{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Friction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental friction factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from fluids.friction import friction_factor, oregon_Res, oregon_fd_smooth\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "Res = np.logspace(np.log10(oregon_Res[0]), np.log10(oregon_Res[-1]), 500)\n",
    "fds_calc = [friction_factor(Re) for Re in Res]\n",
    "plt.loglog(oregon_Res, oregon_fd_smooth, 'x', label='Oregon Data')\n",
    "plt.loglog(Res, fds_calc, label='Colebrook')\n",
    "plt.xlabel('Reynolds number')\n",
    "plt.ylabel('Darcy friction factor')\n",
    "plt.title(\"Experimental friction factor data for smooth pipe\")\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11.21, 20.22, 29.28, 43.19, 57.73, 64.58, 86.05, 113.3, 135.3, 157.5, 179.4, 206.4, 228.0, 270.9, 315.2, 358.9, 402.9, 450.2, 522.5, 583.1, 671.8, 789.8, 891.0, 1013.0, 1197.0, 1300.0, 1390.0, 1669.0, 1994.0, 2227.0, 2554.0, 2868.0, 2903.0, 2926.0, 2955.0, 2991.0, 2997.0, 3047.0, 3080.0, 3264.0, 3980.0, 4835.0, 5959.0, 8162.0, 10900.0, 13650.0, 18990.0, 29430.0, 40850.0, 59220.0, 84760.0, 120000.0, 176000.0, 237700.0, 298200.0, 467800.0, 587500.0, 824200.0, 1050000.0]\n",
      "[5.537, 3.492, 2.329, 1.523, 1.173, 0.9863, 0.7826, 0.5709, 0.4815, 0.4182, 0.3655, 0.3237, 0.2884, 0.2433, 0.2077, 0.1834, 0.1656, 0.1475, 0.1245, 0.1126, 0.09917, 0.08501, 0.07722, 0.06707, 0.0588, 0.05328, 0.04815, 0.04304, 0.03739, 0.03405, 0.03091, 0.02804, 0.03182, 0.03846, 0.03363, 0.04124, 0.035, 0.03875, 0.04285, 0.0426, 0.03995, 0.03797, 0.0361, 0.03364, 0.03088, 0.02903, 0.0267, 0.02386, 0.02086, 0.02, 0.01805, 0.01686, 0.01594, 0.01511, 0.01462, 0.01365, 0.01313, 0.01244, 0.01198]\n"
     ]
    }
   ],
   "source": [
    "print(oregon_Res)\n",
    "print(oregon_fd_smooth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Roughness data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Brass': 1.52e-06,\n",
       " 'Lead': 1.52e-06,\n",
       " 'Glass': 1.52e-06,\n",
       " 'Steel': 1.52e-06,\n",
       " 'Asphalted cast iron': 0.000122,\n",
       " 'Galvanized iron': 0.000152,\n",
       " 'Cast iron': 0.000259,\n",
       " 'Wood stave': 0.000183,\n",
       " 'Rough wood stave': 0.000914,\n",
       " 'Concrete': 0.000305,\n",
       " 'Rough concrete': 0.00305,\n",
       " 'Riveted steel': 0.000914,\n",
       " 'Rough riveted steel': 0.00914}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fluids.friction import _roughness\n",
    "# Material from Perry's handbook; roughness in meters.\n",
    "_roughness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Really good table from Idelʹchik, I. E, and A. S Ginevskiĭ. Handbook of Hydraulic \n",
    "Resistance. Redding, CT: Begell House, 2007.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Rough channels in rock, Blast-hewed, little jointing': (0.1, 0.14, None),\n",
       " 'Rough channels in rock, Blast-hewed, substantial jointing': (0.13,\n",
       "  0.5,\n",
       "  None),\n",
       " 'Rough channels in rock, Roughly cut or very uneven surface': (0.5,\n",
       "  1.5,\n",
       "  None),\n",
       " 'Unlined tunnels, Rocks, gneiss, diameter 3-13.5 m': (0.3, 0.7, None),\n",
       " 'Unlined tunnels, Rocks, granite, diameter 3-9 m': (0.2, 0.7, None),\n",
       " 'Unlined tunnels, Shale, diameter, diameter 9-12 m': (0.25, 0.65, None),\n",
       " 'Unlined tunnels, Shale, quartz, quartzile, diameter 7-10 m': (0.2,\n",
       "  0.6,\n",
       "  None),\n",
       " 'Unlined tunnels, Shale, sedimentary, diameter 4-7 m': (None, None, 0.4),\n",
       " 'Unlined tunnels, Shale, nephrite bearing, diameter 3-8 m': (None, None, 0.2),\n",
       " 'Wood tubes, Boards, thoroughly dressed': (None, None, 0.00015),\n",
       " 'Wood tubes, Boards, well dressed': (None, None, 0.0003),\n",
       " 'Wood tubes, Boards, undressed but fitted': (None, None, 0.0007),\n",
       " 'Wood tubes, Boards, undressed': (None, None, 0.001),\n",
       " 'Wood tubes, Staved': (None, None, 0.0006),\n",
