From 04e78dd63b443a2f8d0e75dfd4d7bd5c545d3783 Mon Sep 17 00:00:00 2001
From: Robert Lanzafame <R.C.Lanzafame@tudelft.nl>
Date: Wed, 13 Nov 2024 06:17:56 +0100
Subject: [PATCH] WS 2.1 update files
---
content/Week_2_1/WS_2_1_solution.ipynb | 175 +++++----
content/Week_2_1/WS_2_1_wiggle.ipynb | 427 +++++----------------
src/teachers/Week_2_1/WS_2_1_solution.html | 151 +++++---
src/teachers/Week_2_1/WS_2_1_wiggle.html | 387 +++++--------------
4 files changed, 374 insertions(+), 766 deletions(-)
diff --git a/content/Week_2_1/WS_2_1_solution.ipynb b/content/Week_2_1/WS_2_1_solution.ipynb
index ea3e5432..acab22b5 100644
--- a/content/Week_2_1/WS_2_1_solution.ipynb
+++ b/content/Week_2_1/WS_2_1_solution.ipynb
@@ -72,7 +72,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 40,
"id": "cdc8695d-6c5f-4be7-ac49-1aa29fac0128",
"metadata": {
"tags": []
@@ -159,7 +159,7 @@
"tags": []
},
"source": [
- "## Task 1: Implement Central Difference\n",
+ "## Part 1: Implement Central Difference\n",
"\n",
"We are going to implement the central difference scheme as derived in the textbook; however, **instead of implementing the system of equations in a matrix formulation**, we will _loop over each of the finite volumes in the system,_ one at a time.\n",
"\n",
@@ -168,7 +168,7 @@
},
{
"cell_type": "markdown",
- "id": "462bbd4d-18ba-453c-bb08-7d4cc04d6576",
+ "id": "18306064-69a2-4513-967e-992f1f88c9da",
"metadata": {},
"source": [
"\n",
@@ -176,6 +176,25 @@
"<p>\n",
"<b>Task 1.1:</b>\n",
"\n",
+ "Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).\n",
+ "\n",
+ "$$\n",
+ "\\frac{\\partial \\phi}{\\partial t} + c\\frac{\\partial \\phi}{\\partial x} = 0\n",
+ "$$\n",
+ " \n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a475d0d7",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
+ "<p>\n",
+ "<b>Task 1.2:</b>\n",
+ "\n",
"<b>Read.</b> Read the code to understand the problem that has been set up for you. Check the arguments and return values; the docstrings are purposefully ommitted so you can focus on the code. You might as well re-read the instructions above one more time, as well (let's be honest, you probably just skimmed over it anyway...)\n",
" \n",
"</div>"
@@ -183,8 +202,8 @@
},
{
"cell_type": "code",
- "execution_count": 2,
- "id": "ba55cdd8",
+ "execution_count": 41,
+ "id": "b1465443",
"metadata": {},
"outputs": [],
"source": [
@@ -196,25 +215,6 @@
"from mpl_toolkits.mplot3d import Axes3D"
]
},
- {
- "cell_type": "markdown",
- "id": "18306064-69a2-4513-967e-992f1f88c9da",
- "metadata": {},
- "source": [
- "\n",
- "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
- "<p>\n",
- "<b>Task 1.2:</b>\n",
- "\n",
- "Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).\n",
- "\n",
- "$$\n",
- "\\frac{\\partial \\phi}{\\partial t} + c\\frac{\\partial \\phi}{\\partial x} = 0\n",
- "$$\n",
- " \n",
- "</div>"
- ]
- },
{
"cell_type": "markdown",
"id": "65abc948-f674-48b8-a752-2ae714525bb9",
@@ -225,7 +225,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 42,
"id": "65376af7-30e6-43e6-9109-b809ad4c5d56",
"metadata": {
"tags": []
@@ -233,7 +233,16 @@
"outputs": [],
"source": [
"def initialize_1D(p0, L, Nx, T, Nt, square=True):\n",
+ " \"\"\"Initialize 1D advection simulation.\n",
+ "\n",
+ " Arguments are defined elsewhere, except one keyword argument\n",
+ " defines the shape of the initial condition.\n",
" \n",
+ " square : bool\n",
+ " - specifies a square pulse if True\n",
+ " - specifies a Gaussian shape if False\n",
+ " \"\"\"\n",
+ "\n",
" dx = L/Nx\n",
" dt = T/Nt\n",
" \n",
@@ -253,7 +262,7 @@
" return x, p_all\n",
"\n",
"def advection_1D(p, dx, dt, c, Nx, central=True):\n",
- "\n",
+ " \"\"\"Solve the advection problem.\"\"\"\n",
" p_new = np.zeros(Nx)\n",
" for i in range(0, Nx):\n",
" if central:\n",
@@ -266,7 +275,8 @@
" \n",
"def run_simulation_1D(p0, c, L, Nx, T, Nt, dx, dt,\n",
" central=True, square=True):\n",
- " \n",
+ " \"\"\"Run sumulation by evaluating each time step.\"\"\"\n",
+ "\n",
" x, p_all = initialize_1D(p0, L, Nx, T, Nt, square=square)\n",
" \n",
" for t in range(Nt):\n",
@@ -276,6 +286,7 @@
" return x, p_all\n",
" \n",
"def plot_1D(x, p, step=0):\n",
+ " \"\"\"Plot phi(x, t) at a given time step.\"\"\"\n",
" fig = plt.figure()\n",
" ax = plt.axes(xlim=(0, round(x.max())),\n",
" ylim=(0, int(np.ceil(p[0].max())) + 1)) \n",
@@ -286,6 +297,7 @@
" plt.show()\n",
" \n",
"def plot_1D_all():\n",
+ " \"\"\"Create animation of phi(x, t) for all t.\"\"\"\n",
" check_variables_1D()\n",
" \n",
" play = widgets.Play(min=0, max=Nt-1, step=1, value=0,\n",
@@ -298,6 +310,10 @@
" return widgets.HBox([slider])\n",
" \n",
"def check_variables_1D():\n",
+ " \"\"\"Print current variable values.\n",
+ " \n",
+ " Students define CFL.\n",
+ " \"\"\"\n",
" print('Current variables values:')\n",
" print(f' p0 [---]: {p0:0.2f}')\n",
" print(f' c [m/s]: {c:0.2f}')\n",
@@ -309,7 +325,11 @@
" print(f' dt [ s ]: {dt:0.2e}')\n",
" print(f'Using central difference?: {central}')\n",
" print(f'Using square init. cond.?: {square}')\n",
- " print(f'CFL: {c*dt/dx:.2e}')"
+ " calculated_CFL = c*dt/dx\n",
+ " if calculated_CFL is None:\n",
+ " print('CFL not calculated yet.')\n",
+ " else:\n",
+ " print(f'CFL: {calculated_CFL:.2e}')"
]
},
{
@@ -340,7 +360,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 43,
"id": "fb0c4d1e-7fce-480d-96c7-7bddcffd525c",
"metadata": {
"tags": []
@@ -386,7 +406,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 44,
"id": "6f7306d6-c303-4cfb-bd24-2eb2976b695c",
"metadata": {},
"outputs": [],
@@ -421,7 +441,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 45,
"id": "580c0f9d-5172-40b8-b9da-8579a9485cdf",
"metadata": {
"tags": []
@@ -477,7 +497,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 46,
"id": "bad7e23c",
"metadata": {},
"outputs": [],
@@ -507,7 +527,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 47,
"id": "48a174cf-79c1-4598-b438-5a139abd68af",
"metadata": {
"tags": []
@@ -547,7 +567,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 48,
"id": "bc849eb8-4c49-4691-b7f8-35985527aca4",
"metadata": {
"tags": []
@@ -578,7 +598,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 49,
"id": "06742685-1f3c-43d1-8657-86a0d324b79f",
"metadata": {
"tags": []
@@ -605,7 +625,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "4f864d2609e449c8bb272b4c1e65c31b",
+ "model_id": "0ee8d9a080c84b35bd6dcecafcbe480f",
"version_major": 2,
"version_minor": 0
},
@@ -619,7 +639,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a1d934af5fdc4799b89714096371ee38",
+ "model_id": "0cfaf3de33ff4541a6bc473ad7e1c158",
"version_major": 2,
"version_minor": 0
},
@@ -627,7 +647,7 @@
"HBox(children=(IntSlider(value=0, max=99999),))"
]
},
- "execution_count": 10,
+ "execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
@@ -636,12 +656,25 @@
"plot_1D_all()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "93554146",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
+ "<p>\n",
+ "<b>Task 1.4:</b>\n",
+ "If you have not already done so, modify the function above to calculate the CFL and rerun the cells to check the values. Can you explain the effect you see when running the animation from <code>plot_1D_all()</code>?\n",
+ "</div>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "62204ce3-0e2f-4641-9615-3cc1d1fe4a24",
"metadata": {},
"source": [
- "## Task 2: Central Difference issues!\n",
+ "## Part 2: Central Difference issues!\n",
"\n",
"The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\\phi$ values.\n",
"\n",
@@ -663,7 +696,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 50,
"id": "8672fa59-e451-4271-8d12-470b8c42df67",
"metadata": {},
"outputs": [
@@ -688,7 +721,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5f73f484d3d74c078b6c553deb044e86",
+ "model_id": "9893b60516274bd7b134bc038e7c3582",
"version_major": 2,
"version_minor": 0
},
@@ -702,7 +735,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "024cd395d4f3438f8301cff8c9c06951",
+ "model_id": "ba0884d06fe74d038abb79930962b3c3",
"version_major": 2,
"version_minor": 0
},
@@ -710,7 +743,7 @@
"HBox(children=(IntSlider(value=0, max=99999),))"
]
},
- "execution_count": 11,
+ "execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
@@ -726,7 +759,7 @@
"id": "bd6bf070-db79-4e76-a44d-e9f187263975",
"metadata": {},
"source": [
- "## Task 3: Upwind scheme\n",
+ "## Part 3: Upwind scheme\n",
"\n",
"More weight can be given to the upwind cells by choosing $\\phi$ values for the East face $\\phi_i$, and for the West face, use $\\phi_{i-1}$. This holds true for positive flow directions. For negative flow directions, you should choose $\\phi$ values for the East face $\\phi_{i-1}$, and for the West face, use $\\phi_{i}$. Note that this is less accurate than the central diffence approach but it will ensure stability."
