{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bdd885c3-8785-4258-9c65-bb7b90f3abe6",
   "metadata": {},
   "source": [
    "# MTF053 - Computer Assignment 2 (CA2) - Numerical Simulation of Boundary Layer Flows,\n",
    "\n",
    "In this assignment you will use a commercial Computational Fluid Dynamics (CFD) software called `Star-CCM+`. Two different simulations will be done. The first involves flow over a flat plate and you will extract data from the CFD simulation to compare with the analytical/empirical formulations for laminar and turbulent boundary layers. In the second simulation you will simulate the flow over a cylinder and compare the simulated flow field with data from an experiment (provided data).\n",
    "\n",
    "![Turbulence](https://imgs.xkcd.com/comics/cloud_swirls.png)\n",
    "\n",
    "The assignemnt is divided into tasks some of which includes psotprocessing of data. For these tasks Python codes are provided that you can run directly in this `Jupyter NoteBook`. For some of the tasks, you are expected to write down answers to questions. This indicated as follows in the notebook: <font color=red>**write your answer here**</font>.\n",
    "\n",
    "More detailed instructions, theory and background are given in the assignment instruction document [MTF053_CA2.pdf](https://courses.g3dflow.com/fluidmech/docs/MTF053_CA2.pdf).\n",
    "\n",
    "## Jupyter notebook instructions\n",
    "\n",
    "### Edit a notebook cell\n",
    "\n",
    "Double click on the cell to enter edit mode.\n",
    "   \n",
    "### Update a cell\n",
    "\n",
    "With the cell marked, klick on the play symbol in at the top of the notebook\n",
    "\n",
    "### Add a new cell\n",
    "\n",
    "Click on the cell above or below which you want a new cell and click on one of the symbols at the top right corner of the marked cell. Set cell type (Markdown or Code) for the new cell by clicking the cell and select *Code* or *Markdown* in the dropdown list in the top menu of the notebook. You'll find a good documentation of Markdown [here](https://www.markdownguide.org/basic-syntax/).\n",
    "\n",
    "### Add an image in a Markdown cell\n",
    "\n",
    "If you like to include an image in one of the Markdown text cells (for example if you want to include a photo of a derivation made on paper), do as follows:\n",
    "\n",
    "1. upload your image using the *upload files* function\n",
    "\n",
    "2. include the image in the notebook by writing: \\!\\[title\\]\\(image.png\\)\n",
    "\n",
    "3. typeset the cell by klicking the play icon at the top of the notebook to see the result. You can also include an image available online, add Markdown code as follows: \\!\\[title\\]\\(url\\)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62c9bfae-e4e0-487f-ade0-a5b339f46741",
   "metadata": {},
   "outputs": [],
   "source": [
    "# INITIALIZATION \n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# NO NEED FOR MODIFICATIONS IN THIS NOTEBOOK SECTION\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# IMPORT THE PYTHON LIBRARIES NEEDED\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# DEFINITION OF GLOBAL VARIABLES\n",
    "\n",
    "FontSize  = 20\n",
    "LineWidth = 2\n",
    "\n",
    "# fluid properties\n",
    "\n",
    "mu    =  1.8e-5 # fluid viscosity (dynamic viscosity)\n",
    "rho   =  1.2    # fluid density\n",
    "nu    = mu/rho\n",