       " 'Plywood tubes, Birch plywood, transverse grain, good quality': (None,\n",
       "  None,\n",
       "  0.00012),\n",
       " 'Plywood tubes, Birch plywood, longitudal grain, good quality': (3e-05,\n",
       "  5e-05,\n",
       "  None),\n",
       " 'Glass tubes, Glass': (1.5e-06, 1e-05, None),\n",
       " 'Concrete water conduits, no finish, New and finished with plater; excellent manufacture (joints aligned, prime coated and smoothed)': (5e-05,\n",
       "  0.00015,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Used and corroded; with a wavy surface and wood framework': (0.001,\n",
       "  0.004,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Old, poor fitting and manufacture; with an overgrown surface and deposits of sand and gravel': (0.001,\n",
       "  0.004,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Very old; damaged surface, very overgrown': (0.005,\n",
       "  None,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Water conduit, finished with smoothed plaster': (0.005,\n",
       "  None,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, New, very well manufactured, hand smoothed, prime-coated joints': (0.0001,\n",
       "  0.0002,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Hand-smoothed cement finish and smoothed joints': (0.00015,\n",
       "  0.00035,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Used, no deposits, moderately smooth, steel or wooden casing, joints prime coated but not smoothed': (0.0003,\n",
       "  0.0006,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Used, prefabricated monoliths, cement plaster (wood floated), rough joints': (0.0005,\n",
       "  0.001,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Conduits for water, sprayed surface of concrete': (0.0005,\n",
       "  0.001,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Brushed air-placed, either sprayed concrete or concrete on more concrete': (None,\n",
       "  None,\n",
       "  0.0023),\n",
       " 'Concrete water conduits, no finish, Non-smoothed air-placed, either sprayed concrete or concrete on more concrete': (0.003,\n",
       "  0.006,\n",
       "  None),\n",
       " 'Concrete water conduits, no finish, Smoothed air-placed, either sprayed concrete or concrete on more concrete': (0.006,\n",
       "  0.017,\n",
       "  0.0005),\n",
       " 'Reinforced concrete tubes, New': (0.00025, 0.00034, None),\n",
       " 'Reinforced concrete tubes, Nonprocessed': (0.0025, None, None),\n",
       " 'Asbestos cement tubes, New': (5e-05, 0.0001, None),\n",
       " 'Asbestos cement tubes, Average': (0.0006, None, None),\n",
       " 'Cement tubes, Smoothed': (0.0003, 0.0008, None),\n",
       " 'Cement tubes, Non processed': (0.001, 0.002, None),\n",
       " 'Cement tubes, Joints, non smoothed': (0.0019, 0.0064, None),\n",
       " 'Cement-mortar plaster channels, Plaster, cement, smoothed joints and protrusions, and a casing': (5e-05,\n",
       "  0.00022,\n",
       "  None),\n",
       " 'Cement-mortar plaster channels, Steel trowled': (None, None, 0.0005),\n",
       " 'Other, Plaster over a screen': (0.01, 0.015, None),\n",
       " 'Other, Salt-glazed ceramic': (None, None, 0.0014),\n",
       " 'Other, Slag-concrete': (None, None, 0.0015),\n",
       " 'Other, Slag and alabaster-filling': (0.001, 0.0015, None),\n",
       " 'Seamless tubes made from brass, copper, lead, aluminum, Commercially smooth': (1.5e-06,\n",
       "  1e-05,\n",
       "  None),\n",
       " 'Seamless steel tubes, New and unused': (2e-05, 0.0001, None),\n",
       " 'Seamless steel tubes, Cleaned, following years of use': (None, 4e-05, None),\n",
       " 'Seamless steel tubes, Bituminized': (None, 4e-05, None),\n",
       " 'Seamless steel tubes, Heating systems piping; either superheated steam pipes, or just water pipes of systems with deaerators and chemical treatment': (None,\n",
       "  None,\n",
       "  0.0001),\n",
       " 'Seamless steel tubes, Following one year as a gas pipeline': (None,\n",
       "  None,\n",
       "  0.00012),\n",
       " 'Seamless steel tubes, Following multiple year as a gas pipeline': (4e-05,\n",
