{ "cells": [ { "cell_type": "markdown", "id": "f61615cd", "metadata": { "tags": [ "header" ] }, "source": [ "# Lecture 4, notebook L4B — the cavity revisited; MINRES with the pressure mass matrix\n", "\n", "Companion to slides 24–25 (Demonstration 2) and 29 (Demonstration 3,\n", "self-study), the slide-29 spectrum figure, and the backup frames B5 and\n", "B6 (add-backs). Conventions as in `L4A` (top-to-bottom\n", "execution, second-solve timings, fallback figures, **[verified]** markers).\n", "Iteration counts and dof counts carry the marker **[record]**: they fed\n", "the v0.5 slide pins. Deck v0.6.2 replaced the iteration pins by the\n", "reference-implementation record **[V7]** (manufactured problem,\n", "triangular meshes); the cells below remain the live self-study\n", "verification of the same flatness assertion. The Demonstration 2\n", "pressure range still feeds the slide-24 and B6 pins.\n", "\n", "**Configuration [P-L1B], resolved (2026-07-12).** The Lecture-1\n", "configuration is known ([V8]): quadrilateral mesh, $N = 32$,\n", "$\\varepsilon = 10^{-8}$, and the quartic lid $u_x = 16x^2(1-x)^2$. The\n", "setup cell carries these values, and `USE_QUADS = True` is the ruling of\n", "2026-07-11 — on the quadrilateral mesh \"Taylor–Hood\" means $Q_2$–$Q_1$,\n", "uniformly stable (Boffi–Brezzi–Fortin); Theorem 42 covers the simplicial\n", "case, as slide 24 states. The executed 2026-07-12 run used the raw\n", "constant lid, whose corner nodes were overridden to zero by the wall\n", "boundary conditions listed after it; the resulting datum has\n", "checkerboard component exactly $h$, which produced\n", "$\\max|p_h| = h/\\varepsilon = 3.125\\cdot 10^{6}$ (diagnosis [V8]).\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "ac71fae1", "metadata": { "tags": [ "setup" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Firedrake 2026.4.1 (PETSc 3.25.0) ready; figures directory: figs/\n" ] } ], "source": [ "# Setup 1/2: Firedrake (FEM-on-Colab guard) and imports.\n", "try:\n", " import firedrake\n", "except ImportError:\n", " import subprocess, urllib.request\n", " urllib.request.urlretrieve(\n", " \"https://fem-on-colab.github.io/releases/firedrake-install-release-real.sh\",\n", " \"/tmp/firedrake-install.sh\")\n", " subprocess.run([\"bash\", \"/tmp/firedrake-install.sh\"], check=True)\n", " import firedrake\n", "\n", "from firedrake import *\n", "from firedrake.pyplot import tripcolor, streamplot\n", "import numpy as np\n", "import scipy.linalg as sla\n", "import scipy.sparse as sp\n", "import scipy.sparse.linalg as spsla\n", "import matplotlib.pyplot as plt\n", "import os, time\n", "os.makedirs(\"figs\", exist_ok=True)\n", "import logging\n", "# The manufactured data has polynomial degree up to twelve; against P1\n", "# arguments TSFC warns that its (correct) quadrature estimate exceeds\n", "# the argument degree tenfold. The estimate is what exact integration\n", "# requires, so the warning is cosmetic and silenced here.\n", "logging.getLogger(\"tsfc\").setLevel(logging.ERROR)\n", "try:\n", " _fd_version = firedrake.__version__\n", "except AttributeError:\n", " try:\n", " from importlib.metadata import version as _pkg_version\n", " _fd_version = _pkg_version(\"firedrake\")\n", " except Exception:\n", " _fd_version = \"unknown\"\n", "from firedrake.petsc import PETSc as _PETSc\n", "_petsc = \".