chapter2/helmholtz_code.ipynb

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    "# Implementation\n",
    "Author: Antonio Baiano Svizzero and Jørgen S. Dokken\n",
    "\n",
    "In this tutorial, you will learn how to:\n",
    "- Define acoustic velocity and impedance boundary conditions\n",
    "- Compute acoustic sound pressure for multiple frequencies\n",
    "- Compute the Sound Pressure Level (SPL) at a given microphone position\n",
    "\n",
    "## Test problem\n",
    "As an example, we will model a plane wave propagating in a tube.\n",
    "While it is a basic test case, the code can be adapted to way more complex problems where velocity and impedance boundary conditions are needed.\n",
    "We will apply a velocity boundary condition $v_n = 0.001$ to one end of the tube (for the sake of simplicity, in this basic example, we are ignoring the point source, which can be applied with scifem) and an impedance $Z$ computed with the Delaney-Bazley model,\n",
    "supposing that a layer of thickness $d = 0.02$ and flow resistivity $\\sigma = 1e4$ is placed at the second end of the tube.\n",
    "The choice of such impedance (the one of a plane wave propagating in free field) will give, as a result, a solution with no reflections.\n",
    "\n",
    "First, we create the mesh with gmsh, also setting the physical group for velocity and impedance boundary conditions and the respective tags."
   ]
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   "cell_type": "code",
   "execution_count": 1,
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info    : Meshing 1D...\n",
      "Info    : [  0%] Meshing curve 1 (Line)\n",
      "Info    : [ 10%] Meshing curve 2 (Line)\n",
      "Info    : [ 20%] Meshing curve 3 (Line)\n",
      "Info    : [ 30%] Meshing curve 4 (Line)\n",
      "Info    : [ 40%] Meshing curve 5 (Line)\n",
      "Info    : [ 50%] Meshing curve 6 (Line)\n",
      "Info    : [ 60%] Meshing curve 7 (Line)\n",
      "Info    : [ 60%] Meshing curve 8 (Line)\n",
      "Info    : [ 70%] Meshing curve 9 (Line)\n",
      "Info    : [ 80%] Meshing curve 10 (Line)\n",
      "Info    : [ 90%] Meshing curve 11 (Line)\n",
      "Info    : [100%] Meshing curve 12 (Line)\n",
      "Info    : Done meshing 1D (Wall 0.000926869s, CPU 0.000589s)\n",
      "Info    : Meshing 2D...\n",
      "Info    : [  0%] Meshing surface 1 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 20%] Meshing surface 2 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 40%] Meshing surface 3 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 60%] Meshing surface 4 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 70%] Meshing surface 5 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 90%] Meshing surface 6 (Plane, Frontal-Delaunay)\n",
      "Info    : Done meshing 2D (Wall 0.0311526s, CPU 0.030342s)\n",
      "Info    : Meshing 3D...\n",
      "Info    : 3D Meshing 1 volume with 1 connected component\n",
      "Info    : Tetrahedrizing 1285 nodes...\n",
      "Info    : Done tetrahedrizing 1293 nodes (Wall 0.0127086s, CPU 0.011753s)\n",
      "Info    : Reconstructing mesh...\n",
      "Info    :  - Creating surface mesh\n",
      "Info    :  - Identifying boundary edges\n",
      "Info    :  - Recovering boundary\n",
      "Info    : Done reconstructing mesh (Wall 0.0264354s, CPU 0.02449s)\n",
      "Info    : Found volume 1\n",
      "Info    : It. 0 - 0 nodes created - worst tet radius 2.85527 (nodes removed 0 0)\n",
      "Info    : 3D refinement terminated (1746 nodes total):\n",
      "Info    :  - 0 Delaunay cavities modified for star shapeness\n",
      "Info    :  - 0 nodes could not be inserted\n",
      "Info    :  - 6588 tetrahedra created in 0.0199287 sec. (330577 tets/s)\n",
      "Info    : 0 node relocations\n",
      "Info    : Done meshing 3D (Wall 0.0772706s, CPU 0.07553s)\n",
      "Info    : Optimizing mesh...\n",
      "Info    : Optimizing volume 1\n",
      "Info    : Optimization starts (volume = 0.01) with worst = 0.0138257 / average = 0.769909:\n",
      "Info    : 0.00 < quality < 0.10 :        18 elements\n",
