divHretention is a tool to estimate ITER-like monoblock H inventories based on their surface temperature and surface concentration of H.
This package can be used to estimate H retention in tokamak divertors.
If you're using this package, please consider citing:
@article{delaporte-mathurin_parametric_2020,
author = {Delaporte-Mathurin, Rémi and Hodille, Etienne and Mougenot, Jonathan and De Temmerman, Gregory and Charles, Yann and Grisolia, Christian},
title = {Parametric study of hydrogenic inventory in the {ITER} divertor based on machine learning},
journal = {Scientific Reports},
volume = {10},
number = {1},
year = {2020},
pages = {17798},
doi = {10.1038/s41598-020-74844-w},
}
pip install divHretention
Click here for examples.
import matplotlib.pyplot as plt
import numpy as np
from divHretention import compute_inventory
x = np.linspace(0, 0.6, num=500) # arc length (m) along the divertor
T = 320 + 1000*np.exp(-50*x)
concentration = 5e21*np.exp(-50*x) # surface concentration (H m-3)
# compute the inventory (H/m) and standard deviation at 10 000s
inv, sig = compute_inventory(T, concentration, time=1e4)
plt.plot(x, inv)
plt.yscale("log")
plt.xlabel("Distance along divertor (m)")
plt.ylabel("Inventory per unit thickness (H/m)")
plt.show()
import matplotlib.pyplot as plt
from divHretention import Exposition
filenames = [
"examples/WEST/West-LSN-P1.6e+21-IP0.449MW.csv",
"examples/WEST/West-LSN-P2.5e+21-IP1.500MW.csv",
]
for i, filename in enumerate(filenames):
res = Exposition(filename, filetype="WEST")
plt.plot(res.arc_length, res.inventory, label="Case {}".format(i+1))
plt.legend()
plt.xlabel("Distance along divertor (m)")
plt.ylabel("Inventory per unit thickness (H/m)")
plt.yscale("log")
plt.show()