Bernouli.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "a513fb47",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "a01ac564",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hydrostatic pressure at node i [mmHg]\n",
    "# Total Head at node i [mmHg]\n",
    "# i = 1, 2, 3, 4, 5, 6\n",
    "\n",
    "# 10 litres in 57.3 seconds\n",
    "hyplst_1 = np.array([275, 240, 15, 145, 170, 180])\n",
    "tothd_1 = np.array([28.8, 26.8, 26.4, 24.2, 22.6, 21.2])\n",
    "\n",
    "\n",
    "# 10 litres in 108 seconds\n",
    "hyplst_2 = np.array([210, 205, 115, 155, 160, 165])\n",
    "tothd_2 = np.array([22.0, 21.5, 21.1, 19.4, 17.7, 17.9])\n",
    "\n",
    "# 10 litres in 190 seconds\n",
    "hyplst_3 = np.array([130, 120, 95, 107.5, 110, 107.5])\n",
    "tothd_3 = np.array([14.1, 13.6, 12.3, 11.8, 11.9, 11.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bac73b3",
   "metadata": {},
   "source": [
    "## Error Analysis (GUM Type A Evaluation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49654961",
   "metadata": {},
   "source": [
    "## Sample Standard Deviation\n",
    "To show the variability of the measured values of the quantity X, we use the sample standard deviation:\n",
    "\n",
    "$s = \\sqrt{\\dfrac{\\sum_{i=1}^n (X_i - \\overline{X})^2}{n - 1}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "6eedeb4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample standard deviation of hydrostatic pressure at node 1: 72.62919523166975\n",
      "The sample standard deviation of total head at node 1: 7.356856212631408\n",
      "\n",
      "The sample standard deviation of hydrostatic pressure at node 2: 61.71169527191206\n",
      "The sample standard deviation of total head at node 2: 6.64253967495365\n",
      "\n",
      "The sample standard deviation of hydrostatic pressure at node 3: 52.91502622129181\n",
      "The sample standard deviation of total head at node 3: 7.122031545376173\n",
      "\n",
      "The sample standard deviation of hydrostatic pressure at node 4: 25.04163200219453\n",
      "The sample standard deviation of total head at node 4: 6.252466180103122\n",
      "\n",
      "The sample standard deviation of hydrostatic pressure at node 5: 32.145502536643185\n",
      "The sample standard deviation of total head at node 5: 5.356304696336832\n",
      "\n",
      "The sample standard deviation of hydrostatic pressure at node 6: 38.26987501068344\n",
      "The sample standard deviation of total head at node 6: 5.150080905513362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def cal_ssd(lst):\n",
    "    \"\"\"Returns the sample standard deviation of the measured values of quantity X. \"\"\"\n",
    "    def calMean(lst, n):\n",
    "        s = 0; \n",
    "        for i in lst: s += i;\n",
    "        mean = s/n;\n",
    "        return mean;\n",
    "    \n",
    "    n = len(lst);\n",
    "    x_bar = calMean(lst, n);\n",
    "    x_diff = [(i - x_bar)**2 for i in lst];\n",
    "    x_var = calMean(x_diff, n-1);\n",
    "    return math.sqrt(x_var);\n",
    "\n",
    "# Calculate the sample standard deviation for hydrostatic pressure and total head at each node\n",
    "ssd_hy , ssd_tothd = [], [];\n",
    "for i in range(0, 6):\n",
    "    ssd_hy.append(cal_ssd([hyplst_1[i], hyplst_2[i], hyplst_3[i]]))\n",
    "    ssd_tothd.append(cal_ssd([tothd_1[i], tothd_2[i], tothd_3[i]]))\n",
    "    print(f\"The sample standard deviation of hydrostatic pressure at node {i+1}: {ssd_hy[i]}\")\n",
    "    print(f\"The sample standard deviation of total head at node {i+1}: {ssd_tothd[i]}\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6bef309",
   "metadata": {},
   "source": [
    "## Standard Error of the mean\n",
    "The measurement of the variability of the means (if a experiment consisting of measurements repeated many times, then the distribution of menas has its own standard deviation) is represented by the standard deviation of the means (often called the standard error of the mean) as $s(\\overline{X})$\n",
    "\n",
    "$s(\\overline{X}) = \\dfrac{s}{\\sqrt{n}}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5b101afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard error of the mean for hydrostatic pressure at node 1: 41.93248541803042\n",
      "The standard error of the mean for the total head at node 1: 4.247482914752115\n",
      "\n",
      "The standard error of the mean for hydrostatic pressure at node 2: 35.629263877386585\n",
