{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "61c6f09c-d285-42a9-b3d3-9c166ab5271f",
   "metadata": {},
   "source": [
    "# Vortex line density in a He II turbulent flow at \"high\" temperature\n",
    "In this session we are going to look at the vorticity in a He II grid flow. More specifically, we are going to try to understand the data obtained in an oscillating grid experiment (see the article [here](https://hal.science/hal-05235538v1/file/OGRES_eff_visc.pdf)) where two kinds of measurements were carried out:\n",
    "* velocity measurement, through the use of particle tracking velocimetry,\n",
    "* second sound attenuation.\n",
    "\n",
    "The data are available for this class only, courtesy of C. Bret, P. Diribarne, J. Duplat and B. Rousset. They will eventually be made public with restrictions on their use, so **please do not use/distribute those data outside this class**.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bf0028f-c1e4-4b72-9c77-26057d43ad7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = '/homelocal/pd218333/Documents/Clement_Data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51955606-8fc6-430c-b360-257bfd6d983b",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcf8d3be-6d68-4214-83ca-99065ed6382a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import join\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "typ = \"ampvar\"\n",
    "freq = 9 #Hz\n",
    "pressure = 30 #mbar\n",
    "data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "## Vortex line density acquired at 8 000 Hz\n",
    "fps = 8e3\n",
    "vld = data['L_vld_sig'][::,0].flatten()\n",
    "tt = np.arange(vld.size) / fps\n",
    "\n",
    "## Grid stroke\n",
    "strokes = data['Smot'].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c3a67350-f189-46c4-9f43-a2906d51543b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ad9fae175c0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Show subsampled signal for each grid stroke\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "_,axs = plt.subplots(1,2,3)\n",
    "\n",
    "npts_per_stroke = int (vld.size / strokes.size)\n",
    "npts_per_plot = 1000\n",
    "stride = int(np.ceil (npts_per_stroke/npts_per_plot))\n",
    "for ii,s in enumerate(strokes):\n",
    "    t = tt[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride]\n",
    "    v = vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride]\n",
    "    axs[0].semilogy (t, v, label=f\"{s}mm\")\n",
    "\n",
    "    pdf,bin_edges = np.histogram(v, bins=30, density=True)\n",
    "    bin_centers = bin_edges[1::]-np.diff(bin_edges)\n",
    "    axs[1].semilogy(bin_centers, pdf, label=f\"{s}mm\")\n",
    "axs[0].legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5291baa-3d3b-49c3-8d59-5b9f96972f4f",
   "metadata": {},
   "source": [
    "## Plot all raw data\n",
    "### Fixed frequency, variable amplitude"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9d6a6d6f-6a99-4df5-b0a0-7e83c96080e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mathcal{L}(m/m^3)$')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax = plt.subplots()\n",
    "typ = \"ampvar\"\n",
    "marks = ['s', 'o', 'D', 'v', '^', '>', '<']\n",
    "pressure = 30\n",
    "for freq in [3, 6, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "\n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    ax.loglog (strokes, vmean, label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "    \n",
    "pressure = 7.5\n",
    "for freq in [3, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure:g}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "\n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    ax.loglog (strokes, vmean, label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_xlabel('f(Hz)')\n",
    "ax.set_ylabel(r'$\\mathcal{L}(m/m^3)$')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dde70570-ff65-4adc-b90a-ccf086baf625",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Fixed amplitude, variable frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "529812a7-36b0-436b-a336-d7043596cb3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$\\\\mathcal{L}(m/m^3)$')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax = plt.subplots()\n",
    "typ = \"freqvar\"\n",
    "pressure = 30\n",
    "for amp in [8, 11] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{amp:d}mm.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## Grid stroke\n",
    "    freqs = data['fmot'].flatten()\n",
    "\n",
    "    npts_per_stroke = int (vld.size / freqs.size)\n",
    "    vmean = np.zeros(freqs.size)\n",