\".join(map(str, _PETSc.Sys.getVersion()))\n", "print(f\"Firedrake {_fd_version} (PETSc {_petsc}) ready; \"\n", " \"figures directory: figs/\")\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "814a4110", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "definitions loaded: N_CAVITY, USE_QUADS, EPS_L1, lid_expression, cavity_mesh, cavity_spaces, cavity_problem, centers, eval_at, plot_pressure\n" ] } ], "source": [ "# Setup 2/2: the cavity builder and the plotting pipeline.\n", "N_CAVITY = 32 # the L1B value ([V8], resolved 2026-07-12).\n", "USE_QUADS = True # ruling 2026-07-11: the L1B quadrilateral mesh.\n", "EPS_L1 = 1e-8 # the Lecture-1 epsilon ([V8]), for backup B6.\n", "\n", "def lid_expression(mesh):\n", " # The L1B quartic lid ([V8]); it vanishes at the corners, so the\n", " # bc-ordering corner override that produced the step-lid datum of\n", " # the 2026-07-12 run is now inert.\n", " x, _ = SpatialCoordinate(mesh)\n", " return as_vector((16 * x**2 * (1 - x)**2, 0.0))\n", "\n", "def cavity_mesh(N=N_CAVITY, use_quads=USE_QUADS):\n", " return UnitSquareMesh(N, N, quadrilateral=use_quads)\n", "\n", "def cavity_spaces(mesh, pair):\n", " if pair == \"TH\": # P2-P1 on triangles, Q2-Q1 on quads\n", " V = VectorFunctionSpace(mesh, \"CG\", 2)\n", " Q = FunctionSpace(mesh, \"CG\", 1)\n", " elif pair == \"Q1P0\":\n", " V = VectorFunctionSpace(mesh, \"CG\", 1)\n", " Q = FunctionSpace(mesh, \"DQ\", 0)\n", " else:\n", " raise ValueError(pair)\n", " return V * Q\n", "\n", "def cavity_problem(mesh, pair, nu_val=1.0, eps=0.0, aP_mass=False):\n", " \"\"\"Forms, boundary conditions, nullspace for the lid-driven cavity.\n", " Boundary ids of UnitSquareMesh: 1 (x=0), 2 (x=1), 3 (y=0), 4 (y=1);\n", " the lid is 4. aP_mass=True supplies the preconditioning form\n", " Jp = nu (grad u, grad v) + nu^{-1} (p, q).\"\"\"\n", " W = cavity_spaces(mesh, pair)\n", " w = Function(W)\n", " u, p = TrialFunctions(W); v, q = TestFunctions(W)\n", " nuc = Constant(nu_val)\n", " a = nuc*inner(grad(u), grad(v))*dx - p*div(v)*dx - q*div(u)*dx\n", " if eps:\n", " a = a - Constant(eps)*p*q*dx\n", " L = inner(Constant((0.0, 0.0)), v)*dx\n", " bcs = [DirichletBC(W.sub(0), lid_expression(mesh), 4),\n", " DirichletBC(W.sub(0), Constant((0.0, 0.0)), (1, 2, 3))]\n", " ns = None\n", " if eps == 0.0:\n", " ns = MixedVectorSpaceBasis(\n", " W, [W.sub(0), VectorSpaceBasis(constant=True, comm=W.comm)])\n", " aP = None\n", " if aP_mass:\n", " aP = nuc*inner(grad(u), grad(v))*dx + (1.0/nuc)*p*q*dx\n", " return W, w, a, L, bcs, ns, aP\n", "\n", "def centers(N):\n", " xs = (np.arange(N) + 0.5) / N\n", " return [(float(a), float(b)) for b in xs for a in xs]\n", "\n", "def eval_at(f, pts):\n", " # Function.at is deprecated in favour of PointEvaluator; the call is\n", " # kept for portability across Firedrake versions and the warning is\n", " # silenced. Migrate to PointEvaluator once the course baseline\n", " # includes it.\n", " import warnings\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\", FutureWarning)\n", " return np.asarray(f.at(pts, tolerance=1e-10))\n", "\n", "def plot_pressure(ph, fname, title, N=N_CAVITY):\n", " # The L1B plotting pipeline: cell-center grid, cividis colormap,\n", " # symmetric limits. The mean is removed first (the nullspace solve\n", " # fixes the constant arbitrarily); this shifts colours only.