    "\n",
    "# DEFINITION OF HELP FUNCTIONS\n",
    "\n",
    "def read_profile_from_csv_file(name):\n",
    "    U = []\n",
    "    y = []\n",
    "    with open(name, \"r\") as file:\n",
    "        file.readline() # read data header line\n",
    "        for line in file.readlines(): # read all lines and extract y and U\n",
    "            f_list = [float(i) for i in line.split(\",\") if i.strip ]\n",
    "            U.append( f_list[0] )\n",
    "            y.append( f_list[1] )\n",
    "    return np.array(y),np.array(U)\n",
    "\n",
    "def read_cylinder_pressure_from_cvs_file(name):\n",
    "    theta    = []\n",
    "    pressure = []\n",
    "    with open(name, \"r\") as file:\n",
    "        file.readline() # read data header line\n",
    "        for line in file.readlines(): # read all lines and extract theta and mean pressure\n",
    "            f_list = [float(i) for i in line.split(\",\") if i.strip ]\n",
    "            if f_list[0] <= 180.:\n",
    "                theta.append(    f_list[0] )\n",
    "                pressure.append( f_list[1] ) \n",
    "    swaps=1\n",
    "    while(swaps):\n",
    "        swaps=0\n",
    "        for i in range(len(theta)-1):\n",
    "            if theta[i] > theta[i+1] :\n",
    "                t1=theta[i]\n",
    "                p1=pressure[i]\n",
    "                theta[i]=theta[i+1]\n",
    "                pressure[i]=pressure[i+1]\n",
    "                theta[i+1]=t1\n",
    "                pressure[i+1]=p1\n",
    "                swaps=swaps+1\n",
    "    return np.array(theta),np.array(pressure)    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bacf438-d3bb-4f41-9862-d68a0279189d",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 1. Flat-Plate Boundary Layer Analysis\n",
    "## Laminar flow\n",
    "---\n",
    "## Task 1.1\n",
    "---\n",
    "\n",
    "Determine the displacement thickness $\\delta^\\ast$, momentum thickness $\\theta$, and the Reynolds-number ratio $Re_{\\delta^\\ast}/\\sqrt{Re_x}$ for the three measured laminar boundary layer profiles provided in the `Python` script below. Compare your result to what can be expected from theory. Are the calculated values inline with theory?\n",
    "\n",
    "**Hints!** \n",
    "\n",
    "1. In `Python` you can do the numerical integration using the function `numpy.trapz` in the `numpy` library. When estimated the integral, make sure to only use measurement points that are within the boundary layer using the following relation\n",
    "\n",
    "$$u(\\delta)\\approx 0.99 U_o$$\n",
    "\n",
    "2. The following relation derived from the Blasius solution for laminar boundary-layer flows can be of use\n",
    "\n",
    "$$\\dfrac{\\delta^\\ast}{x}=\\dfrac{1.721}{\\sqrt{Re_x}}$$\n",
    "\n",
    "\n",
    "<font color=red>**write your answer here**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "878ec971-778d-4f34-b828-8788f345f1a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK 1.1 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. CALCULATE DISPLACEMENT THICKNESS, MOMENTUM THICKNESS, \n",
    "#    AND THE REYNOLDS NUMBER RATIO Re_delta/sqrt(Re_x)\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# provided measured laminar boundary layer data\n",
    "\n",