]
@@ -748,7 +781,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 51,
"id": "1860025d-3c94-4fb7-ac2e-72c8b60578e0",
"metadata": {
"tags": []
@@ -775,7 +808,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e16d805478914cce9836cc40441f95c6",
+ "model_id": "38d5a28ef7ea4dc286154ae35fc84308",
"version_major": 2,
"version_minor": 0
},
@@ -789,7 +822,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d54c1b34eebb409eb66c766dfa2a77a9",
+ "model_id": "2ac4a5e00466491e9be840556fc68502",
"version_major": 2,
"version_minor": 0
},
@@ -797,7 +830,7 @@
"HBox(children=(IntSlider(value=0, max=99999),))"
]
},
- "execution_count": 12,
+ "execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
@@ -817,7 +850,7 @@
"tags": []
},
"source": [
- "## Task 4: Stability Analysis and False Diffusion\n",
+ "## Part 4: Stability Analysis and False Diffusion\n",
"\n",
"Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:\n",
"$$\n",
@@ -835,14 +868,14 @@
"\n",
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 4:</b>\n",
+ "<b>Task 4.1:</b>\n",
"Change the Python variables that define the problem and check to see if the CFL predicts stability of the method. You are welcome to explore both discretization schemes and pulse types; there is no \"right answer\" to this question, except confirming that large CFL causes instability, and vice-versa.\n",
"</div>"
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 52,
"id": "faf77fd1-0c67-4452-88d4-51dcc88768dc",
"metadata": {
"tags": []
@@ -864,7 +897,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 53,
"id": "444571d6-58af-498e-846f-72867a972625",
"metadata": {
"tags": []
@@ -891,7 +924,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "63927b65f44544e187f2be997ca4aad1",
+ "model_id": "3eae551362664045964396cdf0e2b092",
"version_major": 2,
"version_minor": 0
},
@@ -905,7 +938,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "26b774d16c794b7ea5b2640f0396126e",
+ "model_id": "ddc4eba671064b2baae59a8225c703fb",
"version_major": 2,
"version_minor": 0
},
@@ -913,7 +946,7 @@
"HBox(children=(IntSlider(value=0, max=9999),))"
]
},
- "execution_count": 14,
+ "execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
@@ -931,7 +964,7 @@
"\n",
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 4.1:</b>\n",
+ "<b>Task 4.2:</b>\n",
"\n",
"You probably found that for CFL equal to 1, the solution is exact: the wave propagates without deforming. However, a complex flow has variable speed in time and space. That is why in practice a conservative CFL value is chosen for the largest expected flow velocities. Make sure to test a CFL condition of 0.8 to visualize the impact on the numerical solution. \n",
" \n",
@@ -951,7 +984,7 @@
"id": "df03203d-587e-4b8b-9beb-ba41013005f9",
"metadata": {},
"source": [
- "## Task 5: 2D Implementation in Python"
+ "## Part 5: 2D Implementation in Python"
]
},
{
@@ -961,7 +994,7 @@
"source": [
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 5:</b>\n",
+ "<b>Task 5.1:</b>\n",
"Apply FVM by hand to the 2D advection equation. The volumes are rectangular. This is a good example of an exam problem.\n",
"\n",
"$$\n",
@@ -978,7 +1011,7 @@
"source": [
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 6:</b>\n",
+ "<b>Task 5.2:</b>\n",
"The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called \"numerical diffusion\" --- when the numerical scheme causes the square pulse to \"diffuse\" into a bell shaped surface. Even though only the advection term was implmented!\n",
"</div>"
]
@@ -999,7 +1032,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 54,
"id": "3353a233",
"metadata": {},
"outputs": [],
@@ -1024,7 +1057,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 55,
"id": "be2b163b-811d-498d-82e9-a6b56327efcd",
"metadata": {
"tags": []
@@ -1036,7 +1069,7 @@
"text": [
"<>:47: SyntaxWarning: invalid escape sequence '\\p'\n",
"<>:47: SyntaxWarning: invalid escape sequence '\\p'\n",
- "C:\\Users\\rlanzafame\\AppData\\Local\\Temp\\ipykernel_18720\\2412768473.py:47: SyntaxWarning: invalid escape sequence '\\p'\n",
+ "C:\\Users\\rlanzafame\\AppData\\Local\\Temp\\ipykernel_26208\\2412768473.py:47: SyntaxWarning: invalid escape sequence '\\p'\n",
" ax.set_zlabel('$\\phi$ [-]')\n"
]
}
@@ -1147,7 +1180,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 56,
"id": "cbf8c749-4c91-4ec2-8849-99e9e7652f2e",
"metadata": {
"tags": []
@@ -1212,7 +1245,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 57,
"id": "94e5c840-0373-451f-af12-2ac58138cc5e",
"metadata": {
"tags": []
@@ -1245,7 +1278,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "5639cefbe0ae40f5b8ceb39cc8fe17ae",
+ "model_id": "ddb08da1ff2f48ef80377af808c4acb2",
"version_major": 2,
"version_minor": 0
},
@@ -1259,7 +1292,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "3038a60fb891458cbe6628117fbf9fde",
+ "model_id": "17eec738b90d418db312aca1d20378a2",
"version_major": 2,
"version_minor": 0
},
@@ -1267,7 +1300,7 @@
"HBox(children=(IntSlider(value=0, max=999),))"
]
},
- "execution_count": 18,
+ "execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
@@ -1292,7 +1325,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 58,
"id": "96aec400-4cc3-412d-863d-9be6c2462f54",
"metadata": {},
"outputs": [
@@ -1323,7 +1356,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "d804fb2cc24f43c88c8cf64a2bcc057d",
+ "model_id": "273f37d5a8d44722b1fdd6292c6f69be",
"version_major": 2,
"version_minor": 0
},
@@ -1337,7 +1370,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "a50f967e6ddc419192594ee033b49cdc",
+ "model_id": "56125097d08a4f579d61eac120dfdf82",
"version_major": 2,
"version_minor": 0
},
@@ -1345,7 +1378,7 @@
"HBox(children=(IntSlider(value=0, max=999),))"
]
},
- "execution_count": 19,
+ "execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
diff --git a/content/Week_2_1/WS_2_1_wiggle.ipynb b/content/Week_2_1/WS_2_1_wiggle.ipynb
index b1dd8d88..bc7f7699 100644
--- a/content/Week_2_1/WS_2_1_wiggle.ipynb
+++ b/content/Week_2_1/WS_2_1_wiggle.ipynb
@@ -70,7 +70,7 @@
"tags": []
},
"source": [
- "## Task 1: Implement Central Difference\n",
+ "## Part 1: Implement Central Difference\n",
"\n",
"We are going to implement the central difference scheme as derived in the textbook; however, **instead of implementing the system of equations in a matrix formulation**, we will _loop over each of the finite volumes in the system,_ one at a time.\n",
"\n",
@@ -79,7 +79,7 @@
},
{
"cell_type": "markdown",
- "id": "462bbd4d-18ba-453c-bb08-7d4cc04d6576",
+ "id": "18306064-69a2-4513-967e-992f1f88c9da",
"metadata": {},
"source": [
"\n",
@@ -87,6 +87,25 @@
"<p>\n",
"<b>Task 1.1:</b>\n",
"\n",
+ "Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).\n",
+ "\n",
+ "$$\n",
+ "\\frac{\\partial \\phi}{\\partial t} + c\\frac{\\partial \\phi}{\\partial x} = 0\n",
+ "$$\n",
+ " \n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4539029d",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
+ "<p>\n",
+ "<b>Task 1.2:</b>\n",
+ "\n",
"<b>Read.</b> Read the code to understand the problem that has been set up for you. Check the arguments and return values; the docstrings are purposefully ommitted so you can focus on the code. You might as well re-read the instructions above one more time, as well (let's be honest, you probably just skimmed over it anyway...)\n",
" \n",
"</div>"
@@ -94,8 +113,8 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "id": "ba55cdd8",
+ "execution_count": null,
+ "id": "60dbc953",
"metadata": {},
"outputs": [],
"source": [
@@ -107,25 +126,6 @@
"from mpl_toolkits.mplot3d import Axes3D"
]
},
- {
- "cell_type": "markdown",
- "id": "18306064-69a2-4513-967e-992f1f88c9da",
- "metadata": {},
- "source": [
- "\n",
- "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
- "<p>\n",
- "<b>Task 1.2:</b>\n",
- "\n",
- "Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).\n",
- "\n",
- "$$\n",
- "\\frac{\\partial \\phi}{\\partial t} + c\\frac{\\partial \\phi}{\\partial x} = 0\n",
- "$$\n",
- " \n",
- "</div>"
- ]
- },
{
"cell_type": "markdown",
"id": "4822b0ad",
@@ -151,7 +151,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": null,
"id": "65376af7-30e6-43e6-9109-b809ad4c5d56",
"metadata": {
"tags": []
@@ -159,15 +159,22 @@
"outputs": [],
"source": [
"def initialize_1D(p0, L, Nx, T, Nt, square=True):\n",
- " \n",
+ " \"\"\"Initialize 1D advection simulation.\n",
+ "\n",
+ " Arguments are defined elsewhere, except one keyword argument\n",
+ " defines the shape of the initial condition.\n",
+ "\n",
+ " square : bool\n",
+ " - specifies a square pulse if True\n",
+ " - specifies a Gaussian shape if False\n",
+ " \"\"\"\n",
+ "\n",
" dx = L/Nx\n",
" dt = T/Nt\n",
" \n",
" x = np.linspace(dx/2, L - dx/2, Nx)\n",
"\n",
- " # Initialise domain:\n",
- " # - a square pulse with p0 between 0.5 and 1\n",
- " # - a Gaussian shape around 1\n",
+ " \n",
" if square:\n",