\n", " pgrid = eval_at(ph, centers(N)).reshape(N, N)\n", " pgrid = pgrid - pgrid.mean()\n", " fig, ax = plt.subplots(figsize=(4.2, 3.4))\n", " vm = np.abs(pgrid).max()\n", " im = ax.imshow(pgrid, origin=\"lower\", cmap=\"cividis\", vmin=-vm,\n", " vmax=vm, extent=[0, 1, 0, 1], interpolation=\"nearest\")\n", " fig.colorbar(im, ax=ax); ax.set_aspect(\"equal\")\n", " ax.set_title(title, fontsize=9)\n", " for ext in (\"png\", \"pdf\"):\n", " fig.savefig(f\"figs/{fname}.{ext}\", dpi=200, bbox_inches=\"tight\")\n", " plt.show()\n", "\n", "print(\"definitions loaded: N_CAVITY, USE_QUADS, EPS_L1, lid_expression, \"\n", " \"cavity_mesh, cavity_spaces, cavity_problem, centers, eval_at, \"\n", " \"plot_pressure\")\n" ] }, { "cell_type": "markdown", "id": "343251f9", "metadata": { "tags": [ "demo2" ] }, "source": [ "## Demonstration 2 (slides 24–25) — the cavity, revisited\n", "\n", "The Lecture-1 cavity, discretized with Taylor–Hood; direct solve. The\n", "constant pressure is removed through a **nullspace**, not by pinning a\n", "degree of freedom: pinning replaces the consistent singular system by a\n", "nearby unsymmetric one and distorts the spectrum that frames 28–29\n", "analyse, while the nullspace keeps the operator symmetric, which is what\n", "MINRES requires.\n", "\n", "The pressure range printed below is the slide-24 pin **[record]**. What\n", "changed relative to Lecture 1 is the pair and nothing else.\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "b4abae31", "metadata": { "tags": [ "demo2" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pressure range (zero mean): [-9.8571, 9.8571] [record: slide 24 / B6 pin]\n" ] }, { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Demo 2: Taylor-Hood cavity, pressure and streamlines.\n", "mesh = cavity_mesh()\n", "W, w, a, L, bcs, ns, _ = cavity_problem(mesh, \"TH\")\n", "solve(a == L, w, bcs=bcs, nullspace=ns,\n", " solver_parameters={\"mat_type\": \"aij\", \"ksp_type\": \"preonly\",\n", " \"pc_type\": \"lu\",\n", " \"pc_factor_mat_solver_type\": \"mumps\"})\n", "uh, ph = w.subfunctions\n", "pbar = assemble(ph*dx)\n", "pdata = ph.dat.data - pbar\n", "print(f\"pressure range (zero mean): [{pdata.min():.4f}, {pdata.max():.4f}]\"\n", " \" [record: slide 24 / B6 pin]\")\n", "plot_pressure(ph, \"l4_cavity_p\", \"Taylor-Hood cavity: pressure\")\n", "\n", "# The L1B streamline pipeline: vertex grid, matplotlib streamplot.\n", "xs = np.linspace(0, 1, N_CAVITY + 1)\n", "upts = [(float(a), float(b)) for b in xs for a in xs]\n", "uv = eval_at(uh, upts)\n", "UX = uv[:, 0].reshape(N_CAVITY + 1, N_CAVITY + 1)\n", "UY = uv[:, 1].reshape(N_CAVITY + 1, N_CAVITY + 1)\n", "fig, ax = plt.subplots(figsize=(4.2, 3.4))\n", "X, Y = np.meshgrid(xs, xs)\n", "ax.streamplot(X, Y, UX, UY, color=\"tab:blue\", density=1.1, linewidth=0.9)\n", "ax.set_aspect(\"equal\"); ax.set_title(\"Taylor-Hood cavity: streamlines\", fontsize=9)\n", "for ext in (\"png\", \"pdf\"):\n", " fig.savefig(f\"figs/l4_cavity_stream.