       "  0.0002,\n",
       "  None),\n",
       " 'Seamless steel tubes, Casings in gas wells, different conditions, several years of use': (6e-05,\n",
       "  0.00022,\n",
       "  None),\n",
       " 'Seamless steel tubes, Heating systems, saturated steam ducts or water pipes (with minor water leakage < 0.5%, and balance water deaerated)': (None,\n",
       "  None,\n",
       "  0.0002),\n",
       " 'Seamless steel tubes, Water heating system pipelines, any source': (None,\n",
       "  None,\n",
       "  0.0002),\n",
       " 'Seamless steel tubes, Oil pipelines, intermediate operating conditions ': (None,\n",
       "  None,\n",
       "  0.0002),\n",
       " 'Seamless steel tubes, Corroded, moderately ': (None, None, 0.0004),\n",
       " 'Seamless steel tubes, Scale, small depositions only ': (None, None, 0.0004),\n",
       " 'Seamless steel tubes, Condensate pipes in open systems or periodically operated steam pipelines': (None,\n",
       "  None,\n",
       "  0.0005),\n",
       " 'Seamless steel tubes, Compressed air piping': (None, None, 0.0008),\n",
       " 'Seamless steel tubes, Following multiple years of operation, generally corroded or with small amounts of scale': (0.00015,\n",
       "  0.001,\n",
       "  None),\n",
       " 'Seamless steel tubes, Water heating piping without deaeration but with chemical treatment of water; leakage up to 3%; or condensate piping operated periodically': (None,\n",
       "  None,\n",
       "  0.001),\n",
       " 'Seamless steel tubes, Used water piping': (0.0012, 0.0015, None),\n",
       " 'Seamless steel tubes, Poor condition': (0.005, None, None),\n",
       " 'Welded steel tubes, Good condition': (4e-05, 0.0001, None),\n",
       " 'Welded steel tubes, New and covered with bitumen': (None, None, 5e-05),\n",
       " 'Welded steel tubes, Used and covered with partially dissolved bitumen; corroded': (None,\n",
       "  None,\n",
       "  0.0001),\n",
       " 'Welded steel tubes, Used, suffering general corrosion': (None,\n",
       "  None,\n",
       "  0.00015),\n",
       " 'Welded steel tubes, Surface looks like new, 10 mm lacquer inside, even joints': (0.0003,\n",
       "  0.0004,\n",
       "  None),\n",
       " 'Welded steel tubes, Used Gas mains': (None, None, 0.0005),\n",
       " 'Welded steel tubes, Double or simple transverse riveted joints; with or without lacquer; without corrosion': (0.0006,\n",
       "  0.0007,\n",
       "  None),\n",
       " 'Welded steel tubes, Lacquered inside but rusted': (0.00095, 0.001, None),\n",
       " 'Welded steel tubes, Gas mains, many years of use, with layered deposits': (None,\n",
       "  None,\n",
       "  0.0011),\n",
       " 'Welded steel tubes, Non-corroded and with double transverse riveted joints': (0.0012,\n",
       "  0.0015,\n",
       "  None),\n",
       " 'Welded steel tubes, Small deposits': (None, None, 0.0015),\n",
       " 'Welded steel tubes, Heavily corroded and with  double transverse riveted joints': (None,\n",
       "  None,\n",
       "  0.002),\n",
       " 'Welded steel tubes, Appreciable deposits': (0.002, 0.004, None),\n",
       " 'Welded steel tubes, Gas mains, many years of use, deposits of resin/naphthalene': (None,\n",
       "  None,\n",
       "  0.0024),\n",
       " 'Welded steel tubes, Poor condition': (0.005, None, None),\n",
       " 'Riveted steel tubes, Riveted laterally and longitudinally with one line; lacquered on the inside': (0.0003,\n",
       "  0.0004,\n",
       "  None),\n",
       " 'Riveted steel tubes, Riveted laterally and longitudinally with two lines; with or without lacquer on the inside and without corrosion': (0.0006,\n",
       "  0.0007,\n",
       "  None),\n",
       " 'Riveted steel tubes, Riveted laterally with one line and longitudinally with two lines; thickly lacquered or torred on the inside': (0.0012,\n",
       "  0.0014,\n",
       "  None),\n",
       " 'Riveted steel tubes, Riveted longitudinally with six lines, after extensive use': (None,\n",
       "  None,\n",
       "  0.002),\n",
