Introduction

Installation

To install the divHretention package, simply run:

pip install divHretention

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()
_images/example_basic.png

From an input file

import matplotlib.pyplot as plt
import numpy as np

from divHretention import Exposition
filenames = [
      "data/exposure_conditions_divertor/WEST/West-LSN-P1.6e+21-IP0.449MW.csv",
      "data/exposure_conditions_divertor/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()
_images/example_files.png

Under the hood

This tool relies on a database of FESTIM simulations of monblocks under various exposure conditions (see figure below).

_images/monoblocks.jpg

The monoblock hydrogen inventory is computed for each of these exposure conditions and a Gaussian Regression is made:

_images/behaviour_law.png

This behaviour law is then used to compte the monoblock inventory for any exposure condition.

References

  • Delaporte-Mathurin, Rémi, Etienne A. Hodille, Jonathan Mougenot, Yann Charles, and Christian Grisolia. 2019. ‘Finite Element Analysis of Hydrogen Retention in ITER Plasma Facing Components Using FESTIM’. Nuclear Materials and Energy 21 (December): 100709. https://doi.org/10.1016/j.nme.2019.100709.

  • Delaporte-Mathurin, Rémi, Etienne Hodille, Jonathan Mougenot, Gregory De Temmerman, Yann Charles, and Christian Grisolia. 2020. ‘Parametric Study of Hydrogenic Inventory in the ITER Divertor Based on Machine Learning’. Scientific Reports 10 (1): 17798. https://doi.org/10.1038/s41598-020-74844-w.