path: root/Problems_In_Fluid_Flow/ch10.ipynb

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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Chapter 10 : SEdimentation and classification"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.1  pageno : 186"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "%matplotlib inline\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "#example 10.1\n",
      "# Initialization of Variable\n",
      "t = [0, 0.5, 1. ,2. ,3., 4., 5., 6., 7., 8., 9., 10.]             #time\n",
      "h = [1.10 ,1.03, .96, .82, .68, .54, .42, .35, .31, .28, .27, .27]\n",
      "Cl = [0 ,0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "m = 0.05\n",
      "V = 1/1000.             #volume\n",
      "v = [0,0,0,0,0,0,0,0,0,0,0]\n",
      "\n",
      "#calculations\n",
      "Co = m/V            #concentration at t = 0\n",
      "v[0] = (h[0]-h[1])/(t[1]-t[0])\n",
      "Cl[0] = Co\n",
      "for i in range(1,11):\n",
      "        v[i] = (h[i-1]-h[i+1])/(t[i+1]-t[i-1])          #slope or settling velocity\n",
      "        Cl[i] = Co*h[0]/(h[i]+v[i]*t[i])\n",
      "\n",
      "plot(t,h,'r--d')\n",
      "clf()\n",
      "plot(Cl,v,'r->')\n",
      "print Cl,v\n",
      "suptitle(\"Concentration vs Settling veocity\")\n",
      "xlabel(\"Concentration(kg/m**3)\")\n",
      "ylabel(\"Settling velocity (m/h)\")\n",
      "show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n",
        "[50.0, 50.0, 50.0, 50.000000000000014, 50.000000000000014, 51.88679245283019, 61.452513966480446, 80.88235294117649, 99.09909909909915, 125.00000000000003, 174.60317460317458]"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " [0.14000000000000012, 0.14000000000000012, 0.14000000000000004, 0.13999999999999996, 0.13999999999999996, 0.13000000000000003, 0.09500000000000003, 0.05499999999999999, 0.034999999999999976, 0.01999999999999999, 0.0050000000000000044]\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['draw_if_interactive']\n",
        "`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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hjFY0bG1tyczM1C9nZmZiZ2dn0L4HDhwgLi6O+Ph4CgsLuXbtGhMmTGDjxo33bFu6aNQk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\nYTApGkIIIQwmRUMIIYTBpGgIIYQw2P8HesHkU34xCuAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2d35450>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.2 page no : 188"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "from numpy import linspace\n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Initialization of Variable\n",
      "t = [0, 0.5, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10]         #time\n",
      "h = [1.10 ,1.03, .96, .82, .68, .54, .42, .35, .31, .28, .27, .27]\n",
      "Cl = linspace(50,100,5)\n",
      "U = [19.53, 17.71, 16.20, 14.92, 13.82, 12.87, 12.04, 11.31, 10.65, 9.55]           #mass ratio of liquid to solid\n",
      "v = [0.139, 0.115, 0.098, 0.083, 0.071, 0.062, 0.055, 0.049, 0.043, 0.034]                   #terminal velocity\n",
      "\n",
      "#above value taken from graph given with ques.\n",
      "C = 130.            #conc. of solids\n",
      "Q = 0.06            #slurry rate\n",
      "Cmax = 130.         #maximum solid conc.\n",
      "rhos = 2300.        #density of solid\n",
      "rho = 998.          #density of water\n",
      "V = rho*(1/C-1/rhos)\n",
      "F = Q*Cl[0]*3600.\n",
      "A = [0,0,0,0,0,0,0,0,0,0]\n",
      "for i in range(10):\n",
      "    A[i] = F*(U[i]-V)/rho/v[i]\n",
      "\n",
      "plot(v,A,'r-')\n",
      "xlabel(\"Settling Velocity(m/h)\")\n",
      "ylabel(\"Area(m**2)\")\n",
      "show()\n",
      "\n",
      "#maxima finding using datatraveller in the graph\n",
      "print \"the area for each settling velocity\",A\n",