" p_init = np.zeros(Nx)\n",
" p_init[int(.5/dx):int(1/dx + 1)] = p0\n",
@@ -179,7 +186,7 @@
" return x, p_all\n",
"\n",
"def advection_1D(p, dx, dt, c, Nx, central=True):\n",
- "\n",
+ " \"\"\"Solve the advection problem.\"\"\"\n",
" p_new = np.zeros(Nx)\n",
" for i in range(0, Nx):\n",
" if central:\n",
@@ -190,7 +197,8 @@
" \n",
"def run_simulation_1D(p0, c, L, Nx, T, Nt, dx, dt,\n",
" central=True, square=True):\n",
- " \n",
+ " \"\"\"Run sumulation by evaluating each time step.\"\"\"\n",
+ "\n",
" x, p_all = initialize_1D(p0, L, Nx, T, Nt, square=square)\n",
" \n",
" for t in range(Nt):\n",
@@ -200,6 +208,7 @@
" return x, p_all\n",
" \n",
"def plot_1D(x, p, step=0):\n",
+ " \"\"\"Plot phi(x, t) at a given time step.\"\"\"\n",
" fig = plt.figure()\n",
" ax = plt.axes(xlim=(0, round(x.max())),\n",
" ylim=(0, int(np.ceil(p[0].max())) + 1)) \n",
@@ -210,6 +219,7 @@
" plt.show()\n",
" \n",
"def plot_1D_all():\n",
+ " \"\"\"Create animation of phi(x, t) for all t.\"\"\"\n",
" check_variables_1D()\n",
" \n",
" play = widgets.Play(min=0, max=Nt-1, step=1, value=0,\n",
@@ -222,6 +232,10 @@
" return widgets.HBox([slider])\n",
" \n",
"def check_variables_1D():\n",
+ " \"\"\"Print current variable values.\n",
+ " \n",
+ " Students define CFL.\n",
+ " \"\"\"\n",
" print('Current variables values:')\n",
" print(f' p0 [---]: {p0:0.2f}')\n",
" print(f' c [m/s]: {c:0.2f}')\n",
@@ -233,7 +247,11 @@
" print(f' dt [ s ]: {dt:0.2e}')\n",
" print(f'Using central difference?: {central}')\n",
" print(f'Using square init. cond.?: {square}')\n",
- " print(f'CFL: {c*dt/dx:.2e}')"
+ " calculated_CFL = None\n",
+ " if calculated_CFL is None:\n",
+ " print('CFL not calculated yet.')\n",
+ " else:\n",
+ " print(f'CFL: {calculated_CFL:.2e}')"
]
},
{
@@ -248,7 +266,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": null,
"id": "bad7e23c",
"metadata": {},
"outputs": [],
@@ -278,31 +296,12 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"id": "48a174cf-79c1-4598-b438-5a139abd68af",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " c [m/s]: 5.00\n",
- " L [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " T [ s ]: 40.0\n",
- " Nt [---]: 100000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dt [ s ]: 4.00e-04\n",
- "Using central difference?: True\n",
- "Using square init. cond.?: True\n",
- "CFL: 1.00e-01\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"check_variables_1D()\n",
"x, p_all = run_simulation_1D(p0, c, L, Nx, T, Nt, dx, dt, central, square)"
@@ -318,23 +317,12 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"id": "bc849eb8-4c49-4691-b7f8-35985527aca4",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"plot_1D(x, p_all)"
]
@@ -349,70 +337,35 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"id": "06742685-1f3c-43d1-8657-86a0d324b79f",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " c [m/s]: 5.00\n",
- " L [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " T [ s ]: 40.0\n",
- " Nt [---]: 100000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dt [ s ]: 4.00e-04\n",
- "Using central difference?: True\n",
- "Using square init. cond.?: True\n",
- "CFL: 1.00e-01\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "89e04bb9e4644b5abd59d4175f86d76e",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Play(value=0, description='step', max=99999), Output()), _dom_classes=('widget-interact'…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "85ce5e98ce254db5b3cd87e7557ce595",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(IntSlider(value=0, max=99999),))"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"plot_1D_all()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "30c0fb7b",
+ "metadata": {},
+ "source": [
+ "\n",
+ "<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
+ "<p>\n",
+ "<b>Task 1.4:</b>\n",
+ "If you have not already done so, modify the function above to calculate the CFL and rerun the cells to check the values. Can you explain the effect you see when running the animation from <code>plot_1D_all()</code>?\n",
+ "</div>"
+ ]
+ },
{
"cell_type": "markdown",
"id": "62204ce3-0e2f-4641-9615-3cc1d1fe4a24",
"metadata": {},
"source": [
- "## Task 2: Central Difference issues!\n",
+ "## Part 2: Central Difference issues!\n",
"\n",
"The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\\phi$ values.\n",
"\n",
@@ -434,58 +387,10 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"id": "8672fa59-e451-4271-8d12-470b8c42df67",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " c [m/s]: 5.00\n",
- " L [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " T [ s ]: 40.0\n",
- " Nt [---]: 100000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dt [ s ]: 4.00e-04\n",
- "Using central difference?: True\n",
- "Using square init. cond.?: False\n",
- "CFL: 1.00e-01\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "44907f3083d641c888c331e844177ee2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Play(value=0, description='step', max=99999), Output()), _dom_classes=('widget-interact'…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "3e1ba9ea0a17469faaf58ae8d9ca80fe",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(IntSlider(value=0, max=99999),))"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"square=False\n",
"x, p_all = run_simulation_1D(p0, c, L, Nx, T, Nt, dx, dt, central, square)\n",
@@ -519,7 +424,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": null,
"id": "1860025d-3c94-4fb7-ac2e-72c8b60578e0",
"metadata": {
"tags": []
@@ -536,7 +441,7 @@
"tags": []
},
"source": [
- "## Task 4: Stability Analysis and False Diffusion\n",
+ "## Part 4: Stability Analysis and False Diffusion\n",
"\n",
"Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:\n",
"$$\n",
@@ -554,14 +459,14 @@
"\n",
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 4:</b>\n",
+ "<b>Task 4.1:</b>\n",
"Change the Python variables that define the problem and check to see if the CFL predicts stability of the method. You are welcome to explore both discretization schemes and pulse types; there is no \"right answer\" to this question, except confirming that large CFL causes instability, and vice-versa.\n",
"</div>"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"id": "faf77fd1-0c67-4452-88d4-51dcc88768dc",
"metadata": {
"tags": []
@@ -579,7 +484,7 @@
"\n",
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 4.1:</b>\n",
+ "<b>Task 4.2:</b>\n",
"\n",
"You probably found that for CFL equal to 1, the solution is exact: the wave propagates without deforming. However, a complex flow has variable speed in time and space. That is why in practice a conservative CFL value is chosen for the largest expected flow velocities. Make sure to test a CFL condition of 0.8 to visualize the impact on the numerical solution. \n",
" \n",
@@ -588,7 +493,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": null,
"id": "8d2ad587",
"metadata": {},
"outputs": [],
@@ -601,7 +506,7 @@
"id": "df03203d-587e-4b8b-9beb-ba41013005f9",
"metadata": {},
"source": [
- "## Task 5: 2D Implementation in Python"
+ "## Part 5: 2D Implementation in Python"
]
},
{
@@ -611,7 +516,7 @@
"source": [
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 5:</b>\n",
+ "<b>Task 5.1:</b>\n",
"Apply FVM by hand to the 2D advection equation. The volumes are rectangular. This is a good example of an exam problem.\n",
"\n",
"$$\n",
@@ -628,7 +533,7 @@
"source": [
"<div style=\"background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px\">\n",
"<p>\n",
- "<b>Task 6:</b>\n",
+ "<b>Task 5.2:</b>\n",
"The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called \"numerical diffusion\" --- when the numerical scheme causes the square pulse to \"diffuse\" into a bell shaped surface. Even though only the advection term was implmented!\n",
"</div>"
]
@@ -649,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"id": "3353a233",
"metadata": {},
"outputs": [],
@@ -674,23 +579,12 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"id": "be2b163b-811d-498d-82e9-a6b56327efcd",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "<>:47: SyntaxWarning: invalid escape sequence '\\p'\n",
- "<>:47: SyntaxWarning: invalid escape sequence '\\p'\n",
- "C:\\Users\\rlanzafame\\AppData\\Local\\Temp\\ipykernel_16812\\198321165.py:47: SyntaxWarning: invalid escape sequence '\\p'\n",
- " ax.set_zlabel('$\\phi$ [-]')\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"def initialize_2D(p0, Lx, Nx, Ly, Ny, T, Nt):\n",
" x = np.linspace(dx/2, Lx - dx/2, Nx)\n",
@@ -797,47 +691,12 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"id": "cbf8c749-4c91-4ec2-8849-99e9e7652f2e",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " cx [m/s]: 5.00\n",
- " cy [m/s]: 5.00\n",
- " Lx [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " Ly [ m ]: 2.0\n",
- " Ny [---]: 100.0\n",
- " T [ s ]: 1.0\n",