    "y35_exp = np.array([0.0, 0.000231, 0.000481, 0.0007059999999999999, 0.0009310000000000001, 0.001181, 0.001406, 0.001656, 0.001881, 0.0021309999999999996, 0.002356, 0.002581, 0.0028309999999999997, 0.003056, 0.003306, 0.0035310000000000003, 0.003756, 0.004006, 0.0042309999999999995, 0.004481, 0.004706, 0.005181, 0.005656, 0.006131, 0.006581])\n",
    "U35_exp = np.array([0.0, 0.56, 1.139, 1.669, 2.167, 2.678, 3.078, 3.509, 3.886, 4.222, 4.505, 4.771, 5.032, 5.218, 5.396, 5.514, 5.618, 5.716, 5.762, 5.772, 5.808, 5.845, 5.845, 5.855, 5.869])\n",
    "\n",
    "y60_exp = np.array([0.0, 0.0003, 0.000625, 0.000925, 0.0012250000000000002, 0.00155, 0.00185, 0.00215, 0.002475, 0.002775, 0.003075, 0.0034, 0.0037, 0.004, 0.004325, 0.004625, 0.004925, 0.00525, 0.00555, 0.005849999999999999, 0.006175, 0.006775000000000001, 0.0074, 0.008025000000000001, 0.008625])\n",
    "U60_exp = np.array([0.0, 0.737, 1.344, 1.891, 2.367, 2.859, 3.291, 3.662, 4.049, 4.376, 4.661, 4.938, 5.149, 5.331, 5.473, 5.574, 5.672, 5.726, 5.771, 5.83, 5.849, 5.855, 5.88, 5.873, 5.854])\n",
    "\n",
    "y90_exp = np.array([0.0, 0.0004, 0.00075, 0.001125, 0.0015, 0.001875, 0.002275, 0.00265, 0.003025, 0.0034, 0.0037749999999999997, 0.00415, 0.004525, 0.004900000000000001, 0.005275, 0.0056500000000000005, 0.00605, 0.006425, 0.0068, 0.007175, 0.007549999999999999, 0.0083, 0.00905, 0.009824999999999999, 0.010575])\n",
    "U90_exp = np.array([0.0, 0.684, 1.253, 1.83, 2.335, 2.789, 3.259, 3.677, 4.066, 4.388, 4.692, 4.952, 5.175, 5.336, 5.493, 5.602, 5.719, 5.769, 5.824, 5.831, 5.858, 5.898, 5.891, 5.914, 5.923])\n",
    "\n",
    "delta_star_35=theta_35=Re_ratio_35=0.\n",
    "delta_star_60=theta_60=Re_ratio_60=0.\n",
    "delta_star_90=theta_90=Re_ratio_90=0.\n",
    "\n",
    "# <= CALCULATE DISPLACEMENT THICKNESS, MOMENTUM THICKNESS AND Re_delta/sqrt(Re_x) \n",
    "# <= FOR X=350mm, X=600mm, AND X=900mm HERE\n",
    "\n",
    "if delta_star_35:\n",
    "    print('=' * 40)\n",
    "    print('displacement thickness:')\n",
    "    print('=' * 40)\n",
    "    print(' @x=350mm: %e m' %delta_star_35 )\n",
    "    print(' @x=600mm: %e m' %delta_star_60 )\n",
    "    print(' @x=900mm: %e m' %delta_star_90 )\n",
    "    print()\n",
    "if theta_35:\n",
    "    print('=' * 40)\n",
    "    print('momentum thickness:')\n",
    "    print('=' * 40)\n",
    "    print(' @x=350mm: %e m' %theta_35 )\n",
    "    print(' @x=600mm: %e m' %theta_60 )\n",
    "    print(' @x=900mm: %e m' %theta_90 )\n",
    "    print()\n",
    "if Re_ratio_35:\n",
    "    print('=' * 40)\n",
    "    print('Re_delta over sqrt(Re_x):')\n",
    "    print('=' * 40)\n",
    "    print(' @x=350mm: %e' %Re_ratio_35 )\n",
    "    print(' @x=600mm: %e' %Re_ratio_60 )\n",
    "    print(' @x=900mm: %e' %Re_ratio_90 )\n",
    "    print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae84d9c8-1ba3-43a7-a595-2bd2a0cba523",
   "metadata": {},
   "source": [
    "---\n",
    "## Task 1.2\n",
    "---\n",
    "\n",
    "The solution to the laminar flat plate velocity field was given by Blasius in 1908. The blasius profile is provided in the `Python` script below. Please note that Blasius solution is not given as $U$ and $y$, but instead in the variables $f^\\prime$ and $\\eta$, defined as\n",
    "\n",
    "$$f^\\prime(\\eta)=\\dfrac{\\overline{u}}{U_o}$$\n",
    "\n",
    "$$\\eta=y\\sqrt{\\dfrac{U_o}{x\\nu}}$$\n",
    "\n",