      "The standard error of the mean for the total head at node 2: 3.835072069437259\n",
      "\n",
      "The standard error of the mean for hydrostatic pressure at node 3: 30.550504633038933\n",
      "The standard error of the mean for the total head at node 3: 4.11190682989994\n",
      "\n",
      "The standard error of the mean for hydrostatic pressure at node 4: 14.45779297741456\n",
      "The standard error of the mean for the total head at node 4: 3.609863032181569\n",
      "\n",
      "The standard error of the mean for hydrostatic pressure at node 5: 18.559214542766743\n",
      "The standard error of the mean for the total head at node 5: 3.092463958291727\n",
      "\n",
      "The standard error of the mean for hydrostatic pressure at node 6: 22.09512263927142\n",
      "The standard error of the mean for the total head at node 6: 2.973400597146491\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def cal_sderr(ssd, n):\n",
    "    \"\"\"Returns the standard deviation of the means\"\"\"\n",
    "    return (ssd / math.sqrt(n));\n",
    "\n",
    "# Calculate the standard error of the mean for hydrostatic pressure and total head at each node\n",
    "sm_hy , sm_tothd = [], [];\n",
    "n = 3;\n",
    "for i in range(0, 6):\n",
    "    sm_hy.append(cal_sderr(ssd_hy[i], n))\n",
    "    sm_tothd.append(cal_sderr(ssd_tothd[i], n))\n",
    "    print(f\"The standard error of the mean for hydrostatic pressure at node {i+1}: {sm_hy[i]}\")\n",
    "    print(f\"The standard error of the mean for the total head at node {i+1}: {sm_tothd[i]}\\n\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417cae58",
   "metadata": {},
   "source": [
    "## Maximum Absolute Percentage Uncertainty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a37a61fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max abs percentage uncertainty of hydrostatic pressure at node 1: 32.25575801386955\n",
      "Max abs percentage uncertainty of the total head at node 1: 30.12399230320649\n",
      "\n",
      "Max abs percentage uncertainty of hydrostatic pressure at node 2: 29.69105323115549\n",
      "Max abs percentage uncertainty of the total head at node 2: 28.19905933409749\n",
      "\n",
      "Max abs percentage uncertainty of hydrostatic pressure at node 3: 203.67003088692624\n",
      "Max abs percentage uncertainty of the total head at node 3: 33.4301368284548\n",
      "\n",
      "Max abs percentage uncertainty of hydrostatic pressure at node 4: 13.449109746432148\n",
      "Max abs percentage uncertainty of the total head at node 4: 30.592059594759057\n",
      "\n",
      "Max abs percentage uncertainty of hydrostatic pressure at node 5: 16.87201322069704\n",
      "Max abs percentage uncertainty of the total head at node 5: 25.9870920864851\n",
      "\n",
      "Max abs percentage uncertainty of hydrostatic pressure at node 6: 20.553602455136204\n",
      "Max abs percentage uncertainty of the total head at node 6: 26.787392767085507\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def cal_ab_max_u(sderr, lst, n):\n",
    "    \"\"\"Returns the maximum absolute percentage uncertainty\"\"\"\n",
    "    result = 0;\n",
    "    for j in range(n): \n",
    "        tmp = sderr / lst[j] * 100; \n",
    "        if (tmp > result):\n",
    "            result = tmp;\n",
    "    return result;\n",
    "\n",
    "# Calculate the maximum absolute percentage uncertainty for hydrostatic pressure and total head at each node\n",
    "ab_hy , ab_tothd = [], [];\n",
    "n = 3;\n",
    "for i in range(0, 6):\n",
    "    ab_hy.append(cal_ab_max_u(sm_hy[i], \n",
    "                              [hyplst_1[i], hyplst_2[i], hyplst_3[i]], n))\n",
    "    ab_tothd.append(cal_ab_max_u(sm_tothd[i], \n",
    "                                 [tothd_1[i], tothd_2[i], tothd_3[i]], n))\n",
    "    print(f\"Max abs percentage uncertainty of hydrostatic pressure at node {i+1}: {ab_hy[i]}\")\n",
    "    print(f\"Max abs percentage uncertainty of the total head at node {i+1}: {ab_tothd[i]}\\n\")  \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fd08d12",
   "metadata": {},
   "source": [
    "## Results Calculations from experimental data\n",
    "\n",
    "Calibrate the Venturi meter and calculate the discharge coefficient"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287f63dc",
   "metadata": {},
   "source": [
    "$G = \\dfrac{C_D \\cdot \\rho \\cdot A_1 \\cdot A_2 \\cdot \\sqrt{2 \\cdot g \\cdot \\Delta h_v}}{\\sqrt{A_1^2 - A_2^2}}$\n",
    "\n",
    "$\\\\\\\\$\n",
