,
1.009658274134666e-06, 1.0041710184809715e-06, 9.98735799030556e-07,
9.933460069422324e-07
]
#previousEffectiveYoungModulus = [1.9151575673985819e-10, 1.8438484993684937e-10, 1.7751043780249623e-10, 1.7086673198657501e-10, 1.6445544059962237e-10, 1.5828989514689877e-10, 1.5232182513198601e-10, 1.4657358054062875e-10, 1.4102192195982855e-10, 1.356688773554728e-10]
#[1.9481265572812479e-09, 1.8737673256049104e-09, 1.8039067622339525e-09, 1.7363927593889167e-09, 1.6712422327984073e-09, 1.608588521815435e-09, 1.5479406983083178e-09, 1.4895254491923164e-09, 1.4331087812704512e-09, 1.3787097254768541e-09]
norm = normMaxListComparison(
previousEffectiveYoungModulus,
effectiveYoungModulus[0:len(previousEffectiveYoungModulus)])
effectiveYoungModulus[0:len(previousEffectiveYoungModulus)]
print "effectiveYoungModulus ", effectiveYoungModulus[0:len(
previousEffectiveYoungModulus)]
#
print " norm ", norm
error = 5.e-5
if norm > error:
print "Be careful: error, the Young modulus isn\'t stable"
mess = " Problem for the Young modulus evaluation test"
raise Warning, " Problem for the Young modulus evaluation test"
pass
else:
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print "Good agreement, the result for the evaluation of the effective Young modulus are stable"
R = 1. #Retardation factor for the tracer
longDis = 6.67e-4
AnalT = []
from math import exp
den = 1./(2.*sqrt(longDis*darcy*time/(porosity*R)))
C1 = darcy*time/(porosity*R)
x = 0.
dx = 0.0008
for dx in dxListe:
x+=dx
value = erfc((x-C1)*den)
value1 = exp(x/longDis)*erfc((x+C1)*den)
value = 0.5*C0*(value+value1)
AnalT.append(value)
tracerNorm = normMaxListComparison(TY.getColumn(2)[0:20],AnalT[0:20])
print 'tracer norm = ',tracerNorm
#
# R = 1 + Kd*2500*(1-porosity)/porosity (pho = 1 here)
#
R = 1. + 2 #Retardation factor
longDis = 6.67e-4
AnalK = []
from math import exp
den = 1./(2.*sqrt(longDis*darcy*time/(porosity*R)))
C1 = darcy*time/(porosity*R)
x = 0.0002
dx = 0.0004
for dx in dxListe:
value = erfc((x-C1)*den)
f_name = "CsdeX_ana.tab"
table_Cs_analytic.readFromFile(f_name,nbcolumns = 2,columnsNames = 'no',name = 'no')
Cs_analytic = table_Cs_analytic.getColumn(1)
print 'Cs_analytic',Cs_analytic[0:50]
Cs_numeric = table_Cs_numeric.getColumn(2)
print 'Cs_numeric',Cs_numeric[0:50]
list1 = []
print 'len(Cs_numeric)',len(Cs_numeric)
print 'len(Cs_analytic)',len(Cs_analytic)
for i in range(len(Cs_analytic)):
list1.append(Cs_analytic[i])
Cs_analytic = list1[0:50]
Cs_numeric = Cs_numeric[0:50]
CsNorm = normMaxListComparison(Cs_numeric,Cs_analytic)
print 'Cs norm = ',CsNorm
epsilon_Cs = 10.E-2
if CsNorm > epsilon_Cs:
OKConc = 0
message = "Error on the Cs concentration higher than 10% and equal to: "+str(CsNorm)
raise Warning, message
else :
OKConc = 1
print "Good agreement between numerical and analytical results for the Cs concentration, error equal to ", CsNorm
print ' '
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print " End of the Cesium case ~"
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
for i in range(1,30):
dx.append(dx[0]+dx[i-1])
analFunction = analyticalFunction(finalTime, 0.02, dx, 3.e-10)
print '================================================= '
print ' comparison to the analytic solution'
print '================================================= '
table_Na_numeric = NadeX
list_Na_analytic = analFunction.evalNa()
list_Na_numeric = table_Na_numeric.getColumn(2)
print " ana: ",list_Na_analytic[0:20]
print " num: ",list_Na_numeric[0:20]
norme_Na = normMaxListComparison(list_Na_numeric[0:30],list_Na_analytic[0:30])
print 'norme_Na = ',norme_Na
epsilon_Na = 10.E-2
if norme_Na > epsilon_Na:
OKConc = 0
print 'Be carefull: error on the Na concentration higher than 10% and equal to ',norme_Na*100,"%"
else :
OKConc = 1
print 'Good agreement between numerical and analytical results for the Na concentration, error equal to ',norme_Na*100,"%"
print ' '
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Comparison to analytical pH
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
table_pH_numeric = pHdeX
for i in range(1,30):
dx.append(dx[0]+dx[i-1])
analFunction = analyticalFunction(finalTime, 0.02, dx, 3.e-10)
table_Na_numeric = NadeX