- " Nt [---]: 1000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dy [ m ]: 2.00e-02\n",
- " dt [ s ]: 1.00e-03\n",
- "Using central difference?: True\n",
- "Solution shape p_all[t_i]: (100, 100)\n",
- "Total time steps in p_all: 1001\n",
- "CFL, direction x: 2.50e-01\n",
- "CFL, C_N, direction y: 2.50e-01\n"
- ]
- },
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- "<Figure size 1100x700 with 2 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
+ "outputs": [],
"source": [
"T = 1\n",
"Nt = 1000\n",
@@ -862,66 +721,12 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"id": "94e5c840-0373-451f-af12-2ac58138cc5e",
"metadata": {
"tags": []
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " cx [m/s]: 5.00\n",
- " cy [m/s]: 5.00\n",
- " Lx [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " Ly [ m ]: 2.0\n",
- " Ny [---]: 100.0\n",
- " T [ s ]: 1.0\n",
- " Nt [---]: 1000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dy [ m ]: 2.00e-02\n",
- " dt [ s ]: 1.00e-03\n",
- "Using central difference?: True\n",
- "Solution shape p_all[t_i]: (100, 100)\n",
- "Total time steps in p_all: 1001\n",
- "CFL, direction x: 2.50e-01\n",
- "CFL, C_N, direction y: 2.50e-01\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "579e4599d5c24c10a1e247dd3066bdf6",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Play(value=0, description='step', max=999), Output()), _dom_classes=('widget-interact',)…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "10df980a3f3c4056bc8844a88324f703",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(IntSlider(value=0, max=999),))"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"X, Y, p_all = run_simulation_2D(p0, cx, cy, Lx, Nx, Ly, Ny, T, Nt, dx, dy, dt, central)\n",
"plot_2D_all()"
@@ -945,61 +750,7 @@
"execution_count": null,
"id": "96aec400-4cc3-412d-863d-9be6c2462f54",
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current variables values:\n",
- " p0 [---]: 2.00\n",
- " cx [m/s]: 5.00\n",
- " cy [m/s]: 5.00\n",
- " Lx [ m ]: 2.0\n",
- " Nx [---]: 100.0\n",
- " Ly [ m ]: 2.0\n",
- " Ny [---]: 100.0\n",
- " T [ s ]: 1.0\n",
- " Nt [---]: 1000.0\n",
- " dx [ m ]: 2.00e-02\n",
- " dy [ m ]: 2.00e-02\n",
- " dt [ s ]: 1.00e-03\n",
- "Using central difference?: True\n",
- "Solution shape p_all[t_i]: (100, 100)\n",
- "Total time steps in p_all: 1001\n",
- "CFL, direction x: 2.50e-01\n",
- "CFL, C_N, direction y: 2.50e-01\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8dbe120ff96440c6bf9f9545f9da97c2",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "interactive(children=(Play(value=0, description='step', max=999), Output()), _dom_classes=('widget-interact',)…"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "385631fb1c7f40319c57f1e9fdf2a6a1",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "HBox(children=(IntSlider(value=0, max=999),))"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"X, Y, p_all = run_simulation_2D(p0, cx, cy, Lx, Nx, Ly, Ny, T, Nt, dx, dy, dt, central=False)\n",
"plot_2D_all()"
diff --git a/src/teachers/Week_2_1/WS_2_1_solution.html b/src/teachers/Week_2_1/WS_2_1_solution.html
index 4b7fa9d2..0346f767 100644
--- a/src/teachers/Week_2_1/WS_2_1_solution.html
+++ b/src/teachers/Week_2_1/WS_2_1_solution.html
@@ -7580,7 +7580,7 @@ a.anchor-link {
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [40]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="o">%%HTML</span>
@@ -7663,13 +7663,13 @@ Nx -> Nx, Ny
</div>
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</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="Task-1:-Implement-Central-Difference">Task 1: Implement Central Difference<a class="anchor-link" href="#Task-1:-Implement-Central-Difference">¶</a></h2><p>We are going to implement the central difference scheme as derived in the textbook; however, <strong>instead of implementing the system of equations in a matrix formulation</strong>, we will <em>loop over each of the finite volumes in the system,</em> one at a time.</p>
+<h2 id="Part-1:-Implement-Central-Difference">Part 1: Implement Central Difference<a class="anchor-link" href="#Part-1:-Implement-Central-Difference">¶</a></h2><p>We are going to implement the central difference scheme as derived in the textbook; however, <strong>instead of implementing the system of equations in a matrix formulation</strong>, we will <em>loop over each of the finite volumes in the system,</em> one at a time.</p>
<p>Because we will want to watch the "pulse" travel over a long period of time, we will take advantage of the reverse-indexing of Python (i.e., the fact that an index <code>a[-3]</code>, for example, will access the third item from the end of the array or list). When taking the volumes to the left of the first volume in direction $x$, we can use the values of $phi$ from the "last" volumes in $x$ (the end of the array). All we need to do is shift the index for $phi_i$ such that we avoid a situation where <code>i+1</code> "breaks" the loop (because the maximum index is <code>i</code>). In other words, only volumes with index <code>i</code> or smaller should be used (e.g., instead of <code>i-1</code>, <code>i</code>, and <code>i+1</code>, use <code>i-2</code>, <code>i-1</code> and <code>i</code>.</p>
</div>
</div>
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</div>
-<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=462bbd4d-18ba-453c-bb08-7d4cc04d6576">
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@@ -7678,17 +7678,34 @@ Nx -> Nx, Ny
<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
+<p>Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).</p>
+$$
+\frac{\partial \phi}{\partial t} + c\frac{\partial \phi}{\partial x} = 0
+$$</p></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a475d0d7">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
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+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
+<p>
+<b>Task 1.2:</b>
<p><b>Read.</b> Read the code to understand the problem that has been set up for you. Check the arguments and return values; the docstrings are purposefully ommitted so you can focus on the code. You might as well re-read the instructions above one more time, as well (let's be honest, you probably just skimmed over it anyway...)</p>
</p></div>
</div>
</div>
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=ba55cdd8">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=b1465443">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [2]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [41]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
@@ -7703,23 +7720,6 @@ Nx -> Nx, Ny
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-<p>
-<b>Task 1.2:</b>
-<p>Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).</p>
-$$
-\frac{\partial \phi}{\partial t} + c\frac{\partial \phi}{\partial x} = 0
-$$</p></div>
-</div>
-</div>
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@@ -7735,11 +7735,20 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Initialize 1D advection simulation.</span>
+
+<span class="sd"> Arguments are defined elsewhere, except one keyword argument</span>
+<span class="sd"> defines the shape of the initial condition.</span>
+<span class="sd"> </span>
+<span class="sd"> square : bool</span>
+<span class="sd"> - specifies a square pulse if True</span>
+<span class="sd"> - specifies a Gaussian shape if False</span>
+<span class="sd"> """</span>
+
<span class="n">dx</span> <span class="o">=</span> <span class="n">L</span><span class="o">/</span><span class="n">Nx</span>
<span class="n">dt</span> <span class="o">=</span> <span class="n">T</span><span class="o">/</span><span class="n">Nt</span>
@@ -7759,7 +7768,7 @@ $$</p></div>
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">p_all</span>
<span class="k">def</span> <span class="nf">advection_1D</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">central</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Solve the advection problem."""</span>
<span class="n">p_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">Nx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Nx</span><span class="p">):</span>
<span class="k">if</span> <span class="n">central</span><span class="p">:</span>
@@ -7772,7 +7781,8 @@ $$</p></div>
<span class="k">def</span> <span class="nf">run_simulation_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span>
<span class="n">central</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Run sumulation by evaluating each time step."""</span>
+
<span class="n">x</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">initialize_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="n">square</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Nt</span><span class="p">):</span>
@@ -7782,6 +7792,7 @@ $$</p></div>