    "Verify that the velocity profiles obtained from the wind tunnel experiment (U35_exp, U60_exp, and U90_exp provided in the code above) are self-similar by transforming the laminar measurement data from the three locations to Blasius variables. Plot them (in one figure) together with the Blasius solution.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e36dd394-8685-471e-9253-02fda9c7f8aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK 1.2 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. GENERATE NON-DIMENSIONAL VELOCITY PROFILES FROM\n",
    "#    THE PROVIDED MEASURED BOUNDARY-LAYER DATA\n",
    "#    (X=350mm, X=600mm, AND X=900mm)\n",
    "#\n",
    "# 2. PLOT THE GENERATED PROFILES\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# provided Blasius profile:\n",
    "\n",
    "blasius_eta    = np.array([0.0, 0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0])\n",
    "blasius_fprime = np.array([0.0, 0.06641, 0.13277, 0.19894, 0.26471, 0.32979, 0.39378, 0.45627, 0.51676, 0.57477, 0.62977, 0.68132, 0.72899, 0.77246, 0.81152, 0.84605, 0.87609, 0.90177, 0.92333, 0.94112, 0.95552, 0.96696, 0.97587, 0.98269, 0.98779, 0.99155])\n",
    "    \n",
    "# <= GENERATE NON-DIMENSIONAL PROFILES FROM THE LAMINAR BOUNDARY \n",
    "# <= LAYER DATA PROVIDED ABOVE (X=350mm, X=600mm, AND X=900mm) HERE  \n",
    "\n",
    "# plot non-dimensional velocity profiles and compare to the Blasius profile\n",
    "\n",
    "fig = plt.figure(num=1, figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax  = fig.add_subplot(111)\n",
    "ax.plot(blasius_fprime,blasius_eta,linewidth=LineWidth,label='Blasius profile')\n",
    "\n",
    "# <= PLOT EXPERIMENTAL DATA HERE\n",
    "\n",
    "ax.set_title('Blasius profile (laminar boundary layer)',fontsize=FontSize)\n",
    "ax.set_xlabel(r'$\\dfrac{\\overline{u}}{U_o}$',fontsize=FontSize)\n",
    "ax.set_ylabel(r'$\\eta=y\\sqrt{\\dfrac{U_o}{\\nu x}}$',fontsize=FontSize)\n",
    "ax.legend(fontsize=FontSize)\n",
    "ax.set_ylim(0.0,7.0)\n",
    "ax.set_xlim(0.0,1.1)\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(FontSize)\n",
    "ax.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d29224ec-e61d-4c2c-8a53-df917adf61f1",
   "metadata": {},
   "source": [
    "## Turbulent flow\n",
    "\n",
    "---\n",
    "## Task 1.3\n",
    "---\n",
    "\n",
    "For the measured turbulent boundary-layer data provided in the `Python` code below, the friction velocity $u^\\ast$ can be estimated to be 0.4171\n",
    "\n",
    "1. Plot the mean velocity profile as $u^+ = f (y^+)$\n",
    "\n",
    "2. Plot the rms-profile as the turbulence intensity $u^\\prime/u^\\ast = f (y^+)$\n",
    "\n",
    "3. Identify the different regions in the turbulent boundary layer (define the regions by specifying approximately which ranges of $y^+$ it corresponds to)\n",
    "\n",
    "    <font color=red>**write your answer here**</font>\n",
    "\n",
    "4. In which region is the maximum turbulent intensity reached and why?\n",
    "\n",
    "    <font color=red>**write your answer here**</font>\n",
    "\n",
    "\n",
    "**Note!** you should use log-scale for the $y^+$ axis and a linear scale for the $u^+$ and $u^\\prime/u^\\ast$ axis. Use `semilogx` with $y^+$ on the x-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a7a57b1-302b-4083-9317-1deca86b5fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TASK 1.3 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. GENERATE NON-DIMENSIONAL PROFILES\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# provided measured turbulent boundary layer data\n",