      "print \"1005 m**2 is the maximum area found out from the plot\"\n",
      "\n",
      "Qu = Q-F/3600./Cmax\n",
      "print  \"Volumetric flow rate of clarified water in (m**3/s): %.4f\"%Qu\n",
      " \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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A5cuXk5SUhLe3NwEBAQQFBbF161ZXl+1ahw/bFyLGxVldiYhIrVw+btKpUyce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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x219ea90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "the area for each settling velocity [956.58577326448562, 984.95679741043818, 989.07500346402981, 1000.9355052127868, 1002.4484410905754, 982.14964830945723, 943.84207758782964, 898.19417690065814, 857.42395579055744, 734.27713187539746]\n",
        "1005 m**2 is the maximum area found out from the plot\n",
        "Volumetric flow rate of clarified water in (m**3/s): 0.0369\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.3 pageno : 192"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "from numpy import linspace\n",
      "\n",
      "# Initialization of Variable\n",
      "rho1 = 2600.        #density lighter\n",
      "rho2 = 5100.        #density heavier\n",
      "pd1 = linspace(0.000015,0.000095,9)          #particle diameter lighter\n",
      "pd2 = linspace(0.000025,0.000095,8)           #particle diameter heavier\n",
      "wp1 = [0 ,22 ,35, 47, 59, 68, 75, 81 ,100]                  #weight distribution lighter\n",
      "wp2 = [0, 21, 33.5, 48, 57.5, 67 ,75, 100]                 #weight distribution heavier\n",
      "rho = 998.6                                         #density water\n",
      "mu = 1.03/1000                                      #viscosity water\n",
      "g = 9.81\n",
      "u = 0.004                                           #velocity of water\n",
      "d = 95/1000000.                                     #paeticle diameter maximum\n",
      "\n",
      "#calculation\n",
      "#part 1\n",
      "Re = d*u*rho/mu\n",
      "d1 = math.sqrt(18*mu*u/g/(rho1-rho))\n",
      "d2 = math.sqrt(18*mu*u/g/(rho2-rho))\n",
      "def inter(d,f,g,b):                                 #interpolation linear\n",
      "    for i in range(b):\n",
      "        if d <= f[i+1] and d>f[i]:\n",
      "            break\n",
      "        else: \n",
      "            continue\n",
      "    a = (d-f[i])/(f[i+1]-f[i])*(g[i+1]-g[i])+g[i]\n",
      "    return a\n",
      "\n",
      "a = inter(d1,pd1,wp1,9)\n",
      "b = inter(d2,pd2,wp2,8)\n",
      "v2 = 1./(1+5.)*100.-b/100.*1./(1+5)*100\n",
      "v1 = 5./(1+5.)*100.-a/100.*5./(1+5)*100\n",
      "pl2 = (v2)/(v2+v1)\n",
      "print  \"The fraction of heavy ore remained in bottom %.4f\"%pl2\n",
      " \n",
      "#part 2\n",
      "rho = 1500.\n",
      "mu = 6.25/10000\n",
      "a = math.log10(2*d**3*rho*g*(rho1-rho)*3*mu**2)     #math.log10(Re**2(R/rho/mu**2))\n",
      "\n",
      "#using value from chart(graph)\n",
      "Re = 10.**0.2136\n",
      "u = Re*mu/rho/d\n",
      "d2 = math.sqrt(18*mu*u/g/(rho1-rho))\n",
      "b = inter(d2,pd2,wp2,8)\n",
      "print \"The percentage of heavy ore left in this case %.4f\"%(100-b+3.5)\n",
      "\n",
      "#part 3\n",
      "a = 0.75                                        #% of heavy ore in overhead product\n",
      "s = 100.*5./6./(100*5./6+0.75*100./6)\n",
      "print \"the fraction of light ore in overhead product: %.4f\"%s\n",
      "\n",
      "#part 4\n",
      "da = pd2[0]\n",
      "db = pd1[8]\n",
      "rho = (da**2*rho2-db**2*rho1)/(-db**2+da**2)\n",
      "print \"The minimum density required to seperate 2 ores in kg/m**3: %.4f\"%rho\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The fraction of heavy ore remained in bottom 0.3197\n",
        "The percentage of heavy ore left in this case 24.8188\n",