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">p_all</span>
<span class="k">def</span> <span class="nf">plot_1D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w"> </span><span class="sd">"""Plot phi(x, t) at a given time step."""</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">xlim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">())),</span>
<span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
@@ -7792,6 +7803,7 @@ $$</p></div>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">plot_1D_all</span><span class="p">():</span>
+<span class="w"> </span><span class="sd">"""Create animation of phi(x, t) for all t."""</span>
<span class="n">check_variables_1D</span><span class="p">()</span>
<span class="n">play</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Play</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">Nt</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -7804,6 +7816,10 @@ $$</p></div>
<span class="k">return</span> <span class="n">widgets</span><span class="o">.</span><span class="n">HBox</span><span class="p">([</span><span class="n">slider</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">check_variables_1D</span><span class="p">():</span>
+<span class="w"> </span><span class="sd">"""Print current variable values.</span>
+<span class="sd"> </span>
+<span class="sd"> Students define CFL.</span>
+<span class="sd"> """</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Current variables values:'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' p0 [---]: </span><span class="si">{</span><span class="n">p0</span><span class="si">:</span><span class="s1">0.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' c [m/s]: </span><span class="si">{</span><span class="n">c</span><span class="si">:</span><span class="s1">0.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
@@ -7815,7 +7831,11 @@ $$</p></div>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' dt [ s ]: </span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">0.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Using central difference?: </span><span class="si">{</span><span class="n">central</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Using square init. cond.?: </span><span class="si">{</span><span class="n">square</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'CFL: </span><span class="si">{</span><span class="n">c</span><span class="o">*</span><span class="n">dt</span><span class="o">/</span><span class="n">dx</span><span class="si">:</span><span class="s1">.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+ <span class="n">calculated_CFL</span> <span class="o">=</span> <span class="n">c</span><span class="o">*</span><span class="n">dt</span><span class="o">/</span><span class="n">dx</span>
+ <span class="k">if</span> <span class="n">calculated_CFL</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s1">'CFL not calculated yet.'</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'CFL: </span><span class="si">{</span><span class="n">calculated_CFL</span><span class="si">:</span><span class="s1">.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
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@@ -7855,7 +7875,7 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">case_create</span><span class="p">(</span><span class="n">p0</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="mf">5.0</span><span class="p">,</span> <span class="n">L</span><span class="o">=</span><span class="mf">2.0</span><span class="p">,</span> <span class="n">Nx</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
@@ -7906,7 +7926,7 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">C</span> <span class="o">=</span> <span class="p">[]</span>
@@ -7948,7 +7968,7 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">p0</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">central</span><span class="p">,</span> <span class="n">square</span> <span class="o">=</span> <span class="n">case_set</span><span class="p">(</span><span class="n">C</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
@@ -8013,7 +8033,7 @@ CFL: 1.00e+00
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">p0</span> <span class="o">=</span> <span class="mf">2.0</span>
@@ -8050,7 +8070,7 @@ CFL: 1.00e+00
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">check_variables_1D</span><span class="p">()</span>
@@ -8100,7 +8120,7 @@ CFL: 1.00e-01
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_1D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p_all</span><span class="p">)</span>
@@ -8137,7 +8157,7 @@ CFL: 1.00e-01
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_1D_all</span><span class="p">()</span>
@@ -8175,7 +8195,7 @@ CFL: 1.00e-01
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@@ -8183,13 +8203,28 @@ CFL: 1.00e-01
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+<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
+<p>
+<b>Task 1.4:</b>
+If you have not already done so, modify the function above to calculate the CFL and rerun the cells to check the values. Can you explain the effect you see when running the animation from <code>plot_1D_all()</code>?
+</p></div>
+</div>
+</div>
+</div>
+</div>
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-<h2 id="Task-2:-Central-Difference-issues!">Task 2: Central Difference issues!<a class="anchor-link" href="#Task-2:-Central-Difference-issues!">¶</a></h2><p>The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\phi$ values.</p>
+<h2 id="Part-2:-Central-Difference-issues!">Part 2: Central Difference issues!<a class="anchor-link" href="#Part-2:-Central-Difference-issues!">¶</a></h2><p>The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\phi$ values.</p>
<p>You might think that the initial abrupt edges of the square wave is responsible for the instability. You can test this by replacing the square pulse with a smooth one.</p>
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@@ -8214,7 +8249,7 @@ Run the 1D simulation again using a smooth pulse. You can do this by changing th
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">square</span><span class="o">=</span><span class="kc">False</span>
@@ -8254,7 +8289,7 @@ CFL: 1.00e-01
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<pre>HBox(children=(IntSlider(value=0, max=99999),))</pre>
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@@ -8268,7 +8303,7 @@ CFL: 1.00e-01
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-<h2 id="Task-3:-Upwind-scheme">Task 3: Upwind scheme<a class="anchor-link" href="#Task-3:-Upwind-scheme">¶</a></h2><p>More weight can be given to the upwind cells by choosing $\phi$ values for the East face $\phi_i$, and for the West face, use $\phi_{i-1}$. This holds true for positive flow directions. For negative flow directions, you should choose $\phi$ values for the East face $\phi_{i-1}$, and for the West face, use $\phi_{i}$. Note that this is less accurate than the central diffence approach but it will ensure stability.</p>
+<h2 id="Part-3:-Upwind-scheme">Part 3: Upwind scheme<a class="anchor-link" href="#Part-3:-Upwind-scheme">¶</a></h2><p>More weight can be given to the upwind cells by choosing $\phi$ values for the East face $\phi_i$, and for the West face, use $\phi_{i-1}$. This holds true for positive flow directions. For negative flow directions, you should choose $\phi$ values for the East face $\phi_{i-1}$, and for the West face, use $\phi_{i}$. Note that this is less accurate than the central diffence approach but it will ensure stability.</p>
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@@ -8292,7 +8327,7 @@ CFL: 1.00e-01
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@@ -8334,7 +8369,7 @@ CFL: 1.00e-01
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@@ -8348,7 +8383,7 @@ CFL: 1.00e-01
</div>
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-<h2 id="Task-4:-Stability-Analysis-and-False-Diffusion">Task 4: Stability Analysis and False Diffusion<a class="anchor-link" href="#Task-4:-Stability-Analysis-and-False-Diffusion">¶</a></h2><p>Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:
+<h2 id="Part-4:-Stability-Analysis-and-False-Diffusion">Part 4: Stability Analysis and False Diffusion<a class="anchor-link" href="#Part-4:-Stability-Analysis-and-False-Diffusion">¶</a></h2><p>Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:
$$
CFL = \frac{c \Delta t}{\Delta x} \leq 1
$$</p>
@@ -8365,7 +8400,7 @@ $$</p>
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<p>
-<b>Task 4:</b>
+<b>Task 4.1:</b>
Change the Python variables that define the problem and check to see if the CFL predicts stability of the method. You are welcome to explore both discretization schemes and pulse types; there is no "right answer" to this question, except confirming that large CFL causes instability, and vice-versa.