    "    for ii,s in enumerate(freqs):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    ax.loglog (freqs, vmean, label=f\"{amp} mm\", linestyle='', marker='+')\n",
    "ax.legend()\n",
    "ax.grid()\n",
    "ax.set_xlabel('S (mm)')\n",
    "ax.set_ylabel(r'$\\mathcal{L}(m/m^3)$')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f661ebbe-dabe-4eaa-8103-2a97e350a728",
   "metadata": {},
   "source": [
    "## Combine all as a function of the Reynolds number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fc114efa-aa00-4e71-9ed4-a53fd4af7b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax = plt.subplots()\n",
    "typ = \"ampvar\"\n",
    "marks = ['s', 'o', 'D', 'v', '^', '>', '<']\n",
    "pressure = 30\n",
    "for freq in [3, 6, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    \n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    Re = vrms*L_int/1e-8\n",
    "    ax.loglog (Re[Re>666], vmean[Re>666], label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "    \n",
    "pressure = 7.5\n",
    "for freq in [3, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure:g}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    \n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    Re = vrms*L_int/1e-8\n",
    "    ax.loglog (Re[Re>666], vmean[Re>666], label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "typ = \"freqvar\"\n",
    "pressure = 30\n",
    "for amp in [8, 11] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{amp:d}mm.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    npts_per_stroke = int (vld.size / freqs.size)\n",
    "    vmean = np.zeros(freqs.size)\n",
    "    for ii,s in enumerate(freqs):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "        \n",
    "    Re = vrms*L_int/1e-8\n",
    "    ax.loglog (Re[Re>666], vmean[Re>666], label=f\"{amp} mm\", linestyle='', marker='+')\n",
    "ax.set_xlabel(\"Re\")\n",
    "ax.set_ylabel(r'$\\mathcal{L} (m/m^3)$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afb5fba5-2637-4074-a564-3f424ba27527",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Intervortex distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "946bdfcc-c10e-4181-bda9-70d1efa089c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ada0772f5c0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,ax = plt.subplots()\n",
    "typ = \"ampvar\"\n",
    "marks = ['s', 'o', 'D', 'v', '^', '>', '<']\n",
    "pressure = 30\n",
    "for freq in [3, 6, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    \n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    Re = vrms*L_int/1e-7\n",
    "    ax.loglog (Re[Re>466], vmean[Re>466]**-0.5/L_int, label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "    \n",
    "pressure = 7.5\n",
    "for freq in [3, 9] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure:g}mbar_{typ}_{freq:d}Hz.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    \n",
    "    npts_per_stroke = int (vld.size / strokes.size)\n",
    "    vmean = np.zeros(strokes.size)\n",
    "    for ii,s in enumerate(strokes):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "    Re = vrms*L_int/1e-7\n",
    "    ax.loglog (Re[Re>466], vmean[Re>466]**-0.5/L_int, label=f\"{freq}Hz ({pressure} mbar)\", linestyle='', marker='+')\n",
    "typ = \"freqvar\"\n",
    "pressure = 30\n",
    "for amp in [8, 11] :\n",
    "    data = loadmat (join (base_path, f\"sec_sound_{pressure}mbar_{typ}_{amp:d}mm.mat\"))\n",
    "\n",
    "    vld = data['L_vld_sig'][::,0].flatten()\n",
    "    tt = np.arange(vld.size) / fps\n",
    "    \n",
    "    ## RMS velocity\n",
    "    vrms = data['Urms'].flatten()\n",
    "    \n",
    "    ## Integral length scale\n",
    "    L_int = data['L_int'].flatten()\n",
    "\n",
    "    ## Grid stroke\n",
    "    strokes = data['Smot'].flatten()\n",
    "    npts_per_stroke = int (vld.size / freqs.size)\n",
    "    vmean = np.zeros(freqs.size)\n",
    "    for ii,s in enumerate(freqs):\n",
    "        vmean[ii] = np.mean(vld[(ii*npts_per_stroke):((ii+1)*npts_per_stroke):stride])\n",
    "        \n",
    "    Re = vrms*L_int/1e-7\n",
    "    ax.loglog (Re[Re>466], vmean[Re>466]**-0.5/L_int, label=f\"{amp} mm\", linestyle='', marker='+')\n",
    "ax.plot ([400, 4000], 0.5*np.array([400, 4000])**(-3/4), linewidth=2, color='k', label=r'$\\delta\\propto Re^{-3/4}$')\n",
    "ax.set_xlabel(\"Re\")\n",
    "ax.set_ylabel(r'$\\delta/L$')\n",
    "ax.grid(True)\n",
    "ax.legend()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "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
}