      "Info    : 0.10 < quality < 0.20 :        69 elements\n",
      "Info    : 0.20 < quality < 0.30 :        66 elements\n",
      "Info    : 0.30 < quality < 0.40 :        67 elements\n",
      "Info    : 0.40 < quality < 0.50 :       150 elements\n",
      "Info    : 0.50 < quality < 0.60 :       273 elements\n",
      "Info    : 0.60 < quality < 0.70 :       913 elements\n",
      "Info    : 0.70 < quality < 0.80 :      1842 elements\n",
      "Info    : 0.80 < quality < 0.90 :      2090 elements\n",
      "Info    : 0.90 < quality < 1.00 :      1095 elements\n",
      "Info    : 152 edge swaps, 0 node relocations (volume = 0.01): worst = 0.300511 / average = 0.784453 (Wall 0.00193693s, CPU 0.00201s)\n",
      "Info    : No ill-shaped tets in the mesh :-)\n",
      "Info    : 0.00 < quality < 0.10 :         0 elements\n",
      "Info    : 0.10 < quality < 0.20 :         0 elements\n",
      "Info    : 0.20 < quality < 0.30 :         0 elements\n",
      "Info    : 0.30 < quality < 0.40 :        67 elements\n",
      "Info    : 0.40 < quality < 0.50 :       135 elements\n",
      "Info    : 0.50 < quality < 0.60 :       271 elements\n",
      "Info    : 0.60 < quality < 0.70 :       907 elements\n",
      "Info    : 0.70 < quality < 0.80 :      1872 elements\n",
      "Info    : 0.80 < quality < 0.90 :      2113 elements\n",
      "Info    : 0.90 < quality < 1.00 :      1081 elements\n",
      "Info    : Done optimizing mesh (Wall 0.00743052s, CPU 0.007641s)\n",
      "Info    : 1746 nodes 9265 elements\n"
     ]
    }
   ],
   "source": [
    "import gmsh\n",
    "\n",
    "gmsh.initialize()\n",
    "\n",
    "# meshsize settings\n",
    "meshsize = 0.02\n",
    "gmsh.option.setNumber(\"Mesh.MeshSizeMax\", meshsize)\n",
    "gmsh.option.setNumber(\"Mesh.MeshSizeMax\", meshsize)\n",
    "\n",
    "\n",
    "# create geometry\n",
    "L = 1\n",
    "W = 0.1\n",
    "\n",
    "gmsh.model.occ.addBox(0, 0, 0, L, W, W)\n",
    "gmsh.model.occ.synchronize()\n",
    "\n",
    "# setup physical groups\n",
    "v_bc_tag = 2\n",
    "Z_bc_tag = 3\n",
    "gmsh.model.addPhysicalGroup(3, [1], 1, \"air_volume\")\n",
    "gmsh.model.addPhysicalGroup(2, [1], v_bc_tag, \"velocity_BC\")\n",
    "gmsh.model.addPhysicalGroup(2, [2], Z_bc_tag, \"impedance\")\n",
    "\n",
    "# mesh generation\n",
    "gmsh.model.mesh.generate(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we import the gmsh mesh with the ```dolfinx.io.gmshio``` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpi4py import MPI\n",
    "from dolfinx import (\n",
    "    fem,\n",
    "    io,\n",
    "    default_scalar_type,\n",
    "    geometry,\n",
    "    __version__ as dolfinx_version,\n",
    ")\n",
    "from dolfinx.fem.petsc import LinearProblem\n",
    "import ufl\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "from packaging.version import Version\n",
    "\n",
    "mesh_data = io.gmshio.model_to_mesh(gmsh.model, MPI.COMM_WORLD, 0, gdim=3)\n",
    "if Version(dolfinx_version) > Version(\"0.9.0\"):\n",
    "    domain = mesh_data.mesh\n",
    "    assert mesh_data.facet_tags is not None\n",
    "    facet_tags = mesh_data.facet_tags\n",
    "else:\n",
    "    domain, _, facet_tags = mesh_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d8cd12",
   "metadata": {},
   "source": [
    "We define the function space for our unknown $p$ and define the range of frequencies we want to solve the Helmholtz equation for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06cebc38",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "V = fem.functionspace(domain, (\"Lagrange\", 1))\n",
    "\n",
    "# Discrete frequency range\n",
    "freq = np.arange(10, 1000, 5)  # Hz\n",
    "\n",
    "# Air parameters\n",
    "rho0 = 1.225  # kg/m^3\n",
    "c = 340  # m/s\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Boundary conditions\n",
    "\n",
    "The Delaney-Bazley model is used to compute the characteristic impedance and wavenumber of the porous layer,\n",
    "treated as an equivalent fluid with complex valued properties\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "Z_c(\\omega) &= \\rho_0 c_0 \\left[1 + 0.0571 X^{-0.754} - j 0.087 X^{-0.732}\\right],\\\\\n",
