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index 4bef7dde30f78b14aa22eab7789913246d106c49..de1a106a918e8a99c192acf564237b9c5460b9af 100644
--- a/public/content/exercises/exercise01.ipynb
+++ b/public/content/exercises/exercise01.ipynb
@@ -130,7 +130,7 @@
"> \\epsilon_{ijk} \\,\\epsilon_{klm} = \\delta_{il} \\,\\delta_{jm} - \\delta_{im} \\, \\delta_{jl}\n",
"> $$"
],
- "id": "f674fd61-fe52-4ab8-a3b1-ad7750cc0412"
+ "id": "b5559e25-7a14-42c2-9e2e-86e9e6e268fd"
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+
+ <ul>
+ <li><a href="#lagrangian-vs-eulerian-description" id="toc-lagrangian-vs-eulerian-description" class="nav-link active" data-scroll-target="#lagrangian-vs-eulerian-description">Lagrangian vs Eulerian description</a>
+ <ul class="collapse">
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+ <ul class="collapse">
+ <li><a href="#task-1" id="toc-task-1" class="nav-link" data-scroll-target="#task-1">Task 1</a></li>
+ <li><a href="#task-2" id="toc-task-2" class="nav-link" data-scroll-target="#task-2">Task 2</a></li>
+ <li><a href="#task-3" id="toc-task-3" class="nav-link" data-scroll-target="#task-3">Task 3</a></li>
+ <li><a href="#task-4" id="toc-task-4" class="nav-link" data-scroll-target="#task-4">Task 4</a></li>
+ </ul></li>
+ </ul></li>
+ <li><a href="#streamlinesstreaklinespathlines" id="toc-streamlinesstreaklinespathlines" class="nav-link" data-scroll-target="#streamlinesstreaklinespathlines">Streamlines/Streaklines/Pathlines</a>
+ <ul class="collapse">
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+ <li><a href="#task-3-1" id="toc-task-3-1" class="nav-link" data-scroll-target="#task-3-1">Task 3</a></li>
+ <li><a href="#task-4-1" id="toc-task-4-1" class="nav-link" data-scroll-target="#task-4-1">Task 4</a></li>
+ </ul></li>
+ </ul></li>
+ </ul>
+<div class="toc-actions"><ul><li><a href="https://git.rwth-aachen.de/mbd/courses/cmm/issues/new" class="toc-action"><i class="bi bi-git"></i>Report an issue</a></li></ul></div><div class="quarto-alternate-formats"><h2>Other Formats</h2><ul><li><a href="exercise02.pdf"><i class="bi bi-file-pdf"></i>PDF</a></li></ul></div></nav>
+ </div>
+<!-- main -->
+<main class="content" id="quarto-document-content"><header id="title-block-header" class="quarto-title-block"><h1 class="title display-7">Exercise 2</h1></header>
+
+<header id="title-block-header">
+
+
+</header>
+
+
+<section id="lagrangian-vs-eulerian-description" class="level1">
+<h1>Lagrangian vs Eulerian description</h1>
+<p>Given the trajectories <span class="math inline">\(\mathbf X(t, \mathbf x_0) = (X(t, \mathbf x_0), Y(t, \mathbf x_0), Z(t, \mathbf x_0))^T\)</span></p>
+<p><span class="math display">\[\begin{align*}
+ X(t, \mathbf x_0) &= x_0 \, cos(t) + y_0 \, sin(t) \\
+ Y(t, \mathbf x_0) &= -x_0 \, sin(t) + y_0 \, cos(t) \\
+ Z(t, \mathbf x_0) &= 0
+\end{align*}\]</span></p>
+<p>with <span class="math inline">\(\mathbf x_0 = (x_0, y_0, z_0)^T\)</span> and the scalar field</p>
+<p><span class="math display">\[
+\phi(\mathbf X(t), t) = x_0^2 + y_0^2
+\]</span></p>
+<section id="tasks" class="level2">
+<h2 class="anchored" data-anchor-id="tasks">Tasks</h2>
+<section id="task-1" class="level3">
+<h3 class="anchored" data-anchor-id="task-1">Task 1</h3>
+<p>Sketch two trajectories for <span class="math inline">\(t \in (0, 2 \pi)\)</span> as well as their respective field values <span class="math inline">\(\phi(\mathbf X(t), t)\)</span> over time.</p>
+</section>
+<section id="task-2" class="level3">
+<h3 class="anchored" data-anchor-id="task-2">Task 2</h3>
+<p>Describe <span class="math inline">\(\phi(\mathbf X(t), t)\)</span> in the Eulerian frame.</p>
+</section>
+<section id="task-3" class="level3">
+<h3 class="anchored" data-anchor-id="task-3">Task 3</h3>
+<p>Consider another field given by <span class="math inline">\(\psi(\mathbf X(t), t) = X^2 + Y^2\)</span>. Rewrite <span class="math inline">\(\psi\)</span> in the Eulerian frame.</p>
+</section>
+<section id="task-4" class="level3">
+<h3 class="anchored" data-anchor-id="task-4">Task 4</h3>
+<p>Compute <span class="math inline">\(\frac{d\phi}{dt}\)</span> and <span class="math inline">\(\frac{D\phi}{dt}\)</span> using their definitions.</p>
+</section>
+</section>
+</section>
+<section id="streamlinesstreaklinespathlines" class="level1">
+<h1>Streamlines/Streaklines/Pathlines</h1>
+<p>Consider the velocity field</p>
+<p><span class="math display">\[\begin{align*}
+ \mathbf{v}(t,x,y) =
+ \begin{pmatrix}
+ u \\
+ v
+ \end{pmatrix}
+ =
+ \begin{pmatrix}
+ - k x + \alpha t \\
+ k y
+ \end{pmatrix}
+\end{align*}\]</span></p>
+<p>with <span class="math inline">\(k, \alpha\)</span> being positive constants.</p>
+<section id="tasks-1" class="level2">
+<h2 class="anchored" data-anchor-id="tasks-1">Tasks</h2>
+<section id="task-1-1" class="level3">
+<h3 class="anchored" data-anchor-id="task-1-1">Task 1</h3>
+<p>Compute the pathlines.</p>
+</section>
+<section id="task-2-1" class="level3">
+<h3 class="anchored" data-anchor-id="task-2-1">Task 2</h3>
+<p>Compute the streamlines.</p>
+</section>
+<section id="task-3-1" class="level3">
+<h3 class="anchored" data-anchor-id="task-3-1">Task 3</h3>
+<p>Compute the streaklines.</p>
+</section>
+<section id="task-4-1" class="level3">
+<h3 class="anchored" data-anchor-id="task-4-1">Task 4</h3>
+<p>Sketch the velocity field for</p>
+<ul>
+<li><span class="math inline">\(t_0 < \frac{k x}{\alpha}\)</span></li>
+<li><span class="math inline">\(t_1 = \frac{k x}{\alpha}\)</span></li>
+<li><span class="math inline">\(t_2 > \frac{k x}{\alpha}\)</span></li>
+</ul>
+<p>and furthermore add</p>
+<ul>
+<li>the pathline for one particular <span class="math inline">\(\mathbf x_0\)</span></li>
+<li>one particular streamline in each figure</li>
+<li>the streakline starting at <span class="math inline">\(t_0\)</span> for one particular <span class="math inline">\(\mathbf x_0\)</span>.</li>
+</ul>
+
+
+</section>
+</section>
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+ window.Quarto.typesetMath(note);
+ }
+ if (note.classList.contains("callout")) {
+ return note.outerHTML;
+ } else {
+ return note.innerHTML;
+ }
+ }
+ }
+ for (var i=0; i<xrefs.length; i++) {
+ const xref = xrefs[i];
+ tippyHover(xref, undefined, function(instance) {
+ instance.disable();
+ let url = xref.getAttribute('href');
+ let hash = undefined;
+ if (url.startsWith('#')) {
+ hash = url;
+ } else {
+ try { hash = new URL(url).hash; } catch {}
+ }
+ if (hash) {
+ const id = hash.replace(/^#\/?/, "");
+ const note = window.document.getElementById(id);
+ if (note !== null) {
+ try {
+ const html = processXRef(id, note.cloneNode(true));
+ instance.setContent(html);
+ } finally {
+ instance.enable();
+ instance.show();
+ }
+ } else {
+ // See if we can fetch this
+ fetch(url.split('#')[0])
+ .then(res => res.text())
+ .then(html => {
+ const parser = new DOMParser();
+ const htmlDoc = parser.parseFromString(html, "text/html");
+ const note = htmlDoc.getElementById(id);
+ if (note !== null) {
+ const html = processXRef(id, note);
+ instance.setContent(html);
+ }
+ }).finally(() => {
+ instance.enable();
+ instance.show();
+ });
+ }
+ } else {
+ // See if we can fetch a full url (with no hash to target)
+ // This is a special case and we should probably do some content thinning / targeting
+ fetch(url)
+ .then(res => res.text())
+ .then(html => {
+ const parser = new DOMParser();
+ const htmlDoc = parser.parseFromString(html, "text/html");
+ const note = htmlDoc.querySelector('main.content');
+ if (note !== null) {
+ // This should only happen for chapter cross references
+ // (since there is no id in the URL)
+ // remove the first header
+ if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+ note.children[0].remove();
+ }
+ const html = processXRef(null, note);
+ instance.setContent(html);
+ }
+ }).finally(() => {
+ instance.enable();
+ instance.show();
+ });
+ }
+ }, function(instance) {
+ });
+ }
+ let selectedAnnoteEl;
+ const selectorForAnnotation = ( cell, annotation) => {
+ let cellAttr = 'data-code-cell="' + cell + '"';
+ let lineAttr = 'data-code-annotation="' + annotation + '"';
+ const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+ return selector;
+ }
+ const selectCodeLines = (annoteEl) => {
+ const doc = window.document;
+ const targetCell = annoteEl.getAttribute("data-target-cell");
+ const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+ const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+ const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+ const lineIds = lines.map((line) => {
+ return targetCell + "-" + line;
+ })
+ let top = null;
+ let height = null;
+ let parent = null;
+ if (lineIds.length > 0) {
+ //compute the position of the single el (top and bottom and make a div)
+ const el = window.document.getElementById(lineIds[0]);
+ top = el.offsetTop;
+ height = el.offsetHeight;
+ parent = el.parentElement.parentElement;
+ if (lineIds.length > 1) {
+ const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
+ const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+ height = bottom - top;
+ }