{ext}\", dpi=200, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, { "cell_type": "markdown", "id": "04459667", "metadata": { "tags": [ "demo3" ] }, "source": [ "## Demonstration 3 (slide 29, self-study) — MINRES with the pressure mass matrix\n", "\n", "$\\mathsf P = \\operatorname{diag}(\\mathsf A,\\ \\nu^{-1}\\mathsf M_p)$ is\n", "supplied through the auxiliary bilinear form\n", "$J_p = \\nu(\\nabla u, \\nabla v) + \\nu^{-1}(p, q)$; a fieldsplit\n", "preconditioner with an LU factorization per block realizes the exact block\n", "inverses of the slides. MINRES, relative tolerance $10^{-8}$.\n", "\n", "These cells are not executed during the lecture. Slide 29 states\n", "Corollary 46 and quotes the reference-implementation record **[V7]**.\n", "The cells below run the same solver on the cavity; the counts differ\n", "from [V7] because the linear system differs (manufactured problem\n", "there, cavity here), and the assertion — a flat count under refinement\n", "— is the same. Timings are quoted from second solves (the first\n", "includes JIT).\n" ] }, { "cell_type": "code", "execution_count": null, "id": "0dd2f987", "metadata": { "tags": [ "demo3" ] }, "outputs": [], "source": [ "# Demo 3: iteration counts under refinement.\n", "params = {\"mat_type\": \"aij\", \"ksp_type\": \"minres\", \"ksp_rtol\": 1e-8,\n", " \"pc_type\": \"fieldsplit\", \"pc_fieldsplit_type\": \"additive\",\n", " \"fieldsplit_0_ksp_type\": \"preonly\", \"fieldsplit_0_pc_type\": \"lu\",\n", " \"fieldsplit_1_ksp_type\": \"preonly\", \"fieldsplit_1_pc_type\": \"lu\"}\n", "\n", "print(\" N unknowns MINRES its second solve [s]\")\n", "for N in (16, 32, 64):\n", " mesh = cavity_mesh(N)\n", " W, w, a, L, bcs, ns, aP = cavity_problem(mesh, \"TH\", aP_mass=True)\n", " problem = LinearVariationalProblem(a, L, w, bcs=bcs, aP=aP)\n", " solver = LinearVariationalSolver(problem, nullspace=ns,\n", " solver_parameters=params)\n", " solver.solve() # first solve: includes JIT\n", " w.assign(0)\n", " t0 = time.perf_counter(); solver.solve(); t1 = time.perf_counter()\n", " its = solver.snes.ksp.getIterationNumber()\n", " print(f\"{N:3d} {W.dim():8d} {its:6d} {t1-t0:8.3f}\"\n", " \" [self-study]\")\n" ] }, { "cell_type": "markdown", "id": "cab3fb83", "metadata": { "tags": [ "addbacks" ] }, "source": [ "## The $\\nu$-sweep (backup B5), the spectrum figure (slide 29), and the $Q_1$–$P_0$ rerun (backup B6)\n", "\n", "**Backup B5 — $\\nu$-robustness (self-study).** The preconditioned\n", "spectrum does not move: Theorem 45 is $\\nu$-free as stated. Variations\n", "of the counts across $\\nu$, if observed, reflect the $\\nu$-dependence\n", "of the effective load (here the lid lifting), not of the\n", "preconditioner; the reference-implementation control [V7c] isolates\n", "this on the manufactured problem.\n", "\n", "**The spectrum figure (slide 29).** Eigenvalues of\n", "$\\mathsf P^{-1}\\mathsf K$ at $N = 8$ on zero-mean pressures.\n", "**Expected [verified]** (structured triangular mesh, all-Dirichlet\n", "velocity; boundary values do not enter the matrices, so the enclosed\n", "problem and the cavity share them): Taylor–Hood — negative interval\n", "$[-0.6179, -0.1198]$, the cluster at $1$, positive interval\n", "$[1.1198, 1.6179]$, matching Corollary 46 with the measured\n", "$\\beta_h^2 = 0.1341$; $P_1$–$P_1$ — seven eigenvalues at $0$ and the\n", "gap at the origin closed to $\\pm 0.0051$. This cell regenerates\n", "`figs/l4_spectrum`.