    "k_c(\\omega) &= \\frac{\\omega}{c_0} \\left[1 + 0.0978 X^{-0.700} - j 0.189 X^{-0.595}\\right],\\\\\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where $X = \\frac{\\rho_0 f}{\\sigma}$.\n",
    "\n",
    "With these, we can compute the surface impedance, that in the case of a rigid passive absorber placed on a rigid wall is given by the formula\n",
    "$$\n",
    "\\begin{align}\n",
    "Z_s = -j Z_c cot(k_c d).\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "Let's create a function to compute it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Impedance calculation\n",
    "def delany_bazley_layer(f, rho0, c, sigma):\n",
    "    X = rho0 * f / sigma\n",
    "    Zc = rho0 * c * (1 + 0.0571 * X**-0.754 - 1j * 0.087 * X**-0.732)\n",
    "    kc = 2 * np.pi * f / c * (1 + 0.0978 * (X**-0.700) - 1j * 0.189 * (X**-0.595))\n",
    "    Z_s = -1j * Zc * (1 / np.tan(kc * d))\n",
    "    return Z_s\n",
    "\n",
    "\n",
    "sigma = 1.5e4\n",
    "d = 0.01\n",
    "Z_s = delany_bazley_layer(freq, rho0, c, sigma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we are going to compute a sound pressure spectrum, all the variables that depend on frequency (that are $\\omega$, $k$ and $Z$) need to be updated in the frequency loop.\n",
    "To make this possible, we will initialize them as dolfinx constants.\n",
    "Then, we define the value for the normal velocity on the first end of the tube"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "omega = fem.Constant(domain, default_scalar_type(0))\n",
    "k = fem.Constant(domain, default_scalar_type(0))\n",
    "Z = fem.Constant(domain, default_scalar_type(0))\n",
    "v_n = 1e-5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to specify the integration measure $ds$, by using ```ufl```, and its built in integration measures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = ufl.Measure(\"ds\", domain=domain, subdomain_data=facet_tags)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variational Formulation\n",
    "We can now write the variational formulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = ufl.TrialFunction(V)\n",
    "v = ufl.TestFunction(V)\n",
    "\n",
    "a = (\n",
    "    ufl.inner(ufl.grad(p), ufl.grad(v)) * ufl.dx\n",
    "    + 1j * rho0 * omega / Z * ufl.inner(p, v) * ds(Z_bc_tag)\n",
    "    - k**2 * ufl.inner(p, v) * ufl.dx\n",
    ")\n",
    "L = -1j * omega * rho0 * ufl.inner(v_n, v) * ds(v_bc_tag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class ```LinearProblem``` is used to setup the PETSc backend and assemble the system vector and matrices.\n",
    "The solution will be stored in a `dolfinx.fem.Function`, ```p_a```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "lines_to_end_of_cell_marker": 2
   },
   "outputs": [],
   "source": [
    "p_a = fem.Function(V)\n",
    "p_a.name = \"pressure\"\n",
    "\n",
    "problem = LinearProblem(\n",
    "    a,\n",
    "    L,\n",
    "    u=p_a,\n",
    "    petsc_options={\n",
    "        \"ksp_type\": \"preonly\",\n",
    "        \"pc_type\": \"lu\",\n",
    "        \"pc_factor_mat_solver_type\": \"mumps\",\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "## Computing the pressure at a given location\n",
    "Before starting our frequency loop, we can build a function that, given a microphone position,\n",
    "computes the sound pressure at its location.\n",
    "We will use the a similar method as in [Deflection of a membrane](../chapter1/membrane_code).\n",
    "However, as the domain doesn't deform in time, we cache the collision detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MicrophonePressure:\n",
    "    def __init__(self, domain, microphone_position):\n",
    "        \"\"\"Initialize microphone(s).\n",
    "\n",
    "        Args:\n",
    "            domain: The domain to insert microphones on\n",
    "            microphone_position: Position of the microphone(s).\n",
    "                Assumed to be ordered as ``(mic0_x, mic1_x, ..., mic0_y, mic1_y, ..., mic0_z, mic1_z, ...)``\n",
    "\n",
    "        \"\"\"\n",