+ if (top !== null && height !== null && parent !== null) {
+ // cook up a div (if necessary) and position it
+ let div = window.document.getElementById("code-annotation-line-highlight");
+ if (div === null) {
+ div = window.document.createElement("div");
+ div.setAttribute("id", "code-annotation-line-highlight");
+ div.style.position = 'absolute';
+ parent.appendChild(div);
+ }
+ div.style.top = top - 2 + "px";
+ div.style.height = height + 4 + "px";
+ div.style.left = 0;
+ let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+ if (gutterDiv === null) {
+ gutterDiv = window.document.createElement("div");
+ gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+ gutterDiv.style.position = 'absolute';
+ const codeCell = window.document.getElementById(targetCell);
+ const gutter = codeCell.querySelector('.code-annotation-gutter');
+ gutter.appendChild(gutterDiv);
+ }
+ gutterDiv.style.top = top - 2 + "px";
+ gutterDiv.style.height = height + 4 + "px";
+ }
+ selectedAnnoteEl = annoteEl;
+ }
+ };
+ const unselectCodeLines = () => {
+ const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+ elementsIds.forEach((elId) => {
+ const div = window.document.getElementById(elId);
+ if (div) {
+ div.remove();
+ }
+ });
+ selectedAnnoteEl = undefined;
+ };
+ // Handle positioning of the toggle
+ window.addEventListener(
+ "resize",
+ throttle(() => {
+ elRect = undefined;
+ if (selectedAnnoteEl) {
+ selectCodeLines(selectedAnnoteEl);
+ }
+ }, 10)
+ );
+ function throttle(fn, ms) {
+ let throttle = false;
+ let timer;
+ return (...args) => {
+ if(!throttle) { // first call gets through
+ fn.apply(this, args);
+ throttle = true;
+ } else { // all the others get throttled
+ if(timer) clearTimeout(timer); // cancel #2
+ timer = setTimeout(() => {
+ fn.apply(this, args);
+ timer = throttle = false;
+ }, ms);
+ }
+ };
+ }
+ // Attach click handler to the DT
+ const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+ for (const annoteDlNode of annoteDls) {
+ annoteDlNode.addEventListener('click', (event) => {
+ const clickedEl = event.target;
+ if (clickedEl !== selectedAnnoteEl) {
+ unselectCodeLines();
+ const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+ if (activeEl) {
+ activeEl.classList.remove('code-annotation-active');
+ }
+ selectCodeLines(clickedEl);
+ clickedEl.classList.add('code-annotation-active');
+ } else {
+ // Unselect the line
+ unselectCodeLines();
+ clickedEl.classList.remove('code-annotation-active');
+ }
+ });
+ }
+ const findCites = (el) => {
+ const parentEl = el.parentElement;
+ if (parentEl) {
+ const cites = parentEl.dataset.cites;
+ if (cites) {
+ return {
+ el,
+ cites: cites.split(' ')
+ };
+ } else {
+ return findCites(el.parentElement)
+ }
+ } else {
+ return undefined;
+ }
+ };
+ var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+ for (var i=0; i<bibliorefs.length; i++) {
+ const ref = bibliorefs[i];
+ const citeInfo = findCites(ref);
+ if (citeInfo) {
+ tippyHover(citeInfo.el, function() {
+ var popup = window.document.createElement('div');
+ citeInfo.cites.forEach(function(cite) {
+ var citeDiv = window.document.createElement('div');
+ citeDiv.classList.add('hanging-indent');
+ citeDiv.classList.add('csl-entry');
+ var biblioDiv = window.document.getElementById('ref-' + cite);
+ if (biblioDiv) {
+ citeDiv.innerHTML = biblioDiv.innerHTML;
+ }
+ popup.appendChild(citeDiv);
+ });
+ return popup.innerHTML;
+ });
+ }
+ }
+});
+</script>
+</div> <!-- /content -->
+
+
+
+
+<footer class="footer"><div class="nav-footer"><div class="nav-footer-center"><div class="toc-actions d-sm-block d-md-none"><ul><li><a href="https://git.rwth-aachen.de/mbd/courses/cmm/issues/new" class="toc-action"><i class="bi bi-git"></i>Report an issue</a></li></ul></div></div></div></footer></body></html>
\ No newline at end of file
diff --git a/public/content/exercises/exercise02.ipynb b/public/content/exercises/exercise02.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..cd00df0f18e2f45a4b338ea23b6a45428b0f9f92
--- /dev/null
+++ b/public/content/exercises/exercise02.ipynb
@@ -0,0 +1,98 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Lagrangian vs Eulerian description\n",
+ "\n",
+ "Given the trajectories\n",
+ "$\\mathbf X(t, \\mathbf x_0) = (X(t, \\mathbf x_0), Y(t, \\mathbf x_0), Z(t, \\mathbf x_0))^T$\n",
+ "\n",
+ "with $\\mathbf x_0 = (x_0, y_0, z_0)^T$ and the scalar field\n",
+ "\n",
+ "$$\n",
+ "\\phi(\\mathbf X(t), t) = x_0^2 + y_0^2\n",
+ "$$\n",
+ "\n",
+ "## Tasks\n",
+ "\n",
+ "### Task 1\n",
+ "\n",
+ "Sketch two trajectories for $t \\in (0, 2 \\pi)$ as well as their\n",
+ "respective field values $\\phi(\\mathbf X(t), t)$ over time.\n",
+ "\n",
+ "### Task 2\n",
+ "\n",
+ "Describe $\\phi(\\mathbf X(t), t)$ in the Eulerian frame.\n",
+ "\n",
+ "### Task 3\n",
+ "\n",
+ "Consider another field given by $\\psi(\\mathbf X(t), t) = X^2 + Y^2$.\n",
+ "Rewrite $\\psi$ in the Eulerian frame.\n",
+ "\n",
+ "### Task 4\n",
+ "\n",
+ "Compute $\\frac{d\\phi}{dt}$ and $\\frac{D\\phi}{dt}$ using their\n",
+ "definitions.\n",
+ "\n",
+ "# Streamlines/Streaklines/Pathlines\n",
+ "\n",
+ "Consider the velocity field\n",
+ "\n",
+ "with $k, \\alpha$ being positive constants.\n",
+ "\n",
+ "## Tasks\n",
+ "\n",
+ "### Task 1\n",
+ "\n",
+ "Compute the pathlines.\n",
+ "\n",
+ "### Task 2\n",
+ "\n",
+ "Compute the streamlines.\n",
+ "\n",
+ "### Task 3\n",
+ "\n",
+ "Compute the streaklines.\n",
+ "\n",
+ "### Task 4\n",
+ "\n",
+ "Sketch the velocity field for\n",
+ "\n",
+ "- $t_0 < \\frac{k x}{\\alpha}$\n",
+ "- $t_1 = \\frac{k x}{\\alpha}$\n",
+ "- $t_2 > \\frac{k x}{\\alpha}$\n",
+ "\n",
+ "and furthermore add\n",
+ "\n",
+ "- the pathline for one particular $\\mathbf x_0$\n",
+ "- one particular streamline in each figure\n",
+ "- the streakline starting at $t_0$ for one particular $\\mathbf x_0$."
+ ],
+ "id": "13626618-f791-4ce6-86c3-1ed0dec45a4a"
+ }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "path": "/opt/conda/envs/cmm/share/jupyter/kernels/python3"
+ },
+ "language_info": {
+ "name": "python",
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": "3"
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.9"
+ }
+ }
+}
\ No newline at end of file
diff --git a/public/content/exercises/exercise02.pdf b/public/content/exercises/exercise02.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..c25ed2cd193462f9e7175fefe9d014649ec00992
Binary files /dev/null and b/public/content/exercises/exercise02.pdf differ
diff --git a/public/content/exercises/exercise02_files/figure-html/cell-2-output-1.png b/public/content/exercises/exercise02_files/figure-html/cell-2-output-1.png
new file mode 100644
index 0000000000000000000000000000000000000000..eaddf5187594fd127408a4e8b7ea3978a82acf3f
Binary files /dev/null and b/public/content/exercises/exercise02_files/figure-html/cell-2-output-1.png differ
diff --git a/public/content/exercises/exercise02_files/figure-html/cell-3-output-1.png b/public/content/exercises/exercise02_files/figure-html/cell-3-output-1.png
new file mode 100644
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Binary files /dev/null and b/public/content/exercises/exercise02_files/figure-html/cell-3-output-1.png differ
diff --git a/public/content/exercises/homework_template.ipynb b/public/content/exercises/homework_template.ipynb
index 898461b07ef43517df0ce1fb901410ea84115d65..0aab21c2b62e78501c23d6c56c8401ade4e29004 100644
--- a/public/content/exercises/homework_template.ipynb
+++ b/public/content/exercises/homework_template.ipynb
@@ -86,7 +86,7 @@
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iooKCggwmg6vasmWLvUP5gw8+0JgxY5Sbm6vKykrDyQA0lA0bNkj6z8Z0OP9L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SkoTCwkJJJV8QBCE0NFTQ6XSCq6urkJ+fz8vDyicIgvDLL78Iv/zyi1BeXi44ODjwYzWZ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\nAICy/S9GeoUSeELglgAAAABJRU5ErkJggg==\n"
}
},
- "id": "8fcd8cbc-2379-4ba9-bf03-a793f5f98da3"
+ "id": "e5489731-839f-47ec-8c44-c535433ea6ec"
}
],
"nbformat": 4,
diff --git a/public/content/exercises/homework_template.pdf b/public/content/exercises/homework_template.pdf
index dc4a6aa21a27a53637ebfecc0ef6f1ce55b742f7..765c50c256c8878d9e02998c7a36677321a72399 100644
Binary files a/public/content/exercises/homework_template.pdf and b/public/content/exercises/homework_template.pdf differ
diff --git a/public/content/exercises/notebooks/homework01.out.ipynb b/public/content/exercises/notebooks/homework01.out.ipynb
index 502373e93d69dddcf87f057b7e3c660b08389c84..b8593c8e71f128fcaba0ac956087434c42ee5b2e 100644
--- a/public/content/exercises/notebooks/homework01.out.ipynb
+++ b/public/content/exercises/notebooks/homework01.out.ipynb
@@ -34,7 +34,7 @@
"\n",
"# Imports"
],
- "id": "cafbe4d1-ed2e-477b-973d-c070042811f3"
+ "id": "aff96fd7-4589-404c-b98b-b7f0371c89fd"
},
{
"cell_type": "code",
@@ -129,7 +129,7 @@
"\n",
"An illustration of DG(0), DG(1) and CG(1) is given below:"
],
- "id": "af91a1a6-9610-46b8-a654-558c05e77a95"
+ "id": "0b1b6025-b347-4051-a7f8-3471696e6a1e"
},
{
"cell_type": "code",
@@ -308,7 +308,7 @@
"contribution for each boundary and two condtributions for the internal\n",
"facet at $x=1$."