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@@ -8376,7 +8411,7 @@ Change the Python variables that define the problem and check to see if the CFL
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@@ -8405,7 +8440,7 @@ Change the Python variables that define the problem and check to see if the CFL
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">x</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">run_simulation_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">central</span><span class="p">,</span> <span class="n">square</span><span class="p">)</span>
@@ -8444,7 +8479,7 @@ CFL: 2.00e-02
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@@ -8460,7 +8495,7 @@ CFL: 2.00e-02
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<p>
-<b>Task 4.1:</b>
+<b>Task 4.2:</b>
<p>You probably found that for CFL equal to 1, the solution is exact: the wave propagates without deforming. However, a complex flow has variable speed in time and space. That is why in practice a conservative CFL value is chosen for the largest expected flow velocities. Make sure to test a CFL condition of 0.8 to visualize the impact on the numerical solution.</p>
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@@ -8487,7 +8522,7 @@ CFL: 2.00e-02
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-<h2 id="Task-5:-2D-Implementation-in-Python">Task 5: 2D Implementation in Python<a class="anchor-link" href="#Task-5:-2D-Implementation-in-Python">¶</a></h2>
+<h2 id="Part-5:-2D-Implementation-in-Python">Part 5: 2D Implementation in Python<a class="anchor-link" href="#Part-5:-2D-Implementation-in-Python">¶</a></h2>
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@@ -8500,7 +8535,7 @@ CFL: 2.00e-02
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<p>
-<b>Task 5:</b>
+<b>Task 5.1:</b>
Apply FVM by hand to the 2D advection equation. The volumes are rectangular. This is a good example of an exam problem.
$$
\frac{\partial \phi}{\partial t} + c_x\frac{\partial \phi}{\partial x} + c_y\frac{\partial \phi}{\partial y} = 0
@@ -8517,7 +8552,7 @@ $$</p></div>
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<p>
-<b>Task 6:</b>
+<b>Task 5.2:</b>
The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called "numerical diffusion" --- when the numerical scheme causes the square pulse to "diffuse" into a bell shaped surface. Even though only the advection term was implmented!
</p></div>
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@@ -8543,7 +8578,7 @@ The code is set up in a very similar way to the 1D case above. Use it to explore
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">p0</span> <span class="o">=</span> <span class="mf">2.0</span>
@@ -8572,7 +8607,7 @@ The code is set up in a very similar way to the 1D case above. Use it to explore
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize_2D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">Lx</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">Ly</span><span class="p">,</span> <span class="n">Ny</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">):</span>
@@ -8678,7 +8713,7 @@ The code is set up in a very similar way to the 1D case above. Use it to explore
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<pre><>:47: SyntaxWarning: invalid escape sequence '\p'
<>:47: SyntaxWarning: invalid escape sequence '\p'
-C:\Users\rlanzafame\AppData\Local\Temp\ipykernel_18720\2412768473.py:47: SyntaxWarning: invalid escape sequence '\p'
+C:\Users\rlanzafame\AppData\Local\Temp\ipykernel_26208\2412768473.py:47: SyntaxWarning: invalid escape sequence '\p'
ax.set_zlabel('$\phi$ [-]')
</pre>
</div>
@@ -8705,7 +8740,7 @@ Here you can check the Python variable assignments and visualize the initial con
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">T</span> <span class="o">=</span> <span class="mi">1</span>
@@ -8775,7 +8810,7 @@ By default the central difference scheme is used, so you should see the numerica
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">run_simulation_2D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">Lx</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">Ly</span><span class="p">,</span> <span class="n">Ny</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">central</span><span class="p">)</span>
@@ -8820,7 +8855,7 @@ CFL, direction y: 2.50e-01
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@@ -8847,7 +8882,7 @@ Switching to the upwind scheme gives a stable solution. Note the numerical diffu
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">run_simulation_2D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">Lx</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">Ly</span><span class="p">,</span> <span class="n">Ny</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">central</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
@@ -8892,7 +8927,7 @@ CFL, direction y: 2.50e-01
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diff --git a/src/teachers/Week_2_1/WS_2_1_wiggle.html b/src/teachers/Week_2_1/WS_2_1_wiggle.html
index ab55db98..5b48dd6f 100644
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+++ b/src/teachers/Week_2_1/WS_2_1_wiggle.html
@@ -7567,13 +7567,13 @@ Nx -> Nx, Ny
</div>
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-<h2 id="Task-1:-Implement-Central-Difference">Task 1: Implement Central Difference<a class="anchor-link" href="#Task-1:-Implement-Central-Difference">¶</a></h2><p>We are going to implement the central difference scheme as derived in the textbook; however, <strong>instead of implementing the system of equations in a matrix formulation</strong>, we will <em>loop over each of the finite volumes in the system,</em> one at a time.</p>
+<h2 id="Part-1:-Implement-Central-Difference">Part 1: Implement Central Difference<a class="anchor-link" href="#Part-1:-Implement-Central-Difference">¶</a></h2><p>We are going to implement the central difference scheme as derived in the textbook; however, <strong>instead of implementing the system of equations in a matrix formulation</strong>, we will <em>loop over each of the finite volumes in the system,</em> one at a time.</p>
<p>Because we will want to watch the "pulse" travel over a long period of time, we will take advantage of the reverse-indexing of Python (i.e., the fact that an index <code>a[-3]</code>, for example, will access the third item from the end of the array or list). When taking the volumes to the left of the first volume in direction $x$, we can use the values of $phi$ from the "last" volumes in $x$ (the end of the array). All we need to do is shift the index for $phi_i$ such that we avoid a situation where <code>i+1</code> "breaks" the loop (because the maximum index is <code>i</code>). In other words, only volumes with index <code>i</code> or smaller should be used (e.g., instead of <code>i-1</code>, <code>i</code>, and <code>i+1</code>, use <code>i-2</code>, <code>i-1</code> and <code>i</code>.</p>
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<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
<p>
<b>Task 1.1:</b>
+<p>Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).</p>
+$$
+\frac{\partial \phi}{\partial t} + c\frac{\partial \phi}{\partial x} = 0
+$$</p></div>
+</div>
+</div>
+</div>
+</div>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=4539029d">
+<div class="jp-Cell-inputWrapper" tabindex="0">
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+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
+<p>
+<b>Task 1.2:</b>
<p><b>Read.</b> Read the code to understand the problem that has been set up for you. Check the arguments and return values; the docstrings are purposefully ommitted so you can focus on the code. You might as well re-read the instructions above one more time, as well (let's be honest, you probably just skimmed over it anyway...)</p>
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@@ -7607,23 +7624,6 @@ Nx -> Nx, Ny
</div>
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-<p>
-<b>Task 1.2:</b>
-<p>Write by hand for FMV the <code>advection_1D</code> equation, compute the convective fluxes of $\phi$ at the surfaces using a linear interpolation (central averaging). Then apply Forward Euler in time to the resulting ODE. Make sure you use the right indexing (maximum index should be <code>i</code>).</p>
-$$
-\frac{\partial \phi}{\partial t} + c\frac{\partial \phi}{\partial x} = 0
-$$</p></div>
-</div>
-</div>
-</div>
-</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">initialize_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Initialize 1D advection simulation.</span>
+
+<span class="sd"> Arguments are defined elsewhere, except one keyword argument</span>
+<span class="sd"> defines the shape of the initial condition.</span>
+
+<span class="sd"> square : bool</span>
+<span class="sd"> - specifies a square pulse if True</span>
+<span class="sd"> - specifies a Gaussian shape if False</span>
+<span class="sd"> """</span>
+
<span class="n">dx</span> <span class="o">=</span> <span class="n">L</span><span class="o">/</span><span class="n">Nx</span>
<span class="n">dt</span> <span class="o">=</span> <span class="n">T</span><span class="o">/</span><span class="n">Nt</span>
<span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">dx</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">L</span> <span class="o">-</span> <span class="n">dx</span><span class="o">/</span><span class="mi">2</span><span class="p">,</span> <span class="n">Nx</span><span class="p">)</span>
- <span class="c1"># Initialise domain:</span>
- <span class="c1"># - a square pulse with p0 between 0.5 and 1</span>
- <span class="c1"># - a Gaussian shape around 1</span>
+
<span class="k">if</span> <span class="n">square</span><span class="p">:</span>
<span class="n">p_init</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">Nx</span><span class="p">)</span>
<span class="n">p_init</span><span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="mf">.5</span><span class="o">/</span><span class="n">dx</span><span class="p">):</span><span class="nb">int</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">dx</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">p0</span>
@@ -7678,7 +7685,7 @@ $$</p></div>
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">p_all</span>
<span class="k">def</span> <span class="nf">advection_1D</span><span class="p">(</span><span class="n">p</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">central</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Solve the advection problem."""</span>
<span class="n">p_new</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">Nx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">Nx</span><span class="p">):</span>