    "\n",
    "y_turb_exp    = np.array([0.00015, 0.000231, 0.0003, 0.000481, 0.0006, 0.000706, 0.000931, 0.001181, 0.001406, 0.001656, 0.001881, 0.002131, 0.002356, 0.002581, 0.002831, 0.003056, 0.003306, 0.003775, 0.00415, 0.004525, 0.0049, 0.005275, 0.00605, 0.0068, 0.0072, 0.0076, 0.0083, 0.0095, 0.01057, 0.012, 0.014, 0.016, 0.019, 0.022, 0.025, 0.03])\n",
    "U_turb_exp    = np.array([3.051, 3.531, 3.917, 4.579, 4.879, 5.086, 5.385, 5.637, 5.805, 5.928, 6.05, 6.139, 6.225, 6.32, 6.424, 6.463, 6.531, 6.657, 6.753, 6.828, 6.905, 6.985, 7.148, 7.265, 7.357, 7.417, 7.52, 7.693, 7.829, 7.984, 8.233, 8.449, 8.683, 8.897, 9.034, 9.121])\n",
    "Urms_turb_exp = np.array([0.771, 0.826, 0.858, 0.874, 0.855, 0.836, 0.826, 0.772, 0.759, 0.73, 0.719, 0.704, 0.699, 0.696, 0.684, 0.675, 0.679, 0.663, 0.662, 0.654, 0.648, 0.632, 0.626, 0.616, 0.603, 0.599, 0.586, 0.564, 0.547, 0.522, 0.484, 0.44, 0.378, 0.308, 0.222, 0.115])\n",
    "\n",
    "# allocate vectors\n",
    "\n",
    "yplus_exp=np.zeros(len(y_turb_exp))\n",
    "uplus_exp=np.zeros(len(y_turb_exp))\n",
    "urmsp_exp=np.zeros(len(y_turb_exp))\n",
    "\n",
    "# <= GENERATE NON-DIMENSIONAL PROFILES HERE\n",
    "\n",
    "if( np.max(yplus_exp) != 0 and np.max(uplus_exp) != 0 and np.max(urmsp_exp) != 0 ):\n",
    "\n",
    "    fig = plt.figure(num=1, figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "    (ax1, ax2) = fig.subplots(nrows=2, sharex=True)\n",
    "\n",
    "    ax1.semilogx(yplus_exp,uplus_exp,'o',linewidth=LineWidth,label='measured')\n",
    "\n",
    "    ax1.semilogx(np.linspace(1,20,50),np.linspace(1,20,50),linestyle='dashed',linewidth=LineWidth,label=r'$u^+=y^+$')\n",
    "    ax1.semilogx([5,1000],[1./0.41*np.log(5)+5.0, 1./0.41*np.log(1000)+5.0],linestyle='dashed',linewidth=LineWidth,label=r'$u^+=\\dfrac{1}{\\kappa}\\ln(y^+)+B$')\n",
    "    ax1.set_title('Turbulent boundary layer data',fontsize=FontSize)\n",
    "    ax1.set_ylabel(r'$u^+$',fontsize=FontSize)\n",
    "    ax1.legend(fontsize=FontSize)\n",
    "    ax1.set_xlim(1,1000)\n",
    "    for label in (ax1.get_xticklabels() + ax1.get_yticklabels()):\n",
    "        label.set_fontsize(FontSize)\n",
    "    ax1.grid(which='both')\n",
    "\n",
    "    ax2.semilogx(yplus_exp,urmsp_exp,'o',linewidth=LineWidth)\n",
    "\n",
    "    ax2.set_xlabel(r'$y^+$',fontsize=FontSize)\n",
    "    ax2.set_ylabel(r'$u_{rms}/u^\\ast$',fontsize=FontSize)\n",
    "    for label in (ax2.get_xticklabels() + ax2.get_yticklabels()):\n",
    "        label.set_fontsize(FontSize)\n",
    "    ax2.grid(which='both')\n",
    "    \n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f17029db-80bf-4125-bc55-e761ae537cee",
   "metadata": {},
   "source": [
    "## Comparison between simulations and experiments\n",
    "---\n",
    "## Task 1.4\n",
    "---\n",
    "\n",
    "\n",
    "1. Plot the three different velocity profiles obtained from the laminar boundary layer simulation (0.35m, 0.60m, 0.90m) compared to the corresponding measured data provided above (task 1.1)\n",
    "\n",
    "    Upload the csv-files extracted from `Star-CCM+` in `Jupyter Lab` first\n",
    "\n",
    "2. Comment on, and try to explain, any significant differences in the profiles extracted from the CFD simulation and the corresponding profiles obtained from measured data. \n",
    "\n",