],
- "id": "4fd57d00-168e-441b-91ed-fcffd47669ff"
+ "id": "61acb5d1-ad55-40f5-90e9-67430dd43c42"
},
{
"cell_type": "code",
@@ -413,7 +413,7 @@
"\n",
"We first start by creating a mesh"
],
- "id": "a6222ee7-6316-4671-9897-2c1d7fd4e9ac"
+ "id": "16488711-36c3-4a6a-96c9-28d2b801cb77"
},
{
"cell_type": "code",
@@ -439,7 +439,7 @@
"source": [
"and we want to compare $DG(0)$ and $DG(1)$ function spaces."
],
- "id": "5bad9b28-78c4-4415-9744-a18588e8e482"
+ "id": "8e27efaa-7b13-4872-bd2a-a56cab64cc0c"
},
{
"cell_type": "code",
@@ -486,7 +486,7 @@
"\n",
"with the characteristic lenth $=min(dx, dy)$."
],
- "id": "21d01fe8-f076-45f6-9224-4e4a1e3e3c23"
+ "id": "f64f3f67-5687-4beb-b8dc-f558f0bc3671"
},
{
"cell_type": "code",
@@ -557,7 +557,7 @@
"source": [
"We now initialize the solver"
],
- "id": "a3f4e91d-b97c-4dfa-843c-7aeef37c74f7"
+ "id": "88dd4f29-2625-4bf1-aa2d-50534c9068fe"
},
{
"cell_type": "code",
@@ -591,7 +591,7 @@
"class. However, we need to be aware that any polynomial degree \\> 0\n",
"might lead to problems when not addressed properly."
],
- "id": "76d021d4-3d4a-45e1-a350-265ff58930ae"
+ "id": "f0998bbe-023a-4b1c-b431-a089ea0401f2"
},
{
"cell_type": "code",
@@ -683,7 +683,7 @@
"source": [
"# A DG(0)/DG(1) minimal example"
],
- "id": "8f355918-2825-4b1b-bcc1-20a851d74002"
+ "id": "50f925e5-b9b2-4c6b-942d-ad36b52dc5cf"
},
{
"cell_type": "code",
@@ -823,7 +823,7 @@
"\\end{cases}\n",
"$$"
],
- "id": "92767aa8-32f0-43ab-9134-bd5da7a89906"
+ "id": "2f5eedff-4ad4-4f22-a1f1-11a453aea97f"
},
{
"cell_type": "code",
@@ -1022,7 +1022,7 @@
"> STUDENT TODO \n",
"> Implement the verification"
],
- "id": "80159a12-eac3-48a1-a6b0-f5ad8e50a5fb"
+ "id": "3dedcdb4-b46b-43e7-b10e-ce21636f6466"
},
{
"cell_type": "code",
@@ -1065,7 +1065,7 @@
"> Visualize the streamlines and pathlines in Paraview and insert an\n",
"> image for each into this notebook"
],
- "id": "9c037b78-d423-4072-be2a-f5b7480aacbf"
+ "id": "b86cc04a-226f-43bf-8f42-d11e15707c00"
},
{
"cell_type": "code",
@@ -1142,7 +1142,7 @@
"\n",
"7. Create a pathline plot in Paraview and explain what pathlines are"
],
- "id": "78736213-1ac7-4048-a8cb-4fea28d504bd"
+ "id": "a6041ef0-1aeb-4459-a61f-168e00023749"
}
],
"nbformat": 4,
diff --git a/public/content/exercises/notebooks/homework01.pdf b/public/content/exercises/notebooks/homework01.pdf
index 547d807fd07b3fa2cacac46e50494165907e0606..2fb9c37d8e1066971205d7cc01c9625c8ad59dd9 100644
Binary files a/public/content/exercises/notebooks/homework01.pdf and b/public/content/exercises/notebooks/homework01.pdf differ
diff --git a/public/content/exercises/notebooks/intro.out.ipynb b/public/content/exercises/notebooks/intro.out.ipynb
index 8aa12aa61f5525168f29fa35a1d64c9f39765f7b..39dc275c2e28c7ca51a3386cb4d9f2c7645cd4bf 100644
--- a/public/content/exercises/notebooks/intro.out.ipynb
+++ b/public/content/exercises/notebooks/intro.out.ipynb
@@ -48,7 +48,7 @@
"> [library.tar.gz](https://mbd.pages.rwth-aachen.de/courses/cmm/content/exercises/notebooks/library.tar.gz)\n",
"> file in the same directory as this notebook."
],
- "id": "3378e890-d183-40bb-8d17-e422224251e2"
+ "id": "4cc9754e-093d-4393-8701-8aa574949b46"
},
{
"cell_type": "code",
@@ -81,7 +81,7 @@
"Here, he have the choice between different discretization types,\n",
"e.g. quadrilateral, triangular etc."
],
- "id": "0c6057fa-28eb-404a-933e-6dd5b7c4d566"
+ "id": "3ec9b0ac-400d-43e2-99eb-da96e25979ff"
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{
"cell_type": "code",
@@ -163,7 +163,7 @@
"quadrilateral mesh that we created (*check out what it looks like\n",
"[here](https://defelement.org/elements/examples/quadrilateral-lagrange-equispaced-1.html)*)"
],
- "id": "668017bb-c23e-410b-8746-b0c06cc7f826"
+ "id": "c45f273b-3d08-463c-aa55-b66fe3d99c69"
},
{
"cell_type": "code",
@@ -194,7 +194,7 @@
"source": [
"Lets have a look at our beautiful mesh:"
],
- "id": "f8bd0dcf-736b-4bd6-abab-63883425aad0"
+ "id": "24e96bb7-18cd-4cc1-ad4c-b71dafb770fd"
},
{
"cell_type": "code",
@@ -255,7 +255,7 @@
"\n",
"Let’s see how we can write the weak form in UFL."
],
- "id": "dc69db01-c62d-42ac-8cb0-b63d6cac55fa"
+ "id": "6554f6a6-7e3e-4cf8-8287-762f63d54eca"
},
{
"cell_type": "code",
@@ -300,7 +300,7 @@
"consider to be the left and right boundary of the domain ($x=0$ and\n",
"$x=1$)."
],
- "id": "15102273-2955-4d8c-93cc-e552feee7764"
+ "id": "442a4830-d96f-4d3a-b93d-83be555b83ef"
},
{
"cell_type": "code",
@@ -326,7 +326,7 @@
"We now create two boundary conditions for a constant value of $g=1$ on\n",
"the left boundary and $g=0$ on the right boundary."
],
- "id": "61d8f94f-826b-47d1-ad88-c05dd1875408"
+ "id": "debf0fe7-e691-4025-ac11-91f1fa461ed2"
},
{
"cell_type": "code",
@@ -386,7 +386,7 @@
"The function lives in the same approximation space as our trial function\n",
"$u_h$ and is initialized to zero:"
],
- "id": "5440c7f6-0446-4977-8b42-f3cc5719e7fc"
+ "id": "ae5443d6-62dc-414e-98f3-d5b2b10fd946"
},
{
"cell_type": "code",
@@ -415,7 +415,7 @@
"source": [
"Then, we create a writer for the VTK file to store our results on disk."
],
- "id": "d522dcbe-5a00-49de-8bc8-4309440c66a1"
+ "id": "96e80b8d-7c76-4098-8485-28501d01f1ba"
},
{
"cell_type": "code",
@@ -454,7 +454,7 @@
"We now reap the true benefit of modern FEM backends: we can assemble the\n",
"linear system and solve it in a single line of code."
