hello everyone, i try this program rasio convertion with carbide in benchmark. but i get error… can you help me?
this program:
import numpy as np
import matplotlib.pyplot as plt
import openmc.deplete
import openmc
import pandas as pd
results = openmc.deplete.Results(‘./depletion_results.h5’)
time = list(range(1, 11))
for i in range (1,11):
time_variable = ‘time’+str(i)
time.append(time_variable)
ax_fis_2 =
for i in range(1,11):
ax_fis_2_variable = ‘moxf2_’+str(i)
ax_fis_2.append(ax_fis_2_variable)
ax_fis_3 =
for i in range(1,11):
ax_fis_3_variable = ‘moxf3_’+str(i)
ax_fis_3.append(ax_fis_3_variable)
ax_fis_4 =
for i in range(1,11):
ax_fis_4_variable = ‘moxf4_’+str(i)
ax_fis_4.append(ax_fis_4_variable)
ax_fis_5 =
for i in range(1,11):
ax_fis_5_variable = ‘moxf5_’+str(i)
ax_fis_5.append(ax_fis_5_variable)
ax_fis_6 =
for i in range(1,11):
ax_fis_6_variable = ‘moxf6_’+str(i)
ax_fis_6.append(ax_fis_6_variable)
ax_fis_7 =
for i in range(1,11):
ax_fis_7_variable = ‘moxf7_’+str(i)
ax_fis_7.append(ax_fis_7_variable)
ax_fis_8 =
for i in range(1,11):
ax_fis_8_variable = ‘moxf8_’+str(i)
ax_fis_8.append(ax_fis_8_variable)
ax_capture_2 =
for i in range(1,11):
ax_capture_2_variable = ‘moxc2_’+str(i)
ax_capture_2.append(ax_capture_2_variable)
ax_capture_3 =
for i in range(1,11):
ax_capture_3_variable = ‘moxc3_’+str(i)
ax_capture_3.append(ax_capture_3_variable)
ax_capture_4 =
for i in range(1,11):
ax_capture_4_variable = ‘moxc4_’+str(i)
ax_capture_4.append(ax_capture_4_variable)
ax_capture_5 =
for i in range(1,11):
ax_capture_5_variable = ‘moxc5_’+str(i)
ax_capture_5.append(ax_capture_5_variable)
ax_capture_6 =
for i in range(1,11):
ax_capture_6_variable = ‘moxc6_’+str(i)
ax_capture_6.append(ax_capture_6_variable)
ax_capture_7 =
for i in range(1,11):
ax_capture_7_variable = ‘moxc7_’+str(i)
ax_capture_7.append(ax_capture_7_variable)
ax_capture_8 =
for i in range(1,11):
ax_capture_8_variable = ‘moxc8_’+str(i)
ax_capture_8.append(ax_capture_8_variable)
material_id =
for i in range(1, 11):
id = str(i)
material_id.append(id)
sum_ax_fis_2 = 0
sum_ax_fis_3 = 0
sum_ax_fis_4 = 0
sum_ax_fis_5 = 0
sum_ax_fis_6 = 0
sum_ax_fis_7 = 0
sum_ax_fis_8 = 0
sum_ax_capture_2 = 0
sum_ax_capture_3 = 0
sum_ax_capture_4 = 0
sum_ax_capture_5 = 0
sum_ax_capture_6 = 0
sum_ax_capture_7 = 0
sum_ax_capture_8 = 0
‘’’
for i, j in zip(range(10), range(1, 11)):
time[i], ax_fis_2[i] = results.get_reaction_rate(str(j), ‘U233’, ‘fission’)
time[i], ax_fis_3[i] = results.get_reaction_rate(str(j), ‘U236’, ‘fission’)
time[i], ax_fis_4[i] = results.get_reaction_rate(str(j), ‘U238’, ‘fission’)
time[i], ax_fis_5[i] = results.get_reaction_rate(str(j), ‘Pu239’, ‘fission’)
time[i], ax_fis_6[i] = results.get_reaction_rate(str(j), ‘Pu240’, ‘fission’)
time[i], ax_fis_7[i] = results.get_reaction_rate(str(j), ‘Pu241’, ‘fission’)
time[i], ax_fis_8[i] = results.get_reaction_rate(str(j), ‘Pu242’, ‘fission’)
time[i], ax_capture_2[i] = results.get_reaction_rate(str(j), 'U233', '(n,gamma)')
time[i], ax_capture_3[i] = results.get_reaction_rate(str(j), 'U236', '(n,gamma)')
time[i], ax_capture_4[i] = results.get_reaction_rate(str(j), 'U238', '(n,gamma)')