    "\n",
    "Coulson, J.M. Richardson, J.F. Backhurst, J.R. Harker, J.H.. (1999). Coulson and Richardson's Chemical Engineering Volume 1 - Fluid Flow, Heat Transfer and Mass Transfer (6th Edition) - 6.3.5 The Venturi Meter. (pp. 255,256). Elsevier. Retrieved from \n",
    "https://app.knovel.com/hotlink/pdf/id:kt007GBHM5/coulson-richardsons-chemical/the-venturi-meter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c7464676",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For the given 10 litres in 57.3 seconds flow rate\n",
      "The discharge coefficient at node 2: 86.40609373470114\n",
      "The discharge coefficient at node 3: 291.08687698690943\n",
      "The discharge coefficient at node 4: 93.30717613262146\n",
      "The discharge coefficient at node 5: 40.75074284398662\n",
      "There is no difference in A1 and A2 such that:\n",
      "The discharge coefficient at node 6: 0.0\n",
      "\n",
      "\n",
      "For the given 10 litres in 108 seconds flow rate\n",
      "The discharge coefficient at node 2: 91.68646612959955\n",
      "The discharge coefficient at node 3: 252.19580557907557\n",
      "The discharge coefficient at node 4: 65.8473276729766\n",
      "The discharge coefficient at node 5: 25.961394303803402\n",
      "There is no difference in A1 and A2 such that:\n",
      "The discharge coefficient at node 6: 0.0\n",
      "\n",
      "\n",
      "For the given 10 litres in 190 seconds flow rate\n",
      "The discharge coefficient at node 2: 52.11651758945658\n",
      "The discharge coefficient at node 3: 101.3661649772157\n",
      "The discharge coefficient at node 4: 39.79523419755205\n",
      "The discharge coefficient at node 5: 20.63103687214385\n",
      "There is no difference in A1 and A2 such that:\n",
      "The discharge coefficient at node 6: 0.0\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Calculate the discharge coefficient\n",
    "\n",
    "\n",
    "# 10 litres in 57.3 seconds\n",
    "hyplst_1 = np.array([275, 240, 15, 145, 170, 180])\n",
    "tothd_1 = np.array([28.8, 26.8, 26.4, 24.2, 22.6, 21.2])\n",
    "\n",
    "\n",
    "# 10 litres in 108 seconds\n",
    "hyplst_2 = np.array([210, 205, 115, 155, 160, 165])\n",
    "tothd_2 = np.array([22.0, 21.5, 21.1, 19.4, 17.7, 17.9])\n",
    "\n",
    "# 10 litres in 190 seconds\n",
    "hyplst_3 = np.array([130, 120, 95, 107.5, 110, 107.5])\n",
    "tothd_3 = np.array([14.1, 13.6, 12.3, 11.8, 11.9, 11.1])\n",
    "\n",
    "G = [10/57.3, 10/108, 10/190]; #[L/s] = [kg/s] for water\n",
    "rho = 1000\n",
    "A1 = 338.6*1e-6 ; # [m^2]\n",
    "A2 = [i*1e-6 for i in [338.6, 233.5, 84.6, 170.2, 255.2, 338.6]]; #[m^2]\n",
    "\n",
    "g = 9.81\n",
    "    \n",
    "def calDischargeCoeff(G, rho, A1, A2, g, hv):\n",
    "    tmp1 = G * math.sqrt(A1**2 - A2**2)\n",
    "    tmp2 = A1 * A2 * math.sqrt(2 * g * hv )\n",
    "    #print(tmp1)\n",
    "    #print(tmp2)\n",
    "    return tmp1 / tmp2\n",
    "    \n",
    "    \n",
    "def printing(val):\n",
    "    if val == 0: print(\"There is no difference in A1 and A2 such that:\")\n",
    "\n",
    "CD1 = [];\n",
    "print(\"For the given 10 litres in 57.3 seconds flow rate\")\n",
    "for i in range(1, 6):\n",
    "    CD1.append(calDischargeCoeff(G[0], rho, A1, A2[i], g, tothd_1[0]-tothd_1[i]))\n",
    "    printing(CD1[i-1])\n",
    "    print(f\"The discharge coefficient at node {i+1}: {CD1[i-1]}\")\n",
    "print(\"\\n\")\n",
    "\n",
    "CD2 = [];\n",
    "print(\"For the given 10 litres in 108 seconds flow rate\")\n",
    "for i in range(1, 6):\n",
    "    CD2.append(calDischargeCoeff(G[1], rho, A1, A2[i], g, tothd_2[0]-tothd_2[i]))\n",
    "    printing(CD2[i-1])\n",
    "    print(f\"The discharge coefficient at node {i+1}: {CD2[i-1]}\")\n",
    "print(\"\\n\")\n",
    "\n",
    "CD3 = [];\n",
    "print(\"For the given 10 litres in 190 seconds flow rate\")\n",
    "for i in range(1, 6):\n",
    "    CD3.append(calDischargeCoeff(G[2], rho, A1, A2[i], g, tothd_3[0]-tothd_3[i]))\n",
    "    printing(CD3[i-1])\n",
    "    print(f\"The discharge coefficient at node {i+1}: {CD3[i-1]}\")\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089e789b",
   "metadata": {},
   "source": [
    "<!-- Another way to calculate the discharge coefficient\n",
    "\n",
    "$G = \\dfrac{C_D \\cdot \\rho \\cdot A_1 \\cdot A_2 \\cdot \\sqrt{2 \\cdot v \\cdot (P_1 - P_2)}}{\\sqrt{A_1^2 - A_2^2}}$\n",
    "\n",