],
- "id": "0f848f99-7eec-404e-8f6d-a141cc641cad"
+ "id": "1e316021-1a46-4c24-bfd9-8b71b906759e"
},
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"cell_type": "code",
@@ -500,7 +500,7 @@
"The result is not super pretty but we can check that the solution indeed\n",
"“looks” like a linear connection between the boundary conditions"
],
- "id": "e6a4fbcf-9c5f-401b-aa20-435e8fd66543"
+ "id": "69457da6-a146-43c4-a7d0-e0e2aa59ad99"
},
{
"cell_type": "code",
@@ -555,7 +555,7 @@
"We can also visualize the solution using Paraview. To do that, we open\n",
"the files written to the following location"
],
- "id": "b54fb91f-6a0b-49e1-9242-8f22ffaf3eb4"
+ "id": "4a9e2fae-8b99-4e38-b736-799a1c990939"
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@@ -589,7 +589,7 @@
"u_{\\text{exact}}(x,y) = 1 - x \\quad \\text{ for } x \\in [0,1], y \\in [0,1]\n",
"$$"
],
- "id": "36f3cc3d-de59-4a89-b966-bd91d58a6d6f"
+ "id": "5563673d-fe0a-4801-8be2-599320e1d311"
},
{
"cell_type": "code",
diff --git a/public/content/exercises/notebooks/intro.pdf b/public/content/exercises/notebooks/intro.pdf
index b7685cd0851791b230af7df135ec6ad2cb355a69..5966e4e74ec71fcbc7a0319dd70a3cee1cbd0dd9 100644
Binary files a/public/content/exercises/notebooks/intro.pdf and b/public/content/exercises/notebooks/intro.pdf differ
diff --git a/public/devcontainer.tar.gz b/public/devcontainer.tar.gz
index 270fe46991a49456b749a78cf102570363a26018..d5257511dfb50f21a2cf9dedc5168c63b44eef8c 100644
Binary files a/public/devcontainer.tar.gz and b/public/devcontainer.tar.gz differ
diff --git a/public/search.json b/public/search.json
index 738be54f64c099b091c49ec61fc1efc9aa460464..4f8c49da899ce73b3d1684b3da22f5a6e159e09b 100644
--- a/public/search.json
+++ b/public/search.json
@@ -555,103 +555,49 @@
"text": "Tasks\n\nTask 1\nIn a rotating reference frame with angular velocity \\(\\Omega\\), state the Bernoulli equation. What are the assumptions used to derive the equation?\n\n\nTask 2\nFor the fluid spinning at \\(\\Omega\\), calculate the difference in height of the fluid at points 1 and 2.\n\n\nTask 3\nCalculate the angular velocity of spinning \\(\\Omega_0\\), at which the height of the fluid at point 1 is zero.\n\n\nTask 4\nFor the fluid spinning at \\(\\Omega_0\\), calculate the pressure at point 3 in terms of the of the ambient pressure \\(p_a\\)"
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- "text": "Base Units\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDimension\n\\(\\mathrm{T}\\)\n\\(\\mathrm{L}\\)\n\\(\\mathrm{M}\\)\n\\(\\theta\\)\n\\(\\mathrm{N}\\)\n\\(\\mathrm{I}\\)\n\\(I_v\\)\n\n\nUnit\n\\(\\mathrm{s}\\)\n\\(\\mathrm{m}\\)\n\\(\\mathrm{kg}\\)\n\\(\\mathrm{K}\\)\n\\(\\mathrm{mol}\\)\n\\(\\mathrm{A}\\)\n\\(\\mathrm{cd}\\)\n\n\n\n\nDerived units\n\n\n\n\n\\(\\mathrm{Hz}\\)\n\\(\\mathrm{s}^{-1}\\)\n\n\n\\(\\mathrm{N}\\)\n\\(\\mathrm{kg} \\, \\mathrm{m} \\, \\mathrm{s}^{-2}\\)\n\n\n\\(\\mathrm{Pa}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}^{-2}\\)\n\n\n\\(\\mathrm{J}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}\\)\n\n\n\\(\\mathrm{W}\\)\n\\(\\mathrm{J} \\, \\mathrm{s}^{-1}\\)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReynold number\nRe\n\\(\\dfrac{v \\, L}{\\nu}\\)\n\n\nFroude number\nFr\n\\(\\dfrac{v}{\\sqrt{gh}}\\)\n\n\nShallowness parameter\n\\(\\epsilon\\)\n\\(\\dfrac{H}{L}\\)\n\n\nPeclet number\nPe\n\\(\\dfrac{L v}{\\alpha}\\)\n\n\nPrandtl number\nPr\n\\(\\dfrac{\\nu}{\\alpha}\\)\n\n\nStefan number\nSte\n\\(\\dfrac{c_p \\Delta T}{h_m}\\)\n\\(h_m:\\) latent heat of melting\n\n\nStrouhal number\nStr\n\\(\\dfrac{f L}{v}\\)\n\n\nMach number\nMa\n\\(\\dfrac{v}{c}\\)\n\n\nLewis number\nLe\n\\(\\dfrac{\\alpha}{D}\\)\n\n\n\n\n\n\n\nShape factor \\[\n\\alpha = \\frac{\\overline{u^2}}{\\overline{u}^2}, \\; \\text{where} \\;\n\\bar \\ast = (1/h) \\int_b^s \\ast \\,dz\n\\]\nDecomposition of the velocity profile \\[\n \\mathbf{v}(\\mathbf{x} + \\mathbf{r}) = \\mathbf{v}(\\mathbf{x}) + \\mathbf{w} \\times \\mathbf{r} + \\mathbf{D} \\cdot \\mathbf{r}\n\\] \\[\n\\text{where} \\;\\; \\mathbf{w} = \\frac{1}{2} \\nabla \\times \\mathbf{v}, \\;\n\\mathbf{D} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} + \\nabla \\mathbf{v}^T \\right), \\;\n\\mathbf{W} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} - \\nabla \\mathbf{v}^T \\right)\n\\]\nCylindrical coordinate transformation rules \\[\n\\begin{aligned}\n\\nabla f & = \\frac{\\partial f}{\\partial r } \\vec{e}_r + \\frac{1}{r} \\frac{\\partial f}{\\partial \\theta} \\vec{e}_\\theta+ \\frac{\\partial f}{\\partial z} \\vec{e}_z, \\\\\n\\nabla \\cdot \\vec{A} & = \\frac{1}{r}\\frac{\\partial}{\\partial r }(r A_r) + \\frac{1}{r} \\frac{\\partial A_\\theta}{\\partial \\theta} + \\frac{\\partial A_z}{\\partial z} \\\\\n\\nabla \\times \\vec{A} & = \\left(\\frac{1}{r}\\frac{\\partial A_z}{\\partial \\theta}-\\frac{\\partial A_\\theta}{\\partial z}\\right)\\vec{e}_r+ \\left(\\frac{\\partial A_r}{\\partial z}-\\frac{\\partial A_z}{\\partial r}\\right)\\vec{e}_\\theta+\\frac{1}{r}\\left(\\frac{\\partial}{\\partial r}(r A_\\theta)-\\frac{\\partial A_r}{\\partial \\theta}\\right) \\vec{e}_z \n\\end{aligned}\n\\]\nMaterial derivative \\[\n\\frac{D}{Dt} f = \\partial_t f + \\mathbf{v} \\cdot \\nabla f\n\\]\nBernoulli equation \\[\n\\tfrac{p}{\\rho} + \\tfrac{v^2}{2} + g z = const. along a streamline\n\\]\nError function \\[\n\\begin{aligned}\n\\text{erf} (x) := \\frac{2}{\\sqrt{\\pi}} \\int_0^x e^{-y^2} dy, \\;\\;\\;\\; &\\text{erfc} (x) := 1 - \\text{erf}(x)\\\\\n\\partial_x \\text{erf} (C x) = \\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}, \\;\\;\\;\\; &\\partial_x \\text{erfc} (C x) = -\\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}\\\\\n\\end{aligned}\n\\]\nThermal diffusivity \\[\n\\alpha = \\frac{\\kappa}{\\rho c_p}\n\\]\n\n\n\n\n\nMaterial symmetry \\[\n \\hat \\sigma^{(\\zeta)} (*) = \\hat \\sigma^{(\\eta)} (* P).\n \\]\nMaterial Isotropy \\[\n \\hat {\\sigma} (F) =\\hat \\sigma ( V \\cdot Q) = \\hat \\sigma ( V \\cdot Q \\cdot P) = \\hat \\sigma ( V \\cdot Q \\cdot Q^T) = \\hat \\sigma ( V)\n \\]\nMaterial objectivity \\[\n \\sigma^{(\\mathbf y)} = Q \\cdot \\sigma^{(\\mathbf x)} \\cdot Q^T\n \\]\nGalilean invariance \\[\n \\pmb{\\zeta} = \\mathbf{x} - \\mathbf{v} t\n \\]\n\n\n\n\n\nMass and momentum balance \\[\n\\begin{aligned}\n\\partial_t \\rho + \\nabla \\cdot \\left( \\rho \\mathbf v \\right) &= 0 \\\\\n\\partial_t ( \\rho \\mathbf v ) + \\nabla \\cdot \\left(\\rho \\mathbf v \\otimes \\mathbf v \\right) &= - \\nabla p + \\nabla \\cdot \\pmb{\\tau} + \\rho \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Euler \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf v & = 0\\\\\n\\partial_t \\mathbf v + \\mathbf v \\cdot \\nabla \\mathbf v &= - \\frac{1}{\\rho}\\nabla p + \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf{v}&= 0 \\\\\n\\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} &= - \\frac{1}{\\rho}\\nabla p + \\nu \\triangle \\mathbf{v} + \\mathbf{b}\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes in cylindrical coordinates \\[\n\\begin{aligned}\n\\frac{\\partial u_r}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_r - \\frac{u_\\theta^2}{r} & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial r} + \\nu \\left(\\nabla^2 u_r - \\frac{u_r}{r^2} - \\frac{2}{r^2} \\frac{\\partial u_\\theta}{\\partial \\theta}\\right) \\\\\n\\frac{\\partial u_\\theta}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_\\theta + \\frac{u_r u_\\theta}{r} & = -\\frac{1}{\\rho r} \\frac{\\partial p}{\\partial \\theta} + \\nu \\left(\\nabla^2 u_\\theta + \\frac{2}{r^2} \\frac{\\partial u_r}{\\partial \\theta} - \\frac{u_\\theta}{r^2} \\right) \\\\\n\\frac{\\partial u_z}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_z & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial z} + \\nu \\nabla^2 u_z \\\\\n\\frac{1}{r} \\frac{\\partial}{\\partial r} \\left( r u_r \\right) + \\frac{1}{r} \\frac{\\partial u_\\theta}{\\partial \\theta} + \\frac{\\partial u_z}{\\partial z} & = 0\n\\end{aligned}\n\\]\nHeat equation \\[\n\\partial_t T + \\nabla \\cdot \\left( T \\mathbf{v} \\right) = \\alpha \\Delta T\n\\]\nIncompresisble Navier-Stokes-Boussinesq-Fourier \\[\\begin{align*}\n \\nabla \\cdot \\mathbf{v} & = 0 \\\\\n \\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} & = - \\frac{1}{\\rho_0} \\nabla \\left( p - \\rho_0 g z \\right) + \\nu \\Delta \\mathbf{v} - \\mathbf{g} B \\left(T-T_0\\right) \\\\\n \\partial_t \\left(\\rho c_p T \\right) + \\nabla \\cdot \\left( \\rho c_p T \\mathbf{v} \\right) & = \\nabla \\cdot \\left( \\kappa \\nabla T \\right) + \\mathbf{S}\n\\end{align*}\\]\n\n\n\n\n\nNewtonian fluid \\[\n\\pmb{\\tau} = \\lambda (\\nabla \\cdot \\mathbf v) \\mathbf I + \\eta \\left( \\nabla \\mathbf v + \\nabla \\mathbf v^T \\right),\n\\]\nHooke’s law \\[\n\\pmb{\\sigma} = \\lambda \\mathrm{tr} (\\mathbf{D}) \\mathbf{I} + 2 \\mu \\mathbf{D},\n\\]\nBoussinesq approximation \\[\n\\rho = \\rho_0 (1-B(T-T_0))\n\\]\nStefan condition \\[\n\\rho_s L \\partial_t X_m(t) = -\\kappa \\partial_x T (X_m^{-}(t),t) + \\kappa \\partial_x T (X_m^{+}(t),t)\n\\]",
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- "text": "Reynold number\nRe\n\\(\\dfrac{v \\, L}{\\nu}\\)\n\n\nFroude number\nFr\n\\(\\dfrac{v}{\\sqrt{gh}}\\)\n\n\nShallowness parameter\n\\(\\epsilon\\)\n\\(\\dfrac{H}{L}\\)\n\n\nPeclet number\nPe\n\\(\\dfrac{L v}{\\alpha}\\)\n\n\nPrandtl number\nPr\n\\(\\dfrac{\\nu}{\\alpha}\\)\n\n\nStefan number\nSte\n\\(\\dfrac{c_p \\Delta T}{h_m}\\)\n\\(h_m:\\) latent heat of melting\n\n\nStrouhal number\nStr\n\\(\\dfrac{f L}{v}\\)\n\n\nMach number\nMa\n\\(\\dfrac{v}{c}\\)\n\n\nLewis number\nLe\n\\(\\dfrac{\\alpha}{D}\\)",
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- "text": "Shape factor \\[\n\\alpha = \\frac{\\overline{u^2}}{\\overline{u}^2}, \\; \\text{where} \\;\n\\bar \\ast = (1/h) \\int_b^s \\ast \\,dz\n\\]\nDecomposition of the velocity profile \\[\n \\mathbf{v}(\\mathbf{x} + \\mathbf{r}) = \\mathbf{v}(\\mathbf{x}) + \\mathbf{w} \\times \\mathbf{r} + \\mathbf{D} \\cdot \\mathbf{r}\n\\] \\[\n\\text{where} \\;\\; \\mathbf{w} = \\frac{1}{2} \\nabla \\times \\mathbf{v}, \\;\n\\mathbf{D} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} + \\nabla \\mathbf{v}^T \\right), \\;\n\\mathbf{W} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} - \\nabla \\mathbf{v}^T \\right)\n\\]\nCylindrical coordinate transformation rules \\[\n\\begin{aligned}\n\\nabla f & = \\frac{\\partial f}{\\partial r } \\vec{e}_r + \\frac{1}{r} \\frac{\\partial f}{\\partial \\theta} \\vec{e}_\\theta+ \\frac{\\partial f}{\\partial z} \\vec{e}_z, \\\\\n\\nabla \\cdot \\vec{A} & = \\frac{1}{r}\\frac{\\partial}{\\partial r }(r A_r) + \\frac{1}{r} \\frac{\\partial A_\\theta}{\\partial \\theta} + \\frac{\\partial A_z}{\\partial z} \\\\\n\\nabla \\times \\vec{A} & = \\left(\\frac{1}{r}\\frac{\\partial A_z}{\\partial \\theta}-\\frac{\\partial A_\\theta}{\\partial z}\\right)\\vec{e}_r+ \\left(\\frac{\\partial A_r}{\\partial z}-\\frac{\\partial A_z}{\\partial r}\\right)\\vec{e}_\\theta+\\frac{1}{r}\\left(\\frac{\\partial}{\\partial r}(r A_\\theta)-\\frac{\\partial A_r}{\\partial \\theta}\\right) \\vec{e}_z \n\\end{aligned}\n\\]\nMaterial derivative \\[\n\\frac{D}{Dt} f = \\partial_t f + \\mathbf{v} \\cdot \\nabla f\n\\]\nBernoulli equation \\[\n\\tfrac{p}{\\rho} + \\tfrac{v^2}{2} + g z = const. along a streamline\n\\]\nError function \\[\n\\begin{aligned}\n\\text{erf} (x) := \\frac{2}{\\sqrt{\\pi}} \\int_0^x e^{-y^2} dy, \\;\\;\\;\\; &\\text{erfc} (x) := 1 - \\text{erf}(x)\\\\\n\\partial_x \\text{erf} (C x) = \\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}, \\;\\;\\;\\; &\\partial_x \\text{erfc} (C x) = -\\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}\\\\\n\\end{aligned}\n\\]\nThermal diffusivity \\[\n\\alpha = \\frac{\\kappa}{\\rho c_p}\n\\]",