        "the fraction of light ore in overhead product: 0.8696\n",
        "The minimum density required to seperate 2 ores in kg/m**3: 2413.9881\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.4 page no : 198"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "from numpy import true_divide\n",
      "\n",
      "# Initialization of Variable\n",
      "\n",
      "rho = 998.\n",
      "w0 = 40.            #density of slurry\n",
      "mu = 1.01/1000\n",
      "g = 9.81\n",
      "rho1 = 2660.        #density quartz\n",
      "h = 0.25\n",
      "t = 18.5*60\n",
      "mp = [5 ,11.8, 20.2, 24.2, 28.5, 37.6 ,61.8]\n",
      "d = true_divide([30.2, 21.4, 17.4, 16.2, 15.2, 12.3, 8.8],1000000)\n",
      "u = h/t\n",
      "d1 = math.sqrt(18*mu*u/g/(rho1-rho))\n",
      "def inter(d,f,g,b):            #interpolation linear\n",
      "    for i in range(b):\n",
      "        if d > f[i+1] and d <= f[i]:\n",
      "            break\n",
      "        else: \n",
      "            continue\n",
      "        break\n",
      "    \n",
      "    a = -(d-f[i+1])/(f[i]-f[i+1])*(g[i+1]-g[i])+g[i+1]\n",
      "    return a\n",
      "a = inter(d1,d,mp,6)\n",
      "phi = 1-a/100.\n",
      "rhot = phi*(rho1-rho)/rho1*w0+rho\n",
      "print \"the density of suspension at depth 25cm in kg/m**3 is %.4f\"%rhot\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "the density of suspension at depth 25cm in kg/m**3 is 1016.5653\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.5 pag eno : 200"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Initialization of Variable\n",
      "t = [0, 45, 135, 495, 1875, 6900, 66600, 86400]             #time\n",
      "m = [0.1911, 0.1586, 0.1388, 0.1109, 0.0805, 0.0568, 0.0372, 0.0359]        #mass total\n",
      "rho1 = 3100.                    #density of cement\n",
      "mu = 1.2/1000                   #viscosity of desperant liquid\n",
      "rho = 790.                      #density of desperant liquid\n",
      "h = 0.2\n",
      "V = 10.\n",
      "s = 0.\n",
      "g = 9.81\n",
      "d = [0,0,0,0,0,0,0,0]\n",
      "mc = [0,0,0,0,0,0,0,0]\n",
      "mp = [0,0,0,0,0,0,0,0]\n",
      "d[0] = 100./1000000             #assumed value\n",
      "\n",
      "for i in range(7):\n",
      "    d[i+1] = math.sqrt(18*mu*h/g/t[i+1]/(rho1-rho))         #dia of particles\n",
      "    mc[i+1] = m[i+1]-0.2/100*V                              #mass of cement\n",
      "    s = s+mc[i+1]  \n",
      "\n",
      "mc[0] = m[0]-0.2*V/100\n",
      "s = s+mc[0]\n",
      "mp[0] = 100.\n",
      "\n",
      "for i in range(7):\n",
      "    mp[i+1] = mc[i+1]/mc[0]*100.            #mass percent below size\n",
      "\n",
      "plot(mp,d)\n",
      "xlabel(\"%undersize\")\n",
      "ylabel(\"Particle Size(m)\")\n",
      "\n",
      "show()\n",
      "u = h/t[1]\n",
      "Re = d[1]*u*rho/mu\n",
      "if Re<2:\n",
      "    print \"since Re<2 for 81% of particles so settlement occurs mainly by stoke-s law\"\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5OcjIyMAf//hHzJkzx/ynNVJTU+Hr6wu9Xo+EhIQmt5k1axb0ej0MBgNyc3Ot7nvmzBlE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2c49850>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "since Re<2 for 81% of particles so settlement occurs mainly by stoke-s law\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.6 page no : 204"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "from matplotlib.pyplot import *\n",
      "\n",
      "# Initialization of Variable\n",
      "rho = 998.\n",
      "rho1 = 2398.            #density of ore\n",
      "mu = 1.01/1000.\n",
      "g = 9.81\n",
      "h = 25/100.\n",
      "t = [114. ,150., 185., 276., 338., 396., 456., 582., 714., 960.]\n",
      "m = [0.1429 ,0.2010, 0.2500, 0.3564, 0.4208, 0.4781, 0.5354 ,0.6139, 0.6563, 0.7277]\n",