    "$\\\\\\\\$ -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "56a921ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def calDischargeCoeff(G, rho, A1, A2, v, P1, P2):\n",
    "#     tmp1 = G * math.sqrt(A1**2 - A2**2)\n",
    "#     tmp2 = A1 * A2 * math.sqrt(2 * v * (P1 - P2) )\n",
    "#     print(tmp1)\n",
    "#     print(tmp2)\n",
    "#     return tmp1 / tmp2\n",
    "\n",
    "# Discharge coefficient for 10 litres in 57.3 seconds\n",
    "# CD1 = [];\n",
    "# for i in range(6):\n",
    "#     CD1.append(calDischargeCoeff(G[0], rho, A1, A2[i], v1[i], P1[0], P2[0][i]))\n",
    "\n",
    "# P1 = [275, 210, 130]\n",
    "# P2 = [[275, 240, 15, 145, 170, 180], \n",
    "#       [210, 205, 115, 155, 160, 165], \n",
    "#       [130, 120, 95, 107.5, 110, 107.5]]\n",
    "\n",
    "# v1 = [G[0]/i for i in A2]\n",
    "# v2 = [G[1]/i for i in A2]\n",
    "# v3 = [G[2]/i for i in A2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a4cc832",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2996a0de",
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   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "92494847",
   "metadata": {},
   "source": [
    "# Investigate the pressure changes along the Venturi meter and give a graphical representation of the pressure distribution.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "d8ac30fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "nodes = [1, 2, 3, 4, 5, 6]\n",
    "(fig, ax1) = plt.subplots(1, figsize=(10, 7))\n",
    "\n",
    "ax1.plot(nodes,hyplst_1,'r-',label='$G_1 =$ '+str(round(G[0], 4)) + ' [kg/s]')\n",
    "ax1.plot(nodes,hyplst_2,'b-',label='$G_2 =$ '+str(round(G[1], 4)) + ' [kg/s]')\n",
    "ax1.plot(nodes,hyplst_3,'g-',label='$G_3 =$ '+str(round(G[2], 4)) + ' [kg/s]')\n",
    "ax1.set_xlabel(r'Node i, where $i \\in \\{1, 2, 3, 4, 5, 6\\}$',fontsize=16)\n",
    "ax1.set_ylabel(r'$P$ [mmHg]',fontsize=16,color='black')\n",
    "ax1.tick_params(axis='y', labelcolor='red', labelsize=14)\n",
    "ax1.tick_params(axis='x', labelsize=14)\n",
    "ax1.legend(loc='upper right',fontsize=12)\n",
    "ax1.grid(True)\n",
    "\n",
    "plt.title('Distributions of hydrostatic pressure over the Venturi tube axis for of the three inlet flowrates. ',fontsize=15)\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.show()\n",
    "print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37dbb989",
   "metadata": {},
   "source": [
    "# Estimate the permanent pressure loss along the Venturi meter. \n",
    "$\\\\\\\\$\n",
    "Basu, Swapan Debnath, Ajay Kumar. (2019). Power Plant Instrumentation and Control Handbook - A Guide to Thermal Power Plants (2nd Edition) - 4.4.2.4 Orifice Plate. (pp. 282, 283, 284, 285). Elsevier. Retrieved from \n",
    "https://app.knovel.com/hotlink/pdf/id:kt0122PUY8/power-plant-instrumentation/orifice-plate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8e73e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 40% from literature values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed1751f",
   "metadata": {},
   "source": [
    "# Estimate the losses for the same length of pipe if the diameter remains constant (assuming smooth pipe). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfd59acd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "eae6eb4e",
   "metadata": {},
   "source": [
    "# Identify the type of flow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a167feeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "63373ebd",
   "metadata": {},
   "source": [
    "# Estimate the profile of the maximum velocity along the Venturi meter. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0829936f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3a807092",
   "metadata": {},
   "source": [
    "\n",
    "# Calculate the maximum and average velocity at different locations and Reynolds numbers from theory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f62f652",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2caba80d",
   "metadata": {},
   "source": [
    "# Where are the deviations from the theoretical predictions attributed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29b5ac47",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58df8c30",
   "metadata": {},
   "source": [
    "# Compare the converging and diverging sections of the Venturi meter in terms of velocity, total head, and energy losses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0261232f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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