       " 'Riveted steel tubes, Riveted laterally with four line and longitudinally with six lines; overlapping joints inside': (None,\n",
       "  None,\n",
       "  0.004),\n",
       " 'Riveted steel tubes, Extremely poor surface; overlapping and uneven joints': (0.005,\n",
       "  None,\n",
       "  None),\n",
       " 'Roofing steel sheets, Oiled': (0.00015, 0.0011, None),\n",
       " 'Roofing steel sheets, Not Oiled': (2e-05, 4e-05, None),\n",
       " 'Galzanized steel tubes, Bright galvanization; new': (7e-05, 0.0001, None),\n",
       " 'Galzanized steel tubes, Ordinary galvanization': (0.0001, 0.00015, None),\n",
       " 'Galzanized sheet steel, New': (None, None, 0.00015),\n",
       " 'Galzanized sheet steel, Used previously for water': (None, None, 0.00018),\n",
       " 'Steel tubes, Glass enamel coat': (1e-06, 1e-05, None),\n",
       " 'Steel tubes, New': (0.00025, 0.001, None),\n",
       " 'Cast-iron tubes, New, bituminized': (0.0001, 0.00015, None),\n",
       " 'Cast-iron tubes, Coated with asphalt': (0.00012, 0.0003, None),\n",
       " 'Cast-iron tubes, Used water pipelines': (None, None, 0.0014),\n",
       " 'Cast-iron tubes, Used and corroded': (0.001, 0.0015, None),\n",
       " 'Cast-iron tubes, Deposits visible': (0.001, 0.0015, None),\n",
       " 'Cast-iron tubes, Substantial deposits': (0.002, 0.004, None),\n",
       " 'Cast-iron tubes, Cleaned after extensive use': (0.0003, 0.0015, None),\n",
       " 'Cast-iron tubes, Severely corroded': (None, 0.003, None),\n",
       " 'Steel water conduits in generating stations, New, clean, seamless (without joints), well fitted': (1.5e-05,\n",
       "  4e-05,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, welded lengthwise and well fitted': (1.2e-05,\n",
       "  3e-05,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, welded lengthwise and well fitted, with transverse welded joints': (8e-05,\n",
       "  0.00017,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, coated, bituminized when manufactured': (1.4e-05,\n",
       "  1.8e-05,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, coated, bituminized when manufactured, with transverse welded joints': (0.0002,\n",
       "  0.0006,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, coated, galvanized': (0.0001,\n",
       "  0.0002,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, coated, roughly galvanized': (0.0004,\n",
       "  0.0007,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, New, clean, coated, bituminized, curved': (0.0001,\n",
       "  0.0014,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, Used, clean, slight corrosion': (0.0001,\n",
       "  0.0003,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, Used, clean, moderate corrosion or slight deposits': (0.0003,\n",
       "  0.0007,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, Used, clean, severe corrosion': (0.0008,\n",
       "  0.0015,\n",
       "  None),\n",
       " 'Steel water conduits in generating stations, Used, clean, previously cleaned of either deposits or rust': (0.00015,\n",
       "  0.0002,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, all welded, <2 years use, no deposits': (0.00012,\n",
       "  0.00024,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, all welded, <20 years use, no deposits': (0.0006,\n",
       "  0.005,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, iron-bacterial corrosion': (0.003,\n",
       "  0.004,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, heavy corrosion, or with incrustation (deposit 1.5 - 9 mm deep)': (0.003,\n",
       "  0.005,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, heavy corrosion, or with incrustation (deposit 3 - 25 mm deep)': (0.006,\n",
       "  0.0065,\n",
       "  None),\n",
       " 'Used steel water conduits in generating stations, Used, inside coating, bituminized, < 2 years use': (0.0001,\n",
       "  0.00035,\n",
       "  None)}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from fluids.friction import HHR_roughness\n",
    "HHR_roughness"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}