<span class="k">if</span> <span class="n">central</span><span class="p">:</span>
@@ -7689,7 +7696,8 @@ $$</p></div>
<span class="k">def</span> <span class="nf">run_simulation_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span>
<span class="n">central</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
-
+<span class="w"> </span><span class="sd">"""Run sumulation by evaluating each time step."""</span>
+
<span class="n">x</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">initialize_1D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">square</span><span class="o">=</span><span class="n">square</span><span class="p">)</span>
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">Nt</span><span class="p">):</span>
@@ -7699,6 +7707,7 @@ $$</p></div>
<span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">p_all</span>
<span class="k">def</span> <span class="nf">plot_1D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w"> </span><span class="sd">"""Plot phi(x, t) at a given time step."""</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">(</span><span class="n">xlim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">round</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">max</span><span class="p">())),</span>
<span class="n">ylim</span><span class="o">=</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()))</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
@@ -7709,6 +7718,7 @@ $$</p></div>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">def</span> <span class="nf">plot_1D_all</span><span class="p">():</span>
+<span class="w"> </span><span class="sd">"""Create animation of phi(x, t) for all t."""</span>
<span class="n">check_variables_1D</span><span class="p">()</span>
<span class="n">play</span> <span class="o">=</span> <span class="n">widgets</span><span class="o">.</span><span class="n">Play</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">Nt</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">value</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
@@ -7721,6 +7731,10 @@ $$</p></div>
<span class="k">return</span> <span class="n">widgets</span><span class="o">.</span><span class="n">HBox</span><span class="p">([</span><span class="n">slider</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">check_variables_1D</span><span class="p">():</span>
+<span class="w"> </span><span class="sd">"""Print current variable values.</span>
+<span class="sd"> </span>
+<span class="sd"> Students define CFL.</span>
+<span class="sd"> """</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Current variables values:'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' p0 [---]: </span><span class="si">{</span><span class="n">p0</span><span class="si">:</span><span class="s1">0.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' c [m/s]: </span><span class="si">{</span><span class="n">c</span><span class="si">:</span><span class="s1">0.2f</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
@@ -7732,7 +7746,11 @@ $$</p></div>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">' dt [ s ]: </span><span class="si">{</span><span class="n">dt</span><span class="si">:</span><span class="s1">0.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Using central difference?: </span><span class="si">{</span><span class="n">central</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Using square init. cond.?: </span><span class="si">{</span><span class="n">square</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'CFL: </span><span class="si">{</span><span class="n">c</span><span class="o">*</span><span class="n">dt</span><span class="o">/</span><span class="n">dx</span><span class="si">:</span><span class="s1">.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
+ <span class="n">calculated_CFL</span> <span class="o">=</span> <span class="kc">None</span>
+ <span class="k">if</span> <span class="n">calculated_CFL</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="s1">'CFL not calculated yet.'</span><span class="p">)</span>
+ <span class="k">else</span><span class="p">:</span>
+ <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'CFL: </span><span class="si">{</span><span class="n">calculated_CFL</span><span class="si">:</span><span class="s1">.2e</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
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@@ -7754,7 +7772,7 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">p0</span> <span class="o">=</span> <span class="mf">2.0</span>
@@ -7786,12 +7804,12 @@ $$</p></div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">check_variables_1D</span><span class="p">()</span>
@@ -7801,30 +7819,6 @@ $$</p></div>
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-<pre>Current variables values:
- p0 [---]: 2.00
- c [m/s]: 5.00
- L [ m ]: 2.0
- Nx [---]: 100.0
- T [ s ]: 40.0
- Nt [---]: 100000.0
- dx [ m ]: 2.00e-02
- dt [ s ]: 4.00e-04
-Using central difference?: True
-Using square init. cond.?: True
-CFL: 1.00e-01
-</pre>
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@@ -7836,12 +7830,12 @@ CFL: 1.00e-01
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_1D</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">p_all</span><span class="p">)</span>
@@ -7850,18 +7844,6 @@ CFL: 1.00e-01
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"/>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f46abdd9-1c87-46a7-8994-e8e35346ce7a">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -7873,12 +7855,12 @@ CFL: 1.00e-01
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=06742685-1f3c-43d1-8657-86a0d324b79f">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=06742685-1f3c-43d1-8657-86a0d324b79f">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">plot_1D_all</span><span class="p">()</span>
@@ -7887,39 +7869,18 @@ CFL: 1.00e-01
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Current variables values:
- p0 [---]: 2.00
- c [m/s]: 5.00
- L [ m ]: 2.0
- Nx [---]: 100.0
- T [ s ]: 40.0
- Nt [---]: 100000.0
- dx [ m ]: 2.00e-02
- dt [ s ]: 4.00e-04
-Using central difference?: True
-Using square init. cond.?: True
-CFL: 1.00e-01
-</pre>
-</div>
</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(Play(value=0, description='step', max=99999), Output()), _dom_classes=('widget-interact'…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>HBox(children=(IntSlider(value=0, max=99999),))</pre>
+<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=30c0fb7b">
+<div class="jp-Cell-inputWrapper" tabindex="0">
+<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
+<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
+</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
+<div style="background-color:#AABAB2; color: black; width: 95%; vertical-align: middle; padding:15px; margin: 10px; border-radius: 10px">
+<p>
+<b>Task 1.4:</b>
+If you have not already done so, modify the function above to calculate the CFL and rerun the cells to check the values. Can you explain the effect you see when running the animation from <code>plot_1D_all()</code>?
+</p></div>
</div>
</div>
</div>
@@ -7930,7 +7891,7 @@ CFL: 1.00e-01
</div>
<div class="jp-InputArea jp-Cell-inputArea"><div class="jp-InputPrompt jp-InputArea-prompt">
</div><div class="jp-RenderedHTMLCommon jp-RenderedMarkdown jp-MarkdownOutput" data-mime-type="text/markdown">
-<h2 id="Task-2:-Central-Difference-issues!">Task 2: Central Difference issues!<a class="anchor-link" href="#Task-2:-Central-Difference-issues!">¶</a></h2><p>The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\phi$ values.</p>
+<h2 id="Part-2:-Central-Difference-issues!">Part 2: Central Difference issues!<a class="anchor-link" href="#Part-2:-Central-Difference-issues!">¶</a></h2><p>The discretization is unstable (regardless of the time step used), largely due to weighting equally the influence by adjacent volumes in the fluxes. The hyperbolic nature of the equation implies that more weight should be given to the upstream/upwind $\phi$ values.</p>
<p>You might think that the initial abrupt edges of the square wave is responsible for the instability. You can test this by replacing the square pulse with a smooth one.</p>
</div>
</div>
@@ -7950,12 +7911,12 @@ Run the 1D simulation again using a smooth pulse. You can do this by changing th
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=8672fa59-e451-4271-8d12-470b8c42df67">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=8672fa59-e451-4271-8d12-470b8c42df67">
<div class="jp-Cell-inputWrapper" tabindex="0">
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
</div>
<div class="jp-InputArea jp-Cell-inputArea">
-<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">square</span><span class="o">=</span><span class="kc">False</span>
@@ -7966,42 +7927,6 @@ Run the 1D simulation again using a smooth pulse. You can do this by changing th
</div>
</div>
</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Current variables values:
- p0 [---]: 2.00
- c [m/s]: 5.00
- L [ m ]: 2.0
- Nx [---]: 100.0
- T [ s ]: 40.0
- Nt [---]: 100000.0
- dx [ m ]: 2.00e-02
- dt [ s ]: 4.00e-04
-Using central difference?: True
-Using square init. cond.?: False
-CFL: 1.00e-01
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(Play(value=0, description='step', max=99999), Output()), _dom_classes=('widget-interact'…</pre>
-</div>
-</div>
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@@ -8033,7 +7958,7 @@ CFL: 1.00e-01
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># re-define key variables and use run_simulation_1D() and plot_1D_all()</span>
@@ -8049,7 +7974,7 @@ CFL: 1.00e-01
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-<h2 id="Task-4:-Stability-Analysis-and-False-Diffusion">Task 4: Stability Analysis and False Diffusion<a class="anchor-link" href="#Task-4:-Stability-Analysis-and-False-Diffusion">¶</a></h2><p>Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:
+<h2 id="Part-4:-Stability-Analysis-and-False-Diffusion">Part 4: Stability Analysis and False Diffusion<a class="anchor-link" href="#Part-4:-Stability-Analysis-and-False-Diffusion">¶</a></h2><p>Now, let’s play with the code. In convective kinematics a von Neumann analysis on the advection equation suggests that the following must hold for stability:
$$
CFL = \frac{c \Delta t}{\Delta x} \leq 1
$$</p>
@@ -8066,7 +7991,7 @@ $$</p>
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<p>
-<b>Task 4:</b>
+<b>Task 4.1:</b>
Change the Python variables that define the problem and check to see if the CFL predicts stability of the method. You are welcome to explore both discretization schemes and pulse types; there is no "right answer" to this question, except confirming that large CFL causes instability, and vice-versa.