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+ "text": "Tasks\n\nShow that the scalar projection of a vector \\(\\mathbf{u}\\) on any of the basis vectors, \\(\\text{proj}_{\\mathbf{e}_{i}}(\\mathbf{u}) := \\mathbf{u} \\cdot \\mathbf{e}_{i}\\), is identical to the component of \\(\\mathbf{u}\\) corresponding to that basis vector.\n\n\nWrite out the dot-product of two arbitrary vectors \\(\\mathbf{u} \\cdot \\mathbf{v}\\) in component notation and index notation.\n\n\nWrite the cross-product of two arbitrary vectors \\(\\mathbf{u} \\times \\mathbf{v}\\) in component notation and in index notation.\n\n\nWrite the expression \\(\\left(\\mathbf{u} \\cdot \\nabla \\right) \\mathbf{u}\\) in component notation and in index notation.\n\n\nShow that, for three arbitrary vectors \\(\\mathbf{u}\\), \\(\\mathbf{v}\\) and \\(\\mathbf{w}\\) the triple vector product\n\n\\[\n\\mathbf{u} \\times (\\mathbf{v} \\times \\mathbf{w}) = \\mathbf{v} (\\mathbf{u} \\cdot \\mathbf{w}) - \\mathbf{w} (\\mathbf{u} \\cdot \\mathbf{v})\n\\]\n\n\n\n\n\n\nTip\n\n\n\nBetween the Kronecker delta and the Levi-Civita symbol, the following identity holds\n\\[\n\\epsilon_{ijk} \\,\\epsilon_{klm} = \\delta_{il} \\,\\delta_{jm} - \\delta_{im} \\, \\delta_{jl}\n\\]",
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- "text": "Material symmetry \\[\n \\hat \\sigma^{(\\zeta)} (*) = \\hat \\sigma^{(\\eta)} (* P).\n \\]\nMaterial Isotropy \\[\n \\hat {\\sigma} (F) =\\hat \\sigma ( V \\cdot Q) = \\hat \\sigma ( V \\cdot Q \\cdot P) = \\hat \\sigma ( V \\cdot Q \\cdot Q^T) = \\hat \\sigma ( V)\n \\]\nMaterial objectivity \\[\n \\sigma^{(\\mathbf y)} = Q \\cdot \\sigma^{(\\mathbf x)} \\cdot Q^T\n \\]\nGalilean invariance \\[\n \\pmb{\\zeta} = \\mathbf{x} - \\mathbf{v} t\n \\]",
+ "text": "The course is complemented by weekly exercises. Please consult the semester schedule for dates, unless otherwise notified through Moodle announcements.\nWe split the 90 minutes exercise into two parts:\n\n45 minutes exercise held by the lecturer. We will typically cover\n\na brief lecture summary\ndiscussion of one old exam problem\n\n45 minutes hands-on tutorial and Q&A session. This is your opportunity to\n\nsolve a selected problem on your own\napproach us regarding the current programming homework assignment\n\nDue to time restrictions, we do not plan to discuss the solution of the tutorial problem. We will publish (partial) solutions for these tutorial problems. Feel free to approach us during the tutorial session if you want to make sure that you got the right result!\n\n\n\n\n\n\n\nExercises problems are typically published a couple of days before the exercise session on this website. We encourage students to have a look at the problems in advance.",
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- "text": "Mass and momentum balance \\[\n\\begin{aligned}\n\\partial_t \\rho + \\nabla \\cdot \\left( \\rho \\mathbf v \\right) &= 0 \\\\\n\\partial_t ( \\rho \\mathbf v ) + \\nabla \\cdot \\left(\\rho \\mathbf v \\otimes \\mathbf v \\right) &= - \\nabla p + \\nabla \\cdot \\pmb{\\tau} + \\rho \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Euler \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf v & = 0\\\\\n\\partial_t \\mathbf v + \\mathbf v \\cdot \\nabla \\mathbf v &= - \\frac{1}{\\rho}\\nabla p + \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf{v}&= 0 \\\\\n\\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} &= - \\frac{1}{\\rho}\\nabla p + \\nu \\triangle \\mathbf{v} + \\mathbf{b}\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes in cylindrical coordinates \\[\n\\begin{aligned}\n\\frac{\\partial u_r}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_r - \\frac{u_\\theta^2}{r} & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial r} + \\nu \\left(\\nabla^2 u_r - \\frac{u_r}{r^2} - \\frac{2}{r^2} \\frac{\\partial u_\\theta}{\\partial \\theta}\\right) \\\\\n\\frac{\\partial u_\\theta}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_\\theta + \\frac{u_r u_\\theta}{r} & = -\\frac{1}{\\rho r} \\frac{\\partial p}{\\partial \\theta} + \\nu \\left(\\nabla^2 u_\\theta + \\frac{2}{r^2} \\frac{\\partial u_r}{\\partial \\theta} - \\frac{u_\\theta}{r^2} \\right) \\\\\n\\frac{\\partial u_z}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_z & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial z} + \\nu \\nabla^2 u_z \\\\\n\\frac{1}{r} \\frac{\\partial}{\\partial r} \\left( r u_r \\right) + \\frac{1}{r} \\frac{\\partial u_\\theta}{\\partial \\theta} + \\frac{\\partial u_z}{\\partial z} & = 0\n\\end{aligned}\n\\]\nHeat equation \\[\n\\partial_t T + \\nabla \\cdot \\left( T \\mathbf{v} \\right) = \\alpha \\Delta T\n\\]\nIncompresisble Navier-Stokes-Boussinesq-Fourier \\[\\begin{align*}\n \\nabla \\cdot \\mathbf{v} & = 0 \\\\\n \\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} & = - \\frac{1}{\\rho_0} \\nabla \\left( p - \\rho_0 g z \\right) + \\nu \\Delta \\mathbf{v} - \\mathbf{g} B \\left(T-T_0\\right) \\\\\n \\partial_t \\left(\\rho c_p T \\right) + \\nabla \\cdot \\left( \\rho c_p T \\mathbf{v} \\right) & = \\nabla \\cdot \\left( \\kappa \\nabla T \\right) + \\mathbf{S}\n\\end{align*}\\]",
+ "text": "The course is complemented by weekly exercises. Please consult the semester schedule for dates, unless otherwise notified through Moodle announcements.\nWe split the 90 minutes exercise into two parts:\n\n45 minutes exercise held by the lecturer. We will typically cover\n\na brief lecture summary\ndiscussion of one old exam problem\n\n45 minutes hands-on tutorial and Q&A session. This is your opportunity to\n\nsolve a selected problem on your own\napproach us regarding the current programming homework assignment\n\nDue to time restrictions, we do not plan to discuss the solution of the tutorial problem. We will publish (partial) solutions for these tutorial problems. Feel free to approach us during the tutorial session if you want to make sure that you got the right result!\n\n\n\n\n\n\n\nExercises problems are typically published a couple of days before the exercise session on this website. We encourage students to have a look at the problems in advance.",
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- "text": "Newtonian fluid \\[\n\\pmb{\\tau} = \\lambda (\\nabla \\cdot \\mathbf v) \\mathbf I + \\eta \\left( \\nabla \\mathbf v + \\nabla \\mathbf v^T \\right),\n\\]\nHooke’s law \\[\n\\pmb{\\sigma} = \\lambda \\mathrm{tr} (\\mathbf{D}) \\mathbf{I} + 2 \\mu \\mathbf{D},\n\\]\nBoussinesq approximation \\[\n\\rho = \\rho_0 (1-B(T-T_0))\n\\]\nStefan condition \\[\n\\rho_s L \\partial_t X_m(t) = -\\kappa \\partial_x T (X_m^{-}(t),t) + \\kappa \\partial_x T (X_m^{+}(t),t)\n\\]",
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+ "text": "Homework Assignments\nIn addition to the weekly exercises, the course is supplemented with biweekly (programming) homework assignments.\nThe homework sheets will be distributed on this website. The publishing dates for the homework assignments can be found in the semester schedule. The deadline is usually one week after the publishing date. The exact deadline can be found on the individual homework assignments.\n\n\n\n\n\n\nCompletion of the homework assignments will grant you bonus points for the final exam.\n\n\n\nThe assignments can be completed in the groups that you will be assigned to in the Moodle course room of this class. The submission of the assignments will also be handled through the Moodle course room.\nDetails regarding the student groups, grading scheme and bonus point policy will be discussed during the first exercise session.\n\n\n\n\n\n\nWarning\n\n\n\nEach homework assignment has a deadline. Submissions after the deadline are not accepted. The submissions cannot be changed after the deadline is expired.\n\n\n\nStudent Groups\nStudents are required to form groups of three students to submit their homework. Groups with less than three members will automatically be merged after the second week of the semester.\n\n\nGrading Scheme\nFor each homework, you can earn up to two points. The points are earned as follows:\n1 Point - Submission (group point) Your submission fulfills the following:\n\nYour homework as uploaded until the deadline expires\nAll tasks in the homework are answered adequately.\nProgramming assignments need to run without error within our provided container on the RWTH Cluster.\nIf the programming homework includes tests, all tests need to pass.\n\n1 Point - Presentation (individual point) You can schedule a group appointment for a presentation in the week after the homework deadline. During this 5 minutes presentation, each present group member will be asked one question from the homework assignment. Which question you will get will be randomized. If you answer the question properly, you will be granted a point.\n\n\nHow can I use the RWTH Cluster for the homework assignments\nSee your wiki entry on ways to work on the homework assignment. In addition, there is a dedicated video on how to get you started published on Moodle.\n\n\nBonus Points Policy\n10% of the total points of the final exam can be gained as bonus points during the semester. These bonus points will be added to the final score of your exam.\n\nBonus point policy\n\n\nHomework points\nExam bonus points\n\n\n\n\n10, 11, 12\n10%\n\n\n9\n9%\n\n\n8\n8%\n\n\n7\n7%\n\n\n6\n6%\n\n\n5\n5%\n\n\n4\n4%\n\n\n3\n3%\n\n\n2\n2%\n\n\n1\n1%",
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"text": "The RWTH Moodle platform is used in conjunction with this weppage. The RWTH Moodle will be used for:\n\nannouncements (change of dates/rooms, exam related information)\ngroups for homework assignments\nhomework submission\n\nThis webpage will be used for:\n\nsharing lecture notes\nsharing slides\nsharing homework sheets\n\nIf you have trouble accessing the RWTH Moodle or parts of this webpage, do not hesitate to contact:\nsteldermann@mbd.rwth-aachen.de, terschanski@mbd.rwth-aachen.de, correa@mbd.rwth-aachen.de."