list_Na_numeric = table_Na_numeric.getColumn(2)
# on tronque la liste de la solution analytique si elle est trop longue
list_Na_analytic = analFunction.evalNa()
print " ana: ",list_Na_analytic[0:20]
print " num: ",list_Na_numeric[0:20]
norme_Na = normMaxListComparison(list_Na_numeric[0:30],list_Na_analytic[0:30])
print 'norme_Na = ',norme_Na
epsilon_Na = 10.E-2
if norme_Na > epsilon_Na:
OKConc = 0
print 'Be carefull: error on the Na concentration higher than 10% and equal to ', norme_Na
else :
OKConc = 1
print 'Good agreement between numerical and analytical results for the Na concentration, error equal to ', norme_Na
print ' '
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
print " Comparison to analytical pH ~"
print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
table_pH_numeric = pHdeX
refSolution = [\
6.8114e-05,\
1.6401e-04,\
3.1388e-04,\
4.8294e-04,\
3.1468e-04,\
2.3243e-04,\
2.0708e-04, 2.0124e-04, 2.0018e-04, 2.0002e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-4]
K_num = ex11.getOutput('K_output')
print K_num
absc = K_num[-1][-1].getColumn(0)[0:]
knum = K_num[-1][-1].getColumn(3)[0:]
print "K =",knum
errorNorm = normMaxListComparison(K_num[-1][1].getColumn(3)[1:11],refSolution[0:10])
#for i in range(0,40):
# print " %15.10e %15.10e %15.10e "%(absc[i],knum[i],refSolution[i])
#
# The front is sharp, so the error control (0.2) is relatively large
#
print 'error norm = ',errorNorm
if errorNorm < 0.2 :
print "~~~~~~~~~~~~~~~\nThe results are stable\n~~~~~~~~~~~~~~~\n"
else:
raise Exception, "~~~~~~~~~~~~~~~\nThe results have significantely changed, check the case\n~~~~~~~~~~~~~~~\n"
ex11.end()
den = 1.0 / (2.0 * sqrt(longDis * time / (porosity * R)))
C1 = darcy * time / (porosity * R)
x = 0.0004
for dx in dxListe:
value = erfc((x - C1) * den)
value1 = exp(x * darcy / longDis) * erfc((x + C1) * den)
value = 0.5 * C0 * (value + value1)
AnalT.append(value)
x += dx
TCompare = []
for i in range(40):
if i % 2 != 0:
TCompare.append(TY.getColumn(-1)[i])
tracerNorm = normMaxListComparison(TCompare, AnalT[0:20])
print " AnalT =", AnalT[0:30]
print "tracer norm = ", tracerNorm
#
# R = 1 + Kd*2500*(1-porosity)/porosity (pho = 1 here)
#
R = 3.0 # Retardation factor = 1 + dq/dc = cec*(fraction of exchangeable ion )/aqueous
AnalK = []
den = 1.0 / (2.0 * sqrt(longDis * time / (porosity * R)))
C1 = darcy * time / (porosity * R)
x = 0.0004
for dx in dxListe:
value = erfc((x - C1) * den)
value1 = exp(x * darcy / longDis) * erfc((x + C1) * den)
value = 0.5 * C0 * (value + value1)
AnalK.append(value)
# Comparison to reference solution issued from phreeqC
#
refSolution = [\
6.8114e-05,\
1.6401e-04,\
3.1388e-04,\
4.8294e-04,\
3.1468e-04,\
2.3243e-04,\
2.0708e-04, 2.0124e-04, 2.0018e-04, 2.0002e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0000e-04,
2.0000e-04, 2.0000e-04, 2.0000e-04, 2.0004e-0]
K_num = module.getOutput('K_output')
print "K =",K_num[-1][-1].getColumn(2)[0:]
errorNorm = normMaxListComparison(K_num[-1][1].getColumn(2)[1:30],refSolution[1:30])
#
# The front is sharp, so the error control (0.05) is relatively large
#
print 'error norm = ',errorNorm
if errorNorm < 0.15 :
print "~~~~~~~~~~~~~~~\nThe results are stable\n~~~~~~~~~~~~~~~\n"
else:
raise Exception, "~~~~~~~~~~~~~~~\nThe results have significantely changed, check the case\n~~~~~~~~~~~~~~~\n"
#print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
#print " End of the guitest case ~"
#print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"
module.end()
R = 1. #Retardation factor for the tracer
longDis = 6.67e-4
AnalT = []
from math import exp
den = 1./(2.*sqrt(longDis*darcy*time/(porosity*R)))
C1 = darcy*time/(porosity*R)
x = 0.
dx = 0.0008
for dx in dxListe:
x+=dx
value = erfc((x-C1)*den)
value1 = exp(x/longDis)*erfc((x+C1)*den)
value = 0.5*C0*(value+value1)
AnalT.append(value)
tracerNorm = normMaxListComparison(TY.getColumn(2)[0:20],AnalT[0:20])
print 'tracer norm = ',tracerNorm
#
# R = 1 + Kd*2500*(1-porosity)/porosity (pho = 1 here)
#
R = 1. + 2 #Retardation factor
longDis = 6.67e-4
AnalK = []
from math import exp
den = 1./(2.*sqrt(longDis*darcy*time/(porosity*R)))
C1 = darcy*time/(porosity*R)
x = 0.0002
dx = 0.0004
for dx in dxListe:
value = erfc((x-C1)*den)