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@@ -8077,7 +8002,7 @@ Change the Python variables that define the problem and check to see if the CFL
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<div class="highlight hl-ipython3"><pre><span></span><span class="c1"># re-define key variables and use run_simulation_1D() and plot_1D_all()</span>
@@ -8095,7 +8020,7 @@ Change the Python variables that define the problem and check to see if the CFL
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<p>
-<b>Task 4.1:</b>
+<b>Task 4.2:</b>
<p>You probably found that for CFL equal to 1, the solution is exact: the wave propagates without deforming. However, a complex flow has variable speed in time and space. That is why in practice a conservative CFL value is chosen for the largest expected flow velocities. Make sure to test a CFL condition of 0.8 to visualize the impact on the numerical solution.</p>
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@@ -8106,7 +8031,7 @@ Change the Python variables that define the problem and check to see if the CFL
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@@ -8122,7 +8047,7 @@ Change the Python variables that define the problem and check to see if the CFL
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-<h2 id="Task-5:-2D-Implementation-in-Python">Task 5: 2D Implementation in Python<a class="anchor-link" href="#Task-5:-2D-Implementation-in-Python">¶</a></h2>
+<h2 id="Part-5:-2D-Implementation-in-Python">Part 5: 2D Implementation in Python<a class="anchor-link" href="#Part-5:-2D-Implementation-in-Python">¶</a></h2>
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@@ -8135,7 +8060,7 @@ Change the Python variables that define the problem and check to see if the CFL
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<p>
-<b>Task 5:</b>
+<b>Task 5.1:</b>
Apply FVM by hand to the 2D advection equation. The volumes are rectangular. This is a good example of an exam problem.
$$
\frac{\partial \phi}{\partial t} + c_x\frac{\partial \phi}{\partial x} + c_y\frac{\partial \phi}{\partial y} = 0
@@ -8152,7 +8077,7 @@ $$</p></div>
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<p>
-<b>Task 6:</b>
+<b>Task 5.2:</b>
The code is set up in a very similar way to the 1D case above. Use it to explore how the advection problem works in 2D! In particular, see if you observe the effect called "numerical diffusion" --- when the numerical scheme causes the square pulse to "diffuse" into a bell shaped surface. Even though only the advection term was implmented!
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-<pre>Current variables values:
- p0 [---]: 2.00
- cx [m/s]: 5.00
- cy [m/s]: 5.00
- Lx [ m ]: 2.0
- Nx [---]: 100.0
- Ly [ m ]: 2.0
- Ny [---]: 100.0
- T [ s ]: 1.0
- Nt [---]: 1000.0
- dx [ m ]: 2.00e-02
- dy [ m ]: 2.00e-02
- dt [ s ]: 1.00e-03
-Using central difference?: True
-Solution shape p_all[t_i]: (100, 100)
-Total time steps in p_all: 1001
-CFL, direction x: 2.50e-01
-CFL, C_N, direction y: 2.50e-01
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"/>
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-</div>
-</div>
-</div>
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<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=a2198cbe-422a-44b4-9bff-d3479befaa43">
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@@ -8405,12 +8278,12 @@ By default the central difference scheme is used, so you should see the numerica
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-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=94e5c840-0373-451f-af12-2ac58138cc5e">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=94e5c840-0373-451f-af12-2ac58138cc5e">
<div class="jp-Cell-inputWrapper" tabindex="0">
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-<div class="jp-InputPrompt jp-InputArea-prompt">In [14]:</div>
+<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div>
<div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
<div class="cm-editor cm-s-jupyter">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">p_all</span> <span class="o">=</span> <span class="n">run_simulation_2D</span><span class="p">(</span><span class="n">p0</span><span class="p">,</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">Lx</span><span class="p">,</span> <span class="n">Nx</span><span class="p">,</span> <span class="n">Ly</span><span class="p">,</span> <span class="n">Ny</span><span class="p">,</span> <span class="n">T</span><span class="p">,</span> <span class="n">Nt</span><span class="p">,</span> <span class="n">dx</span><span class="p">,</span> <span class="n">dy</span><span class="p">,</span> <span class="n">dt</span><span class="p">,</span> <span class="n">central</span><span class="p">)</span>
@@ -8420,48 +8293,6 @@ By default the central difference scheme is used, so you should see the numerica
</div>
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</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
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-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Current variables values:
- p0 [---]: 2.00
- cx [m/s]: 5.00
- cy [m/s]: 5.00
- Lx [ m ]: 2.0
- Nx [---]: 100.0
- Ly [ m ]: 2.0
- Ny [---]: 100.0
- T [ s ]: 1.0
- Nt [---]: 1000.0
- dx [ m ]: 2.00e-02
- dy [ m ]: 2.00e-02
- dt [ s ]: 1.00e-03
-Using central difference?: True
-Solution shape p_all[t_i]: (100, 100)
-Total time steps in p_all: 1001
-CFL, direction x: 2.50e-01
-CFL, C_N, direction y: 2.50e-01
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(Play(value=0, description='step', max=999), Output()), _dom_classes=('widget-interact',)…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[14]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>HBox(children=(IntSlider(value=0, max=999),))</pre>
-</div>
-</div>
-</div>
-</div>
</div>
<div class="jp-Cell jp-MarkdownCell jp-Notebook-cell" id="cell-id=f29a9aef-f355-45bb-9156-7fcd5d1d547c">
<div class="jp-Cell-inputWrapper" tabindex="0">
@@ -8477,7 +8308,7 @@ Switching to the upwind scheme gives a stable solution. Note the numerical diffu
</div>
</div>
</div>
-</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell" id="cell-id=96aec400-4cc3-412d-863d-9be6c2462f54">
+</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell jp-mod-noOutputs" id="cell-id=96aec400-4cc3-412d-863d-9be6c2462f54">
<div class="jp-Cell-inputWrapper" tabindex="0">
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</div>
@@ -8492,48 +8323,6 @@ Switching to the upwind scheme gives a stable solution. Note the numerical diffu
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</div>
-<div class="jp-Cell-outputWrapper">
-<div class="jp-Collapser jp-OutputCollapser jp-Cell-outputCollapser">
-</div>
-<div class="jp-OutputArea jp-Cell-outputArea">
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>Current variables values:
- p0 [---]: 2.00
- cx [m/s]: 5.00
- cy [m/s]: 5.00
- Lx [ m ]: 2.0
- Nx [---]: 100.0
- Ly [ m ]: 2.0
- Ny [---]: 100.0
- T [ s ]: 1.0
- Nt [---]: 1000.0
- dx [ m ]: 2.00e-02
- dy [ m ]: 2.00e-02
- dt [ s ]: 1.00e-03
-Using central difference?: True
-Solution shape p_all[t_i]: (100, 100)
-Total time steps in p_all: 1001
-CFL, direction x: 2.50e-01
-CFL, C_N, direction y: 2.50e-01
-</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child">
-<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
-<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="text/plain" tabindex="0">
-<pre>interactive(children=(Play(value=0, description='step', max=999), Output()), _dom_classes=('widget-interact',)…</pre>
-</div>
-</div>
-<div class="jp-OutputArea-child jp-OutputArea-executeResult">
-<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[Â ]:</div>
-<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain" tabindex="0">
-<pre>HBox(children=(IntSlider(value=0, max=999),))</pre>
-</div>
-</div>
-</div>
-</div>
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@@ -8550,11 +8339,11 @@ CFL, C_N, direction y: 2.50e-01
article { position: relative }
</style>
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-</img></a>
+<img alt="Creative Commons License" src="https://i.creativecommons.org/l/by/4.0/88x31.png" style="border-width:; width:88px; height:auto; padding-top:10px"/>
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-</img></a>
+<img alt="TU Delft" src="https://gitlab.tudelft.nl/mude/public/-/raw/main/tu-logo/TU_P1_full-color.png" style="border-width:0; width:100px; height:auto; padding-bottom:0px"/>
+</a>
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