    "    <font color=red>**write your answer here**</font>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7b1db5a6-49bb-403c-8cdd-354ef43ba00e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK 1.4 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. READ BOUNDARY LAYER DATA FROM FILE \n",
    "#    (X=350mm, X=600mm, AND X=900mm)\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# load laminar boundary-layer data extracted from CFD\n",
    "\n",
    "y35_CFD = []\n",
    "U35_CFD = []\n",
    "y60_CFD = []\n",
    "U60_CFD = []\n",
    "y90_CFD = []\n",
    "U90_CFD = []\n",
    "\n",
    "# <= READ DATA FROM FILE HERE \n",
    "\n",
    "#y35_CFD,U35_CFD = read_profile_from_csv_file(\"filename.csv\")\n",
    "#y60_CFD,U60_CFD = read_profile_from_csv_file(\"filename.csv\")\n",
    "#y90_CFD,U90_CFD = read_profile_from_csv_file(\"filename.csv\")\n",
    "\n",
    "# make a plot comparing the data extraced from CFD and the corresponding measured data\n",
    "\n",
    "fig = plt.figure(num=1, figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax  = fig.add_subplot(111)\n",
    "\n",
    "line35, = ax.plot(U35_exp,y35_exp,linewidth=LineWidth,linestyle='dashed',label=r'$x=0.35\\ m$ (exp)')\n",
    "line60, = ax.plot(U60_exp,y60_exp,linewidth=LineWidth,linestyle='dashed',label=r'$x=0.60\\ m$ (exp)')\n",
    "line90, = ax.plot(U90_exp,y90_exp,linewidth=LineWidth,linestyle='dashed',label=r'$x=0.90\\ m$ (exp)')\n",
    "\n",
    "if len(y35_CFD):\n",
    "    ax.plot(U35_CFD,y35_CFD,linewidth=LineWidth,color = line35.get_color(),label=r'$x=0.35\\ m$ (CFD)')\n",
    "if len(y60_CFD):\n",
    "    ax.plot(U60_CFD,y60_CFD,linewidth=LineWidth,color = line60.get_color(),label=r'$x=0.60\\ m$ (CFD)')\n",
    "if len(y90_CFD):\n",
    "    ax.plot(U90_CFD,y90_CFD,linewidth=LineWidth,color = line90.get_color(),label=r'$x=0.90\\ m$ (CFD)')\n",
    "\n",
    "ax.set_title('Laminar boundary layer',fontsize=FontSize)\n",
    "ax.set_xlabel(r'$U\\ [m/s]$',fontsize=FontSize)\n",
    "ax.set_ylabel(r'$y\\ [m]$',fontsize=FontSize)\n",
    "ax.legend(fontsize=FontSize)\n",
    "ax.set_ylim(0.0,0.012)\n",
    "ax.set_xlim(0.0,6.0)\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(FontSize)\n",
    "ax.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4156e68b-3267-48a9-98b7-c4d7328ea20d",
   "metadata": {},
   "source": [
    "---\n",
    "## Task 1.5\n",
    "---\n",
    "\n",
    "1. Plot the turbulent data from the CFD simulation compared to the corresponding data provided above (task 1.3)\n",
    "\n",
    "2. Comment on the predicted turbulent flat-plate boundary layer velocity profile in relation to the corresponding measured data \n",
    "    \n",
    "    1. Does the result look like you expected? \n",
    "    \n",
    "        <font color=red>**write your answers here**</font>\n",
    "    \n",
    "    2. Do you think that the results could be improved and in that case how? \n",
    "    \n",
    "        <font color=red>**write your answers here**</font>\n",
    "    \n",
    "    3. Can you think of any significant sources of error? \n",
    "    \n",
    "        <font color=red>**write your answers here**</font>\n",
    "\n",
    "\n",