\n", "\n", "**Backup B6 — $Q_1$–$P_0$ beside Taylor–Hood.** The Lecture-1 rerun.\n", "Here the lid lifting *does* supply a kernel-incompatible constraint\n", "datum (Demonstration 1c in `L4A`), so the $\\varepsilon$-shift produces\n", "the checkerboard. **Expected [verified]** with the Lecture-1 lid ([V8]): checkerboard\n", "amplitude $762.94$ (sign depends on the orientation of the parity\n", "vector) and a fraction close to one under the $\\ell^2$ functional\n", "used here; the Lecture-1 record's $99.7\\%$ is computed with the\n", "Lecture-1 fraction functional. The maximum decomposes visibly:\n", "$\\max|p_h| \\approx 762.94 + 9.5$, the mode plus the physical\n", "pressure.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "d522c6d1", "metadata": { "tags": [ "addback-B4" ] }, "outputs": [], "source": [ "# Backup B5: nu-sweep at N = 32, exact blocks (self-study).\n", "mesh = cavity_mesh(32)\n", "print(\" nu MINRES its\")\n", "for nu_val in (1.0, 1e-2, 1e-4):\n", " W, w, a, L, bcs, ns, aP = cavity_problem(mesh, \"TH\", nu_val=nu_val,\n", " aP_mass=True)\n", " problem = LinearVariationalProblem(a, L, w, bcs=bcs, aP=aP)\n", " solver = LinearVariationalSolver(problem, nullspace=ns,\n", " solver_parameters=params)\n", " solver.solve()\n", " print(f\" {nu_val:7.0e} {solver.snes.ksp.getIterationNumber():4d}\"\n", " \" [self-study]\")\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e466f492", "metadata": { "tags": [ "addback-B3" ] }, "outputs": [], "source": [ "# The slide-29 spectrum figure: P^{-1}K at N = 8 (triangular mesh).\n", "def petsc_to_csr(A):\n", " ai, aj, av = A.petscmat.getValuesCSR()\n", " return sp.csr_matrix((av, aj, ai), shape=A.petscmat.getSize())\n", "\n", "def spectrum(pair):\n", " mesh = UnitSquareMesh(8, 8, diagonal=\"right\") # convention;\n", " # the spectrum is identical across diagonal families.\n", " if pair == \"TH\":\n", " V = VectorFunctionSpace(mesh, \"CG\", 2)\n", " else:\n", " V = VectorFunctionSpace(mesh, \"CG\", 1)\n", " Q = FunctionSpace(mesh, \"CG\", 1)\n", " u, v = TrialFunction(V), TestFunction(V)\n", " p, q = TrialFunction(Q), TestFunction(Q)\n", " KV = petsc_to_csr(assemble(inner(grad(u), grad(v))*dx, mat_type=\"aij\"))\n", " Bm = petsc_to_csr(assemble(-div(u)*q*dx, mat_type=\"aij\"))\n", " Mp = petsc_to_csr(assemble(p*q*dx, mat_type=\"aij\"))\n", " bd = DirichletBC(V, 0, \"on_boundary\").nodes\n", " mask = np.ones(V.dim(), bool)\n", " for c in range(V.value_size):\n", " mask[V.value_size*np.asarray(bd) + c] = False\n", " iv = np.nonzero(mask)[0]\n", " A_ = KV[iv][:, iv].toarray(); Bi = Bm[:, iv].toarray()\n", " Kfull = np.block([[A_, Bi.T], [Bi, np.zeros((Bi.shape[0],)*2)]])\n", " Pfull = sla.block_diag(A_, Mp.toarray())\n", " m = np.asarray(Mp @ np.ones(Mp.shape[0])).ravel()\n", " Z = sla.null_space(m[None, :])\n", " Wb = sla.block_diag(np.eye(A_.shape[0]), Z)\n", " return np.sort(sla.eigh(Wb.T @ Kfull @ Wb, Wb.T @ Pfull @ Wb,\n", " eigvals_only=True))\n", "\n", "fig, axs = plt.subplots(1, 2, figsize=(7.4, 2.6), sharey=True)\n", "for ax, pair, title in ((axs[0], \"TH\", \"Taylor-Hood\"),\n", " (axs[1], \"P1\", \"$P_1$-$P_1$\")):\n", " lam = spectrum(pair)\n", " ax.plot(lam, np.zeros_like(lam), \"|\", ms=14, mew=1.1, color=\"#1F2A36\")\n", " for x in ((1-5**0.5)/2, (1+5**0.5)/2, 0.0, 1.0):\n", " ax.axvline(x, color=\"#8F1D2C\", lw=0.6, ls=\":\", alpha=0.7)\n", " ax.set_title(title + r\", $N=8$\", fontsize=9)\n", " ax.set_yticks([]); ax.set_xlim(-0.75, 1.75); ax.tick_params(labelsize=8)\n", " if pair == \"TH\":\n", " neg = lam[lam < -1e-10]\n", " pos = lam[(lam > 1e-10) & (np.abs(lam-1) > 1e-8)]\n", " print(f\"TH : neg in [{neg.min():.4f}, {neg.max():.4f}], \"\n", " f\"pos in [{pos.min():.4f}, {pos.max():.4f}]\"\n", " \" [verified: -0.6179, -0.1198, 1.1198, 1.6179]\")\n", " else:\n", " print(f\"P1 : #(lam = 0) = {int((np.abs(lam) < 1e-10).sum())}, \"\n", " f\"gap edges {lam[np.abs(lam) > 1e-10].min():.4f} ... \"\n", " \" [verified: 7 zeros, +-0.0051 next to 0 and 1]\")\n", "fig.suptitle(r\"eigenvalues of $\\mathsf{P}^{-1}\\mathsf{K}$ on zero-mean pressures\",\n", " fontsize=9, y=1.02)\n", "fig.tight_layout()\n", "for ext in (\"pdf\", \"png\"):\n", " fig.savefig(f\"figs/l4_spectrum.{ext}\", dpi=200, bbox_inches=\"tight\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "1e6d2c4e", "metadata": { "tags": [ "addback-B5" ] }, "outputs": [], "source": [ "# Backup B6: Q1-P0 cavity, eps-shift -- the Lecture-1 rerun (quadrilaterals).\n", "mesh_q = cavity_mesh(N_CAVITY, use_quads=True)\n", "W, w, a, L, bcs, ns, _ = cavity_problem(mesh_q, \"Q1P0\", eps=EPS_L1)\n", "solve(a == L, w, bcs=bcs,\n", " solver_parameters={\"mat_type\": \"aij\", \"ksp_type\": \"preonly\",\n", " \"pc_type\": \"lu\"})\n", "uh, ph = w.subfunctions\n", "pbar = assemble(ph*dx)\n", "pdata = ph.dat.data - pbar\n", "# checkerboard projection: cell parity from the cell midpoints (DQ0 dofs)\n", "midf = Function(VectorFunctionSpace(mesh_q, \"DQ\", 0))\n", "midf.interpolate(SpatialCoordinate(mesh_q))\n", "mid = midf.dat.data\n", "parity = ((np.floor(mid[:, 0]*N_CAVITY) + np.floor(mid[:, 1]*N_CAVITY))\n", " % 2) * 2 - 1\n", "chk = parity / np.linalg.norm(parity)\n", "frac = abs(pdata @ chk) / np.linalg.norm(pdata)\n", "amp_cb = float((pdata * parity).mean()) # the L1 amplitude functional\n", "print(f\"checkerboard amplitude = {amp_cb:.4f}; max|p_h| = \"\n", " f\"{np.abs(pdata).max():.4f}; checkerboard fraction = {100*frac:.1f}%\")\n", "print(\"[V8] expected with the Lecture-1 lid: |amplitude| = 762.94 \"\n", " \"(fraction close to one under this functional)\")\n", "plot_pressure(ph, \"l4_cavity_q1p0\", \"Q1-P0 cavity, eps-shift: checkerboard\")\n" ] }, { "cell_type": "markdown", "id": "84c98e53", "metadata": { "tags": [ "closing" ] }, "source": [ "## What feeds the deck\n", "\n", "Generated here: `figs/l4_cavity_p`, `figs/l4_cavity_stream`,\n", "`figs/l4_spectrum`, `figs/l4_cavity_q1p0` (deck build note [P2]).\n", "Recorded here for the slide pins: the Demonstration 2 pressure range\n", "(slide 24 and backup B6; deck build note [P1]). The Demonstration 3\n", "tables and the B5 sweep are self-study verification; the deck quotes\n", "the reference-implementation record [V7]. If Firedrake is unavailable in the lecture\n", "room, the committed figures are the fallback (course rule).\n" ] } ], "metadata": { "kernelspec": { "display_name": "Firedrake", "language": "python", "name": "firedrake" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }