divHretention

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},
}

👉 Documentation 👉 Publication

Get started

pip install divHretention

Click here for examples.

Basic usage

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()

From an input file

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()