diff --git a/README.md b/README.md
index f496bf0..ebdeddb 100644
--- a/README.md
+++ b/README.md
@@ -8,25 +8,25 @@
[Task 2](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task2.ipynb): Simulation of a Thermo-Desorption experiment
-[Task 3](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task3.ipynb): Multi-material simulations
+[Task 3](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task3.ipynb): Simple permeation model
-[Task 4](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task4.ipynb): Post-processing and visualisation
+[Task 4](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task4.ipynb): Permeation barrier modelling
-[Task 5](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task5.ipynb): Advection-diffusion problem
+[Task 5](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task5.ipynb): Post-processing and visualisation
-[Task 6](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task6.ipynb): Heat transfer simulation
+[Task 6](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task6.ipynb): Advection-diffusion problem
-[Task 7](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task7.ipynb): CAD integration
+[Task 7](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task7.ipynb): Heat transfer simulation
-[Task 8](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task8.ipynb): Integration with the HTM library
+[Task 8](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task8.ipynb): CAD integration
+
+[Task 9](https://github.com/RemDelaporteMathurin/FESTIM-workshop/blob/main/tasks/task8.ipynb): Integration with the HTM library
## Getting started
### A. Codespaces (recommended)
-1. [Sign up to Codespace beta](https://github.com/features/codespaces/signup)
-
-2. [Create a Codespace](https://github.com/codespaces/new?machine=standardLinux32gb&repo=520445592&ref=main&devcontainer_path=.devcontainer%2Fdevcontainer.json&location=WestEurope) based on this repo
+You can [create a Codespace](https://github.com/codespaces/new?machine=standardLinux32gb&repo=520445592&ref=main&devcontainer_path=.devcontainer%2Fdevcontainer.json&location=WestEurope) based on this repo
### B. Local install
diff --git a/tasks/permeation_task.ipynb b/tasks/permeation_task.ipynb
deleted file mode 100644
index a4b2aeb..0000000
--- a/tasks/permeation_task.ipynb
+++ /dev/null
@@ -1,380 +0,0 @@
-{
- "cells": [
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Simple permeation simulation\n",
- "\n",
- "In this task, we'll go through the basics of FESTIM and run a simple permeation simulation on a 1D domain."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import festim as F"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The first step is to create a model using a `Simulation` object."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_model = F.Simulation()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We'll consider a 3 mm-thick material and a regular mesh (1000 cells)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "\n",
- "my_model.mesh = F.MeshFromVertices(\n",
- " vertices=np.linspace(0, 3e-4, num=1001)\n",
- ")"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "`Material` objects hold the materials properties like diffusivity and solubility.\n",
- "\n",
- "Here we only need the diffusivity defined as an Arrhenius law: $D = D_0 \\exp{(-E_D/k_B T)}$ where $k_B$ is the Boltzmann constant in eV/K and $T$ is the temperature in K. From this, the pre-exponential coefficient, $D_0$ in units m2/s, and the diffusion actiavtion energy, $E_D$ in units eV are needed.`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_model.materials = F.Material(id=1, D_0=1.9e-7, E_D=0.2)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The temperature is set at 500 K"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_model.T = F.Temperature(value=500)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "FESTIM has a `SievertsBC` class representing Sievert's law of solubility: $c = S \\ \\sqrt{P}$ at metal surfaces.\n",
- "\n",
- "> Note:\n",
- "> \n",
- "> A similar class exists for non-metallic materials behaving according to Henry's law: `HenrysBC`\n",
- "\n",
- "We'll use this boundary condition on the left surface (`id=1`) and will assume a zero concentration on the right side (`id=2`)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [],
- "source": [
- "P_up = 100 # Pa\n",
- "\n",
- "my_model.boundary_conditions = [\n",
- " F.SievertsBC(surfaces=1, S_0=4.02e21, E_S=1.04, pressure=P_up),\n",
- " F.DirichletBC(surfaces=2, value=0, field=0)\n",
- "]"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "With `Settings` we set the main solver parameters."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_model.settings = F.Settings(\n",
- " absolute_tolerance=1e-2,\n",
- " relative_tolerance=1e-10,\n",
- " final_time=100 # s\n",
- " )"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's choose a stepsize small enough to have good temporal resolution, with 1% change ratio:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "my_model.dt = F.Stepsize(initial_value=1/20)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "For this permeation experiment, we are only interested in the hydrogen flux on the right side:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "derived_quantities = F.DerivedQuantities([F.HydrogenFlux(surface=2)])\n",
- "\n",
- "my_model.exports = [derived_quantities]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Defining initial values\n",
- "Defining variational problem\n",
- "Defining source terms\n",
- "Defining boundary conditions\n",
- "Time stepping...\n",
- "100.0 % 1.0e+02 s Ellapsed time so far: 13.1 s\n",
- "100.0 % 1.0e+02 s Ellapsed time so far: 13.1 s\r"
- ]
- }
- ],
- "source": [
- "my_model.initialise()\n",
- "\n",
- "my_model.run()"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This problem can be solved analytically. The solution for the downstream flux is:\n",
- "\n",
- "$$\\mathrm{downstream \\ flux} = \\frac{P_\\mathrm{up} \\Phi}{L} \\left(1 + 2 \\sum_{n=1}^{\\infty} \\left(-1\\right)^{n} \\exp{(- \\frac{\\pi^{2} D n^{2} t}{L^{2}})}\\right) $$"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "def downstream_flux(t, P_up, permeability, L, D):\n",
- " \"\"\"calculates the downstream H flux at a given time t\n",
- "\n",
- " Args:\n",
- " t (float, np.array): the time\n",
- " P_up (float): upstream partial pressure of H\n",
- " permeability (float): material permeability\n",
- " L (float): material thickness\n",
- " D (float): diffusivity of H in the material\n",
- "\n",
- " Returns:\n",
- " float, np.array: the downstream flux of H\n",
- " \"\"\"\n",
- " n_array = np.arange(1, 10000)[:, np.newaxis]\n",
- " summation = np.sum((-1)**n_array * np.exp(-(np.pi * n_array)**2 * D/L**2 * t), axis=0)\n",
- " return P_up**0.5 * permeability / L * (2*summation + 1)"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We can compare the computed downstream flux to the analytical solution:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Text(0, 0.5, 'Downstream flux (H/m2/s)')"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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",
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