      "d = [0,0,0,0,0,0,0,0,0,0]\n",
      "P = [0,0,0,0,0,0,0,0,0,0]\n",
      "\n",
      "for i in range(10):\n",
      "    ms = 0.0573+m[9]        #total mass setteled\n",
      "    d[i] = math.sqrt(18.*mu*h/g/(rho1-rho)/t[i])\n",
      "    P[i] = m[i]/ms*100      #mass percent of sample\n",
      "\n",
      "plot(t,P)\n",
      "xlabel(\"Settling time (s)\")\n",
      "ylabel(\"mass percent in (%)\")\n",
      "show()\n",
      "print \"& its percentage mass distribution respectively\" ,\"the particle size distribution in (m)\" ,P,d\n",
      "W = [0,0,0,0,0,0,0,0,0,0]\n",
      "de = [0,0,0,0,0,0,0,0,0,0]\n",
      "for i in range(9):\n",
      "    de[i] = (P[i+1]-P[i-1])/(t[i+1]-t[i-1])        #slope \n",
      "    W[i] = P[i]-t[i]*de[i]\n",
      "    W[0] = P[0]-P[0]\n",
      "\n",
      "W[9] = P[9]-t[9] * 0.025\n",
      "print \"mass and diameter(m)respectively with serial no:\"\n",
      "\n",
      "for i in range(4,10):\n",
      "    print i-4,\n",
      "    print \"mass, is\",\n",
      "    print \"for diameter in(m) of %f   %f\"%(W[i],d[i])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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z3lIlFbk0pk2DV1+1CqOE1xGIiFwWr5bGxo0beeWVV0hNTSU/P98d4KuvviqV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\nEA92IY9i/rEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2fe4c90>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "& its percentage mass distribution respectively the particle size distribution in (m) [18.203821656050955, 25.605095541401273, 31.84713375796178, 45.40127388535032, 53.60509554140127, 60.904458598726116, 68.20382165605096, 78.20382165605095, 83.60509554140127, 92.70063694267515] [5.387856206749596e-05, 4.6970241455099756e-05, 4.229436978392301e-05, 3.4626921406038745e-05, 3.129032486308103e-05, 2.890818526183746e-05, 2.693928103374798e-05, 2.3845527268417837e-05, 2.1528774873471063e-05, 1.856661815577208e-05]\n",
        "mass and diameter(m)respectively with serial no:\n",
        "0 mass, is for diameter in(m) of 9.937792   0.000031\n",
        "1 mass, is for diameter in(m) of 11.912124   0.000029\n",
        "2 mass, is for diameter in(m) of 25.792480   0.000027\n",
        "3 mass, is for diameter in(m) of 43.461413   0.000024\n",
        "4 mass, is for diameter in(m) of 56.222222   0.000022\n",
        "5 mass, is for diameter in(m) of 68.700637   0.000019\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "example 10.7 page no : 208"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "\n",
      "import math \n",
      "\n",
      "# Initialization of Variable\n",
      "\n",
      "rho = 1002.             #density of print erant\n",
      "rho1 = 2240.            #density of kaolin\n",
      "mu = 1.01/1000          #viscosity\n",
      "g = 9.81\n",
      "t = 600.\n",
      "h2 = 0.2\n",
      "h1 = 0.4\n",
      "dg = 15.*10**-6         #particle size to be removed\n",
      "\n",
      "#calculations\n",
      "#part 1\n",
      "d = math.sqrt(18*mu*h2/g/(rho1-rho)/t)\n",
      "x = dg/d\n",
      "f = h2/h1*(1-x**2)      #fraction separated after first decanting\n",
      "g = f*(1-f)\n",
      "print \"fraction of particles separated after second decanting %.4f\"%g\n",
      "print \"total fraction of particles separated after decanting %.4f\"%(f+g)\n",
      "\n",
      "#part 2\n",
      "h = (1.-20/40.*(1-x**2))**6\n",
      "print \"fraction of particles separated after sixth decanting %.4f\"%h\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "fraction of particles separated after second decanting 0.1992\n",
        "total fraction of particles separated after decanting 0.4737\n",
        "fraction of particles separated after sixth decanting 0.1458\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}