    "**Note!** it might be good to know that the measured data were obtained using hot-wire anemometry, a technique where a thin tungsten wire is heated by an electric current and the cooling effect introduced by the flow is measured, which can be converted to fluid velocity. When a flat-plate boundary layer is measured, the hot-wire probe (see illustration below) is traversed vertically through the boundary layer to measure the velocity at different wall-normal coordinates.\n",
    "\n",
    "![hotwire](https://fluidmech.g3dflow.com/Assignments/CA2/Images/hotwire.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c2d9a760-0673-42d2-b61f-bd0cc31266e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK 1.5 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. READ DATA EXPORTED FROM STAR-CCM+\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "# read turbulent boundary layer data extracted from CFD\n",
    "\n",
    "y_turb_CFD = []\n",
    "U_turb_CFD = []\n",
    "\n",
    "# <= READ CFD DATA HERE\n",
    "# y_turb_CFD,U_turb_CFD = read_profile_from_csv_file(\"filename.csv\")\n",
    "\n",
    "# make a plot comparing the data extraced from CFD and the corresponding measured data\n",
    "\n",
    "fig = plt.figure(num=1, figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax  = fig.add_subplot(111)\n",
    "\n",
    "ax.semilogx(y_turb_exp,U_turb_exp,'-o',linewidth=LineWidth,label='measured')\n",
    "if ( len(y_turb_CFD) and len(U_turb_CFD) ):\n",
    "    ax.semilogx(y_turb_CFD,U_turb_CFD,'-o',linewidth=LineWidth,label='CFD')\n",
    "\n",
    "ax.set_title('Turbulent boundary layer',fontsize=FontSize)\n",
    "ax.set_xlabel(r'$y\\ [m/s]$',fontsize=FontSize)\n",
    "ax.set_ylabel(r'$\\overline{u}\\ [m]$',fontsize=FontSize)\n",
    "ax.legend(fontsize=FontSize)\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(FontSize)\n",
    "ax.grid(which='both')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1107992a-a520-4afb-8074-fc82af47cee6",
   "metadata": {},
   "source": [
    "# 2. Flow around a cylinder\n",
    "---\n",
    "## Task 2.1\n",
    "---\n",
    "Plot the `Mean Static Pressure` profile along the cylinder surface using data extracted from the simulation and the provided measured pressure distribution over a cylinder in the same figure (if you have done the hands-on lab - *flow around immersed bodies* - you can use the measured data from the lab as well). \n",
    "\n",
    "Do the numerical results match qualitatively and/or quantitatively with the measured data? E.g., is the separation point the same? \n",
    "\n",
    "<font color=red>**write your answer here**</font>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16a079d1-bfd6-409f-ac3f-739f531558e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TASK 2.1 MAIN CODE\n",
    "\n",
    "#********************************************************\n",
    "#\n",
    "# CODE MODIFICATIONS NEEDED:\n",
    "#\n",
    "# 1. READ DATA EXPORTED FROM STAR-CCM+\n",
    "#\n",
    "#********************************************************\n",
    "\n",
    "cylinder_degrees_exp  = np.array([0,10,20,30,40,50,60,70,80,90,100,120,140,160,180])\n",
    "cylinder_pressure_exp = np.array([56.898,52.974,39.24,19.62,-1.962,-23.544,-37.278,-42.183,-34.335,-30.411,-30.411,-32.373,-30.411,-30.411,-30.411])\n",
    "\n",
    "cylinder_degrees_CFD  = []\n",
    "cylinder_pressure_CFD = []\n",
    "\n",
    "# <= READ CFD DATA HERE\n",
    "# cylinder_degrees_CFD, cylinder_pressure_CFD = read_cylinder_pressure_from_cvs_file(\"filename.csv\")\n",
    "\n",
    "# make a plot comparing the data extraced from CFD and the corresponding measured data\n",