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- "text": "The course is complemented by weekly exercises. Please consult the semester schedule for dates, unless otherwise notified through Moodle announcements.\nWe split the 90 minutes exercise into two parts:\n\n45 minutes exercise held by the lecturer. We will typically cover\n\na brief lecture summary\ndiscussion of one old exam problem\n\n45 minutes hands-on tutorial and Q&A session. This is your opportunity to\n\nsolve a selected problem on your own\napproach us regarding the current programming homework assignment\n\nDue to time restrictions, we do not plan to discuss the solution of the tutorial problem. We will publish (partial) solutions for these tutorial problems. Feel free to approach us during the tutorial session if you want to make sure that you got the right result!\n\n\n\n\n\n\n\nExercises problems are typically published a couple of days before the exercise session on this website. We encourage students to have a look at the problems in advance.",
+ "text": "If not announced otherwise on moodle, the lectures/exercises take place as such:\nLectures:\n\nMonday, 8:30 – 10:00, Eilfschornsteinstr. 18, Room 009, MS\nFriday, 8:30 – 10:00, Eilfschornsteinstr. 18, Room 009, MS\n\nExercises:\n\nFriday, 14:30 – 16:00, Eilfschornsteinstr. 18, Room 009, MS\n\nExam:\n\nTuesday, 05.08.2025, 09:00 – 11:00, FT (2090|120), Melatener Str. 23-25, 1st floor"
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+ "text": "Course Plan\nThe course plan will be updated as we progress with the term. Changes regarding timing and lecture format will be communicated via moodle.\n\n\n\n\n\n\n\n\n\n\nL\nEx\nDay\nDate\nTopic\n\n\n\n\n1\n\nMo\n07.04.2025\nIntroduction\n\n\n2\n\nFr\n11.04.2025\nFundamentals\n\n\n\n1\nFr\n11.04.2025\nExercise session\n\n\n3\n\nMo\n14.04.2025\nBalance laws\n\n\n–\n–\nFr\n18.04.2025\n–\n\n\n–\n–\nMo\n21.04.2025\n–\n\n\n5\n\nFr\n25.04.2025\nIncompressible Euler\n\n\n\n2\nFr\n25.04.2025\nExercise session / release assignment 1\n\n\n6\n\nMo\n28.04.2025\nIncompressible Euler\n\n\n7\n\nFr\n02.05.2025\nForces and stresses\n\n\n\n3\nFr\n02.05.2025\nExercise session\n\n\n8\n\nMo\n05.05.2025\nForces and stresses\n\n\n9\n\nFr\n09.05.2025\nLinear elasticity\n\n\n\n4\nFr\n09.05.2025\nExercise session / release assignment 2\n\n\n10\n\nMo\n12.05.2025\nLinear elasticity\n\n\n11\n\nFr\n16.05.2025\nLinear elasticity\n\n\n\n5\nFr\n16.05.2025\nExercise session\n\n\n12\n\nMo\n19.05.2025\nIncompressible Navier-Stokes\n\n\n13\n\nFr\n23.05.2025\nIncompressible Navier-Stokes\n\n\n\n6\nFr\n23.05.2025\nExercise session / release assignment 3\n\n\n15\n\nMo\n26.05.2025\nPorous media flow\n\n\n16\n\nFr\n30.05.2025\nPorous media flow\n\n\n\n7\nFr\n30.05.2025\nExercise session\n\n\n17\n\nMo\n02.06.2025\nShallow flow\n\n\n18\n\nFr\n06.06.2025\nShallow flow\n\n\n\n8\nFr\n06.06.2025\nExercise session / release assignment 4\n\n\n–\n\nMo\n09.06.2025\n–\n\n\n–\n\nFr\n13.06.2025\n–\n\n\n\n–\nFr\n13.06.2025\n–\n\n\n19\n\nMo\n16.06.2025\nShallow moment equations\n\n\n20\n\nFr\n20.06.2025\nLubricated flow\n\n\n\n9\nFr\n20.06.2025\nExercise session / release assignment 5\n\n\n21\n\nMo\n23.06.2025\nHeat equation\n\n\n22\n\nFr\n27.06.2025\nSolid-liquid phase change\n\n\n\n10\nFr\n27.06.2025\nExercise session\n\n\n23\n\nMo\n30.06.2025\nSolid-liquid phase change\n\n\n24\n\nFr\n04.07.2025\nContact phase change\n\n\n\n11\nFr\n04.07.2025\nExercise session / release assignment 6\n\n\n25\n\nMo\n07.07.2025\nMaxwell\n\n\n26\n\nFr\n11.07.2025\nBloch equations and MRI\n\n\n\n12\nFr\n11.07.2025\nExercise session\n\n\n27\n\nMo\n14.07.2025\nConvection & thermochemistry coupled PC\n\n\n28\n\nFr\n18.07.2025\nReactive transport in porous media\n\n\n\n13\nFr\n18.07.2025\nQ&A"
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+ "text": "Base Units\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDimension\n\\(\\mathrm{T}\\)\n\\(\\mathrm{L}\\)\n\\(\\mathrm{M}\\)\n\\(\\theta\\)\n\\(\\mathrm{N}\\)\n\\(\\mathrm{I}\\)\n\\(I_v\\)\n\n\nUnit\n\\(\\mathrm{s}\\)\n\\(\\mathrm{m}\\)\n\\(\\mathrm{kg}\\)\n\\(\\mathrm{K}\\)\n\\(\\mathrm{mol}\\)\n\\(\\mathrm{A}\\)\n\\(\\mathrm{cd}\\)\n\n\n\n\nDerived units\n\n\n\n\n\\(\\mathrm{Hz}\\)\n\\(\\mathrm{s}^{-1}\\)\n\n\n\\(\\mathrm{N}\\)\n\\(\\mathrm{kg} \\, \\mathrm{m} \\, \\mathrm{s}^{-2}\\)\n\n\n\\(\\mathrm{Pa}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}^{-2}\\)\n\n\n\\(\\mathrm{J}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}\\)\n\n\n\\(\\mathrm{W}\\)\n\\(\\mathrm{J} \\, \\mathrm{s}^{-1}\\)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nReynold number\nRe\n\\(\\dfrac{v \\, L}{\\nu}\\)\n\n\nFroude number\nFr\n\\(\\dfrac{v}{\\sqrt{gh}}\\)\n\n\nShallowness parameter\n\\(\\epsilon\\)\n\\(\\dfrac{H}{L}\\)\n\n\nPeclet number\nPe\n\\(\\dfrac{L v}{\\alpha}\\)\n\n\nPrandtl number\nPr\n\\(\\dfrac{\\nu}{\\alpha}\\)\n\n\nStefan number\nSte\n\\(\\dfrac{c_p \\Delta T}{h_m}\\)\n\\(h_m:\\) latent heat of melting\n\n\nStrouhal number\nStr\n\\(\\dfrac{f L}{v}\\)\n\n\nMach number\nMa\n\\(\\dfrac{v}{c}\\)\n\n\nLewis number\nLe\n\\(\\dfrac{\\alpha}{D}\\)\n\n\n\n\n\n\n\nShape factor \\[\n\\alpha = \\frac{\\overline{u^2}}{\\overline{u}^2}, \\; \\text{where} \\;\n\\bar \\ast = (1/h) \\int_b^s \\ast \\,dz\n\\]\nDecomposition of the velocity profile \\[\n \\mathbf{v}(\\mathbf{x} + \\mathbf{r}) = \\mathbf{v}(\\mathbf{x}) + \\mathbf{w} \\times \\mathbf{r} + \\mathbf{D} \\cdot \\mathbf{r}\n\\] \\[\n\\text{where} \\;\\; \\mathbf{w} = \\frac{1}{2} \\nabla \\times \\mathbf{v}, \\;\n\\mathbf{D} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} + \\nabla \\mathbf{v}^T \\right), \\;\n\\mathbf{W} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} - \\nabla \\mathbf{v}^T \\right)\n\\]\nCylindrical coordinate transformation rules \\[\n\\begin{aligned}\n\\nabla f & = \\frac{\\partial f}{\\partial r } \\vec{e}_r + \\frac{1}{r} \\frac{\\partial f}{\\partial \\theta} \\vec{e}_\\theta+ \\frac{\\partial f}{\\partial z} \\vec{e}_z, \\\\\n\\nabla \\cdot \\vec{A} & = \\frac{1}{r}\\frac{\\partial}{\\partial r }(r A_r) + \\frac{1}{r} \\frac{\\partial A_\\theta}{\\partial \\theta} + \\frac{\\partial A_z}{\\partial z} \\\\\n\\nabla \\times \\vec{A} & = \\left(\\frac{1}{r}\\frac{\\partial A_z}{\\partial \\theta}-\\frac{\\partial A_\\theta}{\\partial z}\\right)\\vec{e}_r+ \\left(\\frac{\\partial A_r}{\\partial z}-\\frac{\\partial A_z}{\\partial r}\\right)\\vec{e}_\\theta+\\frac{1}{r}\\left(\\frac{\\partial}{\\partial r}(r A_\\theta)-\\frac{\\partial A_r}{\\partial \\theta}\\right) \\vec{e}_z \n\\end{aligned}\n\\]\nMaterial derivative \\[\n\\frac{D}{Dt} f = \\partial_t f + \\mathbf{v} \\cdot \\nabla f\n\\]\nBernoulli equation \\[\n\\tfrac{p}{\\rho} + \\tfrac{v^2}{2} + g z = const. along a streamline\n\\]\nError function \\[\n\\begin{aligned}\n\\text{erf} (x) := \\frac{2}{\\sqrt{\\pi}} \\int_0^x e^{-y^2} dy, \\;\\;\\;\\; &\\text{erfc} (x) := 1 - \\text{erf}(x)\\\\\n\\partial_x \\text{erf} (C x) = \\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}, \\;\\;\\;\\; &\\partial_x \\text{erfc} (C x) = -\\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}\\\\\n\\end{aligned}\n\\]\nThermal diffusivity \\[\n\\alpha = \\frac{\\kappa}{\\rho c_p}\n\\]\n\n\n\n\n\nMaterial symmetry \\[\n \\hat \\sigma^{(\\zeta)} (*) = \\hat \\sigma^{(\\eta)} (* P).