time[i], ax_capture_5[i] = results.get_reaction_rate(str(j), 'Pu239', '(n,gamma)')
time[i], ax_capture_6[i] = results.get_reaction_rate(str(j), 'Pu240', '(n,gamma)')
time[i], ax_capture_7[i] = results.get_reaction_rate(str(j), 'Pu241', '(n,gamma)')
time[i], ax_capture_8[i] = results.get_reaction_rate(str(j), 'Pu242', '(n,gamma)')
sum_ax_fis_2 += ax_fis_2[i]
sum_ax_fis_3 += ax_fis_3[i]
sum_ax_fis_4 += ax_fis_4[i]
sum_ax_fis_5 += ax_fis_5[i]
sum_ax_fis_6 += ax_fis_6[i]
sum_ax_fis_7 += ax_fis_7[i]
sum_ax_fis_8 += ax_fis_8[i]
‘’’
for i, j in enumerate(range(1, 11)):
time[i], ax_fis_2[i] = results.get_reaction_rate(str(j), ‘U233’, ‘fission’)
time[i], ax_fis_3[i] = results.get_reaction_rate(str(j), ‘U236’, ‘fission’)
time[i], ax_fis_4[i] = results.get_reaction_rate(str(j), ‘U238’, ‘fission’)
time[i], ax_fis_5[i] = results.get_reaction_rate(str(j), ‘Pu239’, ‘fission’)
time[i], ax_fis_6[i] = results.get_reaction_rate(str(j), ‘Pu240’, ‘fission’)
time[i], ax_fis_7[i] = results.get_reaction_rate(str(j), ‘Pu241’, ‘fission’)
time[i], ax_fis_8[i] = results.get_reaction_rate(str(j), ‘Pu242’, ‘fission’)
time[i], ax_capture_2[i] = results.get_reaction_rate(str(j), 'U233', '(n,gamma)')
time[i], ax_capture_3[i] = results.get_reaction_rate(str(j), 'U236', '(n,gamma)')
time[i], ax_capture_4[i] = results.get_reaction_rate(str(j), 'U238', '(n,gamma)')
time[i], ax_capture_5[i] = results.get_reaction_rate(str(j), 'Pu239', '(n,gamma)')
time[i], ax_capture_6[i] = results.get_reaction_rate(str(j), 'Pu240', '(n,gamma)')
time[i], ax_capture_7[i] = results.get_reaction_rate(str(j), 'Pu241', '(n,gamma)')
time[i], ax_capture_8[i] = results.get_reaction_rate(str(j), 'Pu242', '(n,gamma)')
sum_ax_fis_2 = np.sum(ax_fis_2)
sum_ax_fis_3 = np.sum(ax_fis_3)
sum_ax_fis_4 = np.sum(ax_fis_4)
sum_ax_fis_5 = np.sum(ax_fis_5)
sum_ax_fis_6 = np.sum(ax_fis_6)
sum_ax_fis_7 = np.sum(ax_fis_7)
sum_ax_fis_8 = np.sum(ax_fis_8)
capture
U233_capture = sum_capture2
U236_capture = sum_capture3
U238_capture = sum_capture4
Pu239_capture = sum_capture5
Pu240_capture = sum_capture6
Pu241_capture = sum_capture7
Pu242_capture = sum_capture8
absorption
U233_absorption = U233_capture + sum_fis2
U236_absorption = U236_capture + sum_fis3
U238_absorption = U238_capture + sum_fis4
Pu239_absorption = Pu239_capture + sum_fis5
Pu240_absorption = Pu240_capture + sum_fis6
Pu241_absorption = Pu241_capture + sum_fis7
Pu242_absorption = Pu242_capture + sum_fis8
CR = ( U236_capture + U238_capture + Pu240_capture + Pu242_capture ) / ( U233_absorption + Pu239_absorption + Pu241_absorption )
time_k, k = results.get_eigenvalue()
time_k = time_k/(6060243012)
CR_file = pd.DataFrame(CR)
CR_file.to_excel(‘CR_file.xlsx’)
k = np.delete(k, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 0) # data keff per tahun
time_k = np.delete(time_k,[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 0) #waktu per tahun
time_k = time/(606024*365)
fig = plt.figure()
plt.plot(time_k,CR,label=‘CR’)
plt.xlabel(‘time_k (years)’)
plt.ylabel(‘Convertion Ratio’)
plt.title(‘Convertion Ratio’, y = 1.08)
plt.legend()
plt.savefig(‘Convertion Ratio.jpg’,dpi = 1000, bbox_inches = ‘tight’)
fig.show()