    "\n",
    "fig = plt.figure(num=1, figsize=(15, 10), dpi=80, facecolor='w', edgecolor='k')\n",
    "ax  = fig.add_subplot(121)\n",
    "\n",
    "ax.plot(cylinder_degrees_exp,cylinder_pressure_exp,'-o',linewidth=LineWidth,label='measured')\n",
    "\n",
    "if ( len(cylinder_degrees_CFD) and len(cylinder_pressure_CFD) ):\n",
    "    ax.plot(cylinder_degrees_CFD,cylinder_pressure_CFD,'-o',linewidth=LineWidth,label='CFD')\n",
    "\n",
    "ax.set_title('Cylinder surface pressure',fontsize=FontSize)\n",
    "ax.set_xlabel('degrees',fontsize=FontSize)\n",
    "ax.set_ylabel(r'$\\Delta p\\ [Pa]$',fontsize=FontSize)\n",
    "ax.legend(fontsize=FontSize)\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(FontSize)\n",
    "ax.grid()\n",
    "\n",
    "ax  = fig.add_subplot(122)\n",
    "\n",
    "ax.plot(cylinder_degrees_exp,cylinder_pressure_exp/np.max(cylinder_pressure_exp),'-o',linewidth=LineWidth,label='measured')\n",
    "\n",
    "if len(cylinder_degrees_CFD):\n",
    "    ax.plot(cylinder_degrees_CFD,cylinder_pressure_CFD/np.max(cylinder_pressure_CFD),'-o',linewidth=LineWidth,label='CFD')\n",
    "\n",
    "ax.set_title('Normalized cylinder surface pressure',fontsize=FontSize)\n",
    "ax.set_xlabel('degrees',fontsize=FontSize)\n",
    "ax.set_ylabel(r'$\\Delta p/\\Delta p_{max}$',fontsize=FontSize)\n",
    "ax.legend(fontsize=FontSize)\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(FontSize)\n",
    "ax.grid()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebbbc31d-9afd-4532-b7bd-9cfe99e679a7",
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    "---\n",
    "## Task 2.2\n",
    "---\n",
    "\n",
    "Show the residuals plot and another plot that justifies that the simulation has run long enough and is converged.\n",
    "\n",
    "**Note!:** you might need to run it longer than the specified 0.5s \n",
    "\n",
    "1. extract plots from `Star-CCM+`\n",
    "2. upload extracted images to `Jupyter Lab`\n",
    "3. include the uploaded images in this notebook using the following Markdown code \\!\\[title\\]\\(filename\\)\n",
    "\n",
    "<font color=red>**include your figures here**</font>\n",
    "\n",
    "---\n",
    "## Task 2.3\n",
    "---\n",
    "The cylinder force monitor signal that you extracted as part of simulation 2 should, if done correctly, be oscillating with a very pronounced sinusoidal shape. Calculate the dominating frequency of the signal. Calculate the corresponding Strouhal number (non-dimensional frequency). \n",
    "\n",
    "$$St=\\dfrac{fD}{U_\\infty}$$\n",
    "\n",
    "1. What Strouhal number do you get?\n",
    "\n",
    "   <font color=red>**write your answer here**</font>\n",
    "\n",
    "2. Compare your result with the data presented in the figure below. Does your result agree with the data provided in the figure?\n",
    "\n",
    "   <font color=red>**write your answer here**</font>\n",
    "\n",
    "3. What is the cause of the oscillation of the cylinder force? \n",
    "\n",
    "   <font color=red>**write your answer here**</font>\n",
    "\n",
    "**Hint:** run the cylinder simulation with the `Vorticity Scene` open and observe what happens with the flow over time as the simulation runs.\n",
    "\n",
    "![Strouhal](https://fluidmech.g3dflow.com/Assignments/CA2/Images/Strouhal.png)\n"
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