\n \\]\nMaterial Isotropy \\[\n \\hat {\\sigma} (F) =\\hat \\sigma ( V \\cdot Q) = \\hat \\sigma ( V \\cdot Q \\cdot P) = \\hat \\sigma ( V \\cdot Q \\cdot Q^T) = \\hat \\sigma ( V)\n \\]\nMaterial objectivity \\[\n \\sigma^{(\\mathbf y)} = Q \\cdot \\sigma^{(\\mathbf x)} \\cdot Q^T\n \\]\nGalilean invariance \\[\n \\pmb{\\zeta} = \\mathbf{x} - \\mathbf{v} t\n \\]\n\n\n\n\n\nMass and momentum balance \\[\n\\begin{aligned}\n\\partial_t \\rho + \\nabla \\cdot \\left( \\rho \\mathbf v \\right) &= 0 \\\\\n\\partial_t ( \\rho \\mathbf v ) + \\nabla \\cdot \\left(\\rho \\mathbf v \\otimes \\mathbf v \\right) &= - \\nabla p + \\nabla \\cdot \\pmb{\\tau} + \\rho \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Euler \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf v & = 0\\\\\n\\partial_t \\mathbf v + \\mathbf v \\cdot \\nabla \\mathbf v &= - \\frac{1}{\\rho}\\nabla p + \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf{v}&= 0 \\\\\n\\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} &= - \\frac{1}{\\rho}\\nabla p + \\nu \\triangle \\mathbf{v} + \\mathbf{b}\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes in cylindrical coordinates \\[\n\\begin{aligned}\n\\frac{\\partial u_r}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_r - \\frac{u_\\theta^2}{r} & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial r} + \\nu \\left(\\nabla^2 u_r - \\frac{u_r}{r^2} - \\frac{2}{r^2} \\frac{\\partial u_\\theta}{\\partial \\theta}\\right) \\\\\n\\frac{\\partial u_\\theta}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_\\theta + \\frac{u_r u_\\theta}{r} & = -\\frac{1}{\\rho r} \\frac{\\partial p}{\\partial \\theta} + \\nu \\left(\\nabla^2 u_\\theta + \\frac{2}{r^2} \\frac{\\partial u_r}{\\partial \\theta} - \\frac{u_\\theta}{r^2} \\right) \\\\\n\\frac{\\partial u_z}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_z & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial z} + \\nu \\nabla^2 u_z \\\\\n\\frac{1}{r} \\frac{\\partial}{\\partial r} \\left( r u_r \\right) + \\frac{1}{r} \\frac{\\partial u_\\theta}{\\partial \\theta} + \\frac{\\partial u_z}{\\partial z} & = 0\n\\end{aligned}\n\\]\nHeat equation \\[\n\\partial_t T + \\nabla \\cdot \\left( T \\mathbf{v} \\right) = \\alpha \\Delta T\n\\]\nIncompresisble Navier-Stokes-Boussinesq-Fourier \\[\\begin{align*}\n \\nabla \\cdot \\mathbf{v} & = 0 \\\\\n \\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} & = - \\frac{1}{\\rho_0} \\nabla \\left( p - \\rho_0 g z \\right) + \\nu \\Delta \\mathbf{v} - \\mathbf{g} B \\left(T-T_0\\right) \\\\\n \\partial_t \\left(\\rho c_p T \\right) + \\nabla \\cdot \\left( \\rho c_p T \\mathbf{v} \\right) & = \\nabla \\cdot \\left( \\kappa \\nabla T \\right) + \\mathbf{S}\n\\end{align*}\\]\n\n\n\n\n\nNewtonian fluid \\[\n\\pmb{\\tau} = \\lambda (\\nabla \\cdot \\mathbf v) \\mathbf I + \\eta \\left( \\nabla \\mathbf v + \\nabla \\mathbf v^T \\right),\n\\]\nHooke’s law \\[\n\\pmb{\\sigma} = \\lambda \\mathrm{tr} (\\mathbf{D}) \\mathbf{I} + 2 \\mu \\mathbf{D},\n\\]\nBoussinesq approximation \\[\n\\rho = \\rho_0 (1-B(T-T_0))\n\\]\nStefan condition \\[\n\\rho_s L \\partial_t X_m(t) = -\\kappa \\partial_x T (X_m^{-}(t),t) + \\kappa \\partial_x T (X_m^{+}(t),t)\n\\]",
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- "text": "The course is complemented by weekly exercises. Please consult the semester schedule for dates, unless otherwise notified through Moodle announcements.\nWe split the 90 minutes exercise into two parts:\n\n45 minutes exercise held by the lecturer. We will typically cover\n\na brief lecture summary\ndiscussion of one old exam problem\n\n45 minutes hands-on tutorial and Q&A session. This is your opportunity to\n\nsolve a selected problem on your own\napproach us regarding the current programming homework assignment\n\nDue to time restrictions, we do not plan to discuss the solution of the tutorial problem. We will publish (partial) solutions for these tutorial problems. Feel free to approach us during the tutorial session if you want to make sure that you got the right result!\n\n\n\n\n\n\n\nExercises problems are typically published a couple of days before the exercise session on this website. We encourage students to have a look at the problems in advance.",
+ "text": "Base Units\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDimension\n\\(\\mathrm{T}\\)\n\\(\\mathrm{L}\\)\n\\(\\mathrm{M}\\)\n\\(\\theta\\)\n\\(\\mathrm{N}\\)\n\\(\\mathrm{I}\\)\n\\(I_v\\)\n\n\nUnit\n\\(\\mathrm{s}\\)\n\\(\\mathrm{m}\\)\n\\(\\mathrm{kg}\\)\n\\(\\mathrm{K}\\)\n\\(\\mathrm{mol}\\)\n\\(\\mathrm{A}\\)\n\\(\\mathrm{cd}\\)\n\n\n\n\nDerived units\n\n\n\n\n\\(\\mathrm{Hz}\\)\n\\(\\mathrm{s}^{-1}\\)\n\n\n\\(\\mathrm{N}\\)\n\\(\\mathrm{kg} \\, \\mathrm{m} \\, \\mathrm{s}^{-2}\\)\n\n\n\\(\\mathrm{Pa}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}^{-2}\\)\n\n\n\\(\\mathrm{J}\\)\n\\(\\mathrm{N} \\, \\mathrm{m}\\)\n\n\n\\(\\mathrm{W}\\)\n\\(\\mathrm{J} \\, \\mathrm{s}^{-1}\\)",
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- "text": "Homework Assignments\nIn addition to the weekly exercises, the course is supplemented with biweekly (programming) homework assignments.\nThe homework sheets will be distributed on this website. The publishing dates for the homework assignments can be found in the semester schedule. The deadline is usually one week after the publishing date. The exact deadline can be found on the individual homework assignments.\n\n\n\n\n\n\nCompletion of the homework assignments will grant you bonus points for the final exam.\n\n\n\nThe assignments can be completed in the groups that you will be assigned to in the Moodle course room of this class. The submission of the assignments will also be handled through the Moodle course room.\nDetails regarding the student groups, grading scheme and bonus point policy will be discussed during the first exercise session.\n\n\n\n\n\n\nWarning\n\n\n\nEach homework assignment has a deadline. Submissions after the deadline are not accepted. The submissions cannot be changed after the deadline is expired.\n\n\n\nStudent Groups\nStudents are required to form groups of three students to submit their homework. Groups with less than three members will automatically be merged after the second week of the semester.\n\n\nGrading Scheme\nFor each homework, you can earn up to two points. The points are earned as follows:\n1 Point - Submission (group point) Your submission fulfills the following:\n\nYour homework as uploaded until the deadline expires\nAll tasks in the homework are answered adequately.\nProgramming assignments need to run without error within our provided container on the RWTH Cluster.\nIf the programming homework includes tests, all tests need to pass.\n\n1 Point - Presentation (individual point) You can schedule a group appointment for a presentation in the week after the homework deadline. During this 5 minutes presentation, each present group member will be asked one question from the homework assignment. Which question you will get will be randomized. If you answer the question properly, you will be granted a point.\n\n\nHow can I use the RWTH Cluster for the homework assignments\nSee your wiki entry on ways to work on the homework assignment. In addition, there is a dedicated video on how to get you started published on Moodle.\n\n\nBonus Points Policy\n10% of the total points of the final exam can be gained as bonus points during the semester. These bonus points will be added to the final score of your exam.\n\nBonus point policy\n\n\nHomework points\nExam bonus points\n\n\n\n\n10, 11, 12\n10%\n\n\n9\n9%\n\n\n8\n8%\n\n\n7\n7%\n\n\n6\n6%\n\n\n5\n5%\n\n\n4\n4%\n\n\n3\n3%\n\n\n2\n2%\n\n\n1\n1%",