    "        self._domain = domain\n",
    "        self._position = np.asarray(\n",
    "            microphone_position, dtype=domain.geometry.x.dtype\n",
    "        ).reshape(3, -1)\n",
    "        self._local_cells, self._local_position = self.compute_local_microphones()\n",
    "\n",
    "    def compute_local_microphones(\n",
    "        self,\n",
    "    ) -> tuple[npt.NDArray[np.int32], npt.NDArray[np.floating]]:\n",
    "        \"\"\"\n",
    "        Compute the local microphone positions for a distributed mesh\n",
    "\n",
    "        Returns:\n",
    "            Two lists (local_cells, local_points) containing the local cell indices and the local points\n",
    "        \"\"\"\n",
    "        points = self._position.T\n",
    "        bb_tree = geometry.bb_tree(self._domain, self._domain.topology.dim)\n",
    "\n",
    "        cells = []\n",
    "        points_on_proc = []\n",
    "\n",
    "        cell_candidates = geometry.compute_collisions_points(bb_tree, points)\n",
    "        colliding_cells = geometry.compute_colliding_cells(\n",
    "            domain, cell_candidates, points\n",
    "        )\n",
    "\n",
    "        for i, point in enumerate(points):\n",
    "            if len(colliding_cells.links(i)) > 0:\n",
    "                points_on_proc.append(point)\n",
    "                cells.append(colliding_cells.links(i)[0])\n",
    "\n",
    "        return np.asarray(cells, dtype=np.int32), np.asarray(\n",
    "            points_on_proc, dtype=domain.geometry.x.dtype\n",
    "        )\n",
    "\n",
    "    def listen(\n",
    "        self, recompute_collisions: bool = False\n",
    "    ) -> npt.NDArray[np.complexfloating]:\n",
    "        if recompute_collisions:\n",
    "            self._local_cells, self._local_position = self.compute_local_microphones()\n",
    "        if len(self._local_cells) > 0:\n",
    "            return p_a.eval(self._local_position, self._local_cells)\n",
    "        else:\n",
    "            return np.zeros(0, dtype=default_scalar_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pressure spectrum is initialized as a numpy array and the microphone location is assigned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p_mic = np.zeros((len(freq), 1), dtype=complex)\n",
    "\n",
    "mic = np.array([0.5, 0.05, 0.05])\n",
    "microphone = MicrophonePressure(domain, mic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency loop\n",
    "\n",
    "Finally, we can write the frequency loop, where we update the values of the frequency-dependent variables and solve the system for each frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for nf in range(0, len(freq)):\n",
    "    k.value = 2 * np.pi * freq[nf] / c\n",
    "    omega.value = 2 * np.pi * freq[nf]\n",
    "    Z.value = Z_s[nf]\n",
    "\n",
    "    problem.solve()\n",
    "    p_a.x.scatter_forward()\n",
    "\n",
    "    p_f = microphone.listen()\n",
    "    p_f = domain.comm.gather(p_f, root=0)\n",
    "\n",
    "    if domain.comm.rank == 0:\n",
    "        assert p_f is not None\n",
    "        p_mic[nf] = np.hstack(p_f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPL spectrum\n",
    "After the computation, the pressure spectrum at the prescribed location is available.\n",
    "Such a spectrum is usually shown using the decibel (dB) scale to obtain the SPL, with the RMS pressure as input,\n",
    "defined as $p_{rms} = \\frac{p}{\\sqrt{2}}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if domain.comm.rank == 0:\n",
    "    import matplotlib.pyplot as plt\n",
    "\n",
    "    fig = plt.figure(figsize=(25, 8))\n",
    "    plt.plot(freq, 20 * np.log10(np.abs(p_mic) / np.sqrt(2) / 2e-5), linewidth=2)\n",
    "    plt.grid(True)\n",
    "    plt.xlabel(\"Frequency [Hz]\")\n",
    "    plt.ylabel(\"SPL [dB]\")\n",
    "    plt.xlim([freq[0], freq[-1]])\n",
    "    plt.ylim([0, 90])\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,py:light"
  },
  "kernelspec": {
   "display_name": "Python 3 (DOLFINx complex)",
   "language": "python",
   "name": "python3-complex"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}