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+ "text": "Reynold number\nRe\n\\(\\dfrac{v \\, L}{\\nu}\\)\n\n\nFroude number\nFr\n\\(\\dfrac{v}{\\sqrt{gh}}\\)\n\n\nShallowness parameter\n\\(\\epsilon\\)\n\\(\\dfrac{H}{L}\\)\n\n\nPeclet number\nPe\n\\(\\dfrac{L v}{\\alpha}\\)\n\n\nPrandtl number\nPr\n\\(\\dfrac{\\nu}{\\alpha}\\)\n\n\nStefan number\nSte\n\\(\\dfrac{c_p \\Delta T}{h_m}\\)\n\\(h_m:\\) latent heat of melting\n\n\nStrouhal number\nStr\n\\(\\dfrac{f L}{v}\\)\n\n\nMach number\nMa\n\\(\\dfrac{v}{c}\\)\n\n\nLewis number\nLe\n\\(\\dfrac{\\alpha}{D}\\)",
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+ "text": "Shape factor \\[\n\\alpha = \\frac{\\overline{u^2}}{\\overline{u}^2}, \\; \\text{where} \\;\n\\bar \\ast = (1/h) \\int_b^s \\ast \\,dz\n\\]\nDecomposition of the velocity profile \\[\n \\mathbf{v}(\\mathbf{x} + \\mathbf{r}) = \\mathbf{v}(\\mathbf{x}) + \\mathbf{w} \\times \\mathbf{r} + \\mathbf{D} \\cdot \\mathbf{r}\n\\] \\[\n\\text{where} \\;\\; \\mathbf{w} = \\frac{1}{2} \\nabla \\times \\mathbf{v}, \\;\n\\mathbf{D} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} + \\nabla \\mathbf{v}^T \\right), \\;\n\\mathbf{W} = \\frac{1}{2} \\left( \\nabla \\mathbf{v} - \\nabla \\mathbf{v}^T \\right)\n\\]\nCylindrical coordinate transformation rules \\[\n\\begin{aligned}\n\\nabla f & = \\frac{\\partial f}{\\partial r } \\vec{e}_r + \\frac{1}{r} \\frac{\\partial f}{\\partial \\theta} \\vec{e}_\\theta+ \\frac{\\partial f}{\\partial z} \\vec{e}_z, \\\\\n\\nabla \\cdot \\vec{A} & = \\frac{1}{r}\\frac{\\partial}{\\partial r }(r A_r) + \\frac{1}{r} \\frac{\\partial A_\\theta}{\\partial \\theta} + \\frac{\\partial A_z}{\\partial z} \\\\\n\\nabla \\times \\vec{A} & = \\left(\\frac{1}{r}\\frac{\\partial A_z}{\\partial \\theta}-\\frac{\\partial A_\\theta}{\\partial z}\\right)\\vec{e}_r+ \\left(\\frac{\\partial A_r}{\\partial z}-\\frac{\\partial A_z}{\\partial r}\\right)\\vec{e}_\\theta+\\frac{1}{r}\\left(\\frac{\\partial}{\\partial r}(r A_\\theta)-\\frac{\\partial A_r}{\\partial \\theta}\\right) \\vec{e}_z \n\\end{aligned}\n\\]\nMaterial derivative \\[\n\\frac{D}{Dt} f = \\partial_t f + \\mathbf{v} \\cdot \\nabla f\n\\]\nBernoulli equation \\[\n\\tfrac{p}{\\rho} + \\tfrac{v^2}{2} + g z = const. along a streamline\n\\]\nError function \\[\n\\begin{aligned}\n\\text{erf} (x) := \\frac{2}{\\sqrt{\\pi}} \\int_0^x e^{-y^2} dy, \\;\\;\\;\\; &\\text{erfc} (x) := 1 - \\text{erf}(x)\\\\\n\\partial_x \\text{erf} (C x) = \\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}, \\;\\;\\;\\; &\\partial_x \\text{erfc} (C x) = -\\frac{2}{\\sqrt{\\pi}} C e^{-(C x)^2}\\\\\n\\end{aligned}\n\\]\nThermal diffusivity \\[\n\\alpha = \\frac{\\kappa}{\\rho c_p}\n\\]",
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+ "text": "Material symmetry \\[\n \\hat \\sigma^{(\\zeta)} (*) = \\hat \\sigma^{(\\eta)} (* P).\n \\]\nMaterial Isotropy \\[\n \\hat {\\sigma} (F) =\\hat \\sigma ( V \\cdot Q) = \\hat \\sigma ( V \\cdot Q \\cdot P) = \\hat \\sigma ( V \\cdot Q \\cdot Q^T) = \\hat \\sigma ( V)\n \\]\nMaterial objectivity \\[\n \\sigma^{(\\mathbf y)} = Q \\cdot \\sigma^{(\\mathbf x)} \\cdot Q^T\n \\]\nGalilean invariance \\[\n \\pmb{\\zeta} = \\mathbf{x} - \\mathbf{v} t\n \\]",
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+ "text": "Mass and momentum balance \\[\n\\begin{aligned}\n\\partial_t \\rho + \\nabla \\cdot \\left( \\rho \\mathbf v \\right) &= 0 \\\\\n\\partial_t ( \\rho \\mathbf v ) + \\nabla \\cdot \\left(\\rho \\mathbf v \\otimes \\mathbf v \\right) &= - \\nabla p + \\nabla \\cdot \\pmb{\\tau} + \\rho \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Euler \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf v & = 0\\\\\n\\partial_t \\mathbf v + \\mathbf v \\cdot \\nabla \\mathbf v &= - \\frac{1}{\\rho}\\nabla p + \\mathbf b\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes \\[\n\\begin{aligned}\n\\nabla \\cdot \\mathbf{v}&= 0 \\\\\n\\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} &= - \\frac{1}{\\rho}\\nabla p + \\nu \\triangle \\mathbf{v} + \\mathbf{b}\n\\end{aligned}\n\\]\nIncompressible Navier-Stokes in cylindrical coordinates \\[\n\\begin{aligned}\n\\frac{\\partial u_r}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_r - \\frac{u_\\theta^2}{r} & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial r} + \\nu \\left(\\nabla^2 u_r - \\frac{u_r}{r^2} - \\frac{2}{r^2} \\frac{\\partial u_\\theta}{\\partial \\theta}\\right) \\\\\n\\frac{\\partial u_\\theta}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_\\theta + \\frac{u_r u_\\theta}{r} & = -\\frac{1}{\\rho r} \\frac{\\partial p}{\\partial \\theta} + \\nu \\left(\\nabla^2 u_\\theta + \\frac{2}{r^2} \\frac{\\partial u_r}{\\partial \\theta} - \\frac{u_\\theta}{r^2} \\right) \\\\\n\\frac{\\partial u_z}{\\partial t} + \\left(\\vec{u}\\cdot \\vec\\nabla \\right) u_z & = -\\frac{1}{\\rho} \\frac{\\partial p}{\\partial z} + \\nu \\nabla^2 u_z \\\\\n\\frac{1}{r} \\frac{\\partial}{\\partial r} \\left( r u_r \\right) + \\frac{1}{r} \\frac{\\partial u_\\theta}{\\partial \\theta} + \\frac{\\partial u_z}{\\partial z} & = 0\n\\end{aligned}\n\\]\nHeat equation \\[\n\\partial_t T + \\nabla \\cdot \\left( T \\mathbf{v} \\right) = \\alpha \\Delta T\n\\]\nIncompresisble Navier-Stokes-Boussinesq-Fourier \\[\\begin{align*}\n \\nabla \\cdot \\mathbf{v} & = 0 \\\\\n \\partial_t \\mathbf{v} + \\mathbf{v} \\cdot \\nabla \\mathbf{v} & = - \\frac{1}{\\rho_0} \\nabla \\left( p - \\rho_0 g z \\right) + \\nu \\Delta \\mathbf{v} - \\mathbf{g} B \\left(T-T_0\\right) \\\\\n \\partial_t \\left(\\rho c_p T \\right) + \\nabla \\cdot \\left( \\rho c_p T \\mathbf{v} \\right) & = \\nabla \\cdot \\left( \\kappa \\nabla T \\right) + \\mathbf{S}\n\\end{align*}\\]",
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+ "text": "Newtonian fluid \\[\n\\pmb{\\tau} = \\lambda (\\nabla \\cdot \\mathbf v) \\mathbf I + \\eta \\left( \\nabla \\mathbf v + \\nabla \\mathbf v^T \\right),\n\\]\nHooke’s law \\[\n\\pmb{\\sigma} = \\lambda \\mathrm{tr} (\\mathbf{D}) \\mathbf{I} + 2 \\mu \\mathbf{D},\n\\]\nBoussinesq approximation \\[\n\\rho = \\rho_0 (1-B(T-T_0))\n\\]\nStefan condition \\[\n\\rho_s L \\partial_t X_m(t) = -\\kappa \\partial_x T (X_m^{-}(t),t) + \\kappa \\partial_x T (X_m^{+}(t),t)\n\\]",
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+ "text": "Tasks\n\nTask 1\nSketch two trajectories for \\(t \\in (0, 2 \\pi)\\) as well as their respective field values \\(\\phi(\\mathbf X(t), t)\\) over time.\n\n\nTask 2\nDescribe \\(\\phi(\\mathbf X(t), t)\\) in the Eulerian frame.\n\n\nTask 3\nConsider another field given by \\(\\psi(\\mathbf X(t), t) = X^2 + Y^2\\). Rewrite \\(\\psi\\) in the Eulerian frame.\n\n\nTask 4\nCompute \\(\\frac{d\\phi}{dt}\\) and \\(\\frac{D\\phi}{dt}\\) using their definitions."
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+ "text": "Tasks\n\nTask 1\nCompute the pathlines.\n\n\nTask 2\nCompute the streamlines.\n\n\nTask 3\nCompute the streaklines.\n\n\nTask 4\nSketch the velocity field for\n\n\\(t_0 < \\frac{k x}{\\alpha}\\)\n\\(t_1 = \\frac{k x}{\\alpha}\\)\n\\(t_2 > \\frac{k x}{\\alpha}\\)\n\nand furthermore add\n\nthe pathline for one particular \\(\\mathbf x_0\\)\none particular streamline in each figure\nthe streakline starting at \\(t_0\\) for one particular \\(\\mathbf x_0\\)."
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"objectID": "content/wiki/authoring.html",
"href": "content/wiki/authoring.html",