adding book

author: Jovina Dsouza 2014-06-18 12:43:07 +0530
committer: Jovina Dsouza 2014-06-18 12:43:07 +0530
commit: 206d0358703aa05d5d7315900fe1d054c2817ddc (patch)
tree: f2403e29f3aded0caf7a2434ea50dd507f6545e2 /Fundamentals_of_Fluid_Mechanics/Ch_8.ipynb
parent: c6f0d6aeb95beaf41e4b679e78bb42c4ffe45a40 (diff)
download: Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.tar.gz
Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.tar.bz2
Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.zip
1 files changed, 1058 insertions, 0 deletions
diff --git a/Fundamentals_of_Fluid_Mechanics/Ch_8.ipynb b/Fundamentals_of_Fluid_Mechanics/Ch_8.ipynb
new file mode 100644
index 00000000..0c545286
--- /dev/null
+++ b/Fundamentals_of_Fluid_Mechanics/Ch_8.ipynb
@@ -0,0 +1,1058 @@
+{
+ "metadata": {
+  "name": "Ch 8"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "heading",
+     "level": 1,
+     "metadata": {},
+     "source": [
+      "Chapter 8: Viscous flow in pipes"
+     ]
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.1 Page no.405"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.1\n",
+      "#Determine the minimum time taken to fill the glass\n",
+      "#(b) Maximum time taken to fill the glass.\n",
+      "\n",
+      "#given\n",
+      "T1=50.0             #degree farenheit\n",
+      "D=0.73              #in\n",
+      "vol=0.0125        #ft**3\n",
+      "T2=140              #degree farenheit\n",
+      "\n",
+      "vis1=2.73*10**-5                          #lb*s/ft**2 at 50 degree farenheit\n",
+      "vis2=0.974*10**-5                        #lb*s/ft**2 at 140 degree farenheit\n",
+      "\n",
+      "#calculation\n",
+      "import math\n",
+      "#for 50 degree farenheit\n",
+      "#if flow is laminar, maximum Re=2100 Re=d*V*D/vis\n",
+      "V1=2100*vis1/(1.94*D/12)\n",
+      "t1=vol/(math.pi*((D/12)**2)/4*V1)\n",
+      "#if flow is turbulent, minimum Re=4000\n",
+      "V2=4000*vis1/(1.94*D/12)\n",
+      "t2=vol/(math.pi*((D/12)**2)/4*V2)\n",
+      "\n",
+      "#for 140 degree farenheit\n",
+      "#if flow is laminar, maximum Re=2100 Re=d*V*D/vis\n",
+      "V3=2100*vis2/(1.94*D/12)\n",
+      "t3=vol/(math.pi*((D/12)**2)/4*V3)\n",
+      "#if flow is turbulent, minimum Re=4000\n",
+      "V4=4000*vis2/(1.94*D/12)\n",
+      "t4=vol/(math.pi*((D/12)**2)/4*V4)\n",
+      "\n",
+      "#result\n",
+      "print(\"For laminar flow\")\n",
+      "print \"The time taken to fill the glass at 50 degree F=\",round(t1,2),\"seconds\"\n",
+      "print \"The time taken to fill the glass 100 degree F=\",round(t3,2),\"seconds\"\n",
+      "print (\"For turbulent flow:\")\n",
+      "print \"The time taken to fill the glass at 50 degree F=\",round(t2,2),\"seconds\"\n",
+      "print \"The time taken to fill the glass at 140 degree F=\",round(t4,2),\"seconds\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "For laminar flow\n",
+        "The time taken to fill the glass at 50 degree F= 8.85 seconds\n",
+        "The time taken to fill the glass 100 degree F= 24.81 seconds\n",
+        "For turbulent flow:\n",
+        "The time taken to fill the glass at 50 degree F= 4.65 seconds\n",
+        "The time taken to fill the glass at 140 degree F= 13.03 seconds\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.2 Page no.412"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.2\n",
+      "#(a) find the pressure drop\n",
+      "#(b)How steep a hill must the pipe be on if oil is to flow through pipe.\n",
+      "#given\n",
+      "vis=0.4                    #Ns/(m**2)\n",
+      "d=900                     #kg/(m**3)\n",
+      "D=0.02                   #m\n",
+      "Q=2.0*(10**-5)      #(m**3)/s\n",
+      "x1=0\n",
+      "x2=10                   #m\n",
+      "p1=200                 #kPa\n",
+      "x3=5                     #m\n",
+      "\n",
+      "#Calculation\n",
+      "import math\n",
+      "V=Q/(math.pi*(D**2)/4)        #m/s\n",
+      "Re=d*V*D/vis\n",
+      "print \"a) Reynolds number =\",round(Re,0),\"Hence the flow is laminar.\"\n",
+      "\n",
+      "pdiff=128*vis*(x2-x1)*Q/(math.pi*(D**4)*1000)\n",
+      "#for part b0 p1=p2 Q=math.pi*(pdiff-(sw*l*math.sin(ang)))*(D**4)/(128*vis*l)\n",
+      "ang=(math.asin(-128*vis*Q/(math.pi*d*9.81*(D**4))))*180/math.pi\n",
+      "#since sin(ang) doesn= not depend on pdiff, the the pressure is constant all along the pipe\n",
+      "#hence for c)\n",
+      "p3=p1              #kPa\n",
+      "\n",
+      "#result\n",
+      "print \"The pressure drop required if the pipe is horizontal=\",round(pdiff,1),\"kpa\"\n",
+      "print \"b) The angle of the hill the pipe must be on if the oil is to flow at the same rate as a) but with (p1=p2) =\",round(ang,2),\"degree\"\n",
+      "print \"c) For conditions of part b), the pressure at x3=5 m = \",round(p3,3),\"kpa\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "a) Reynolds number = 3.0 Hence the flow is laminar.\n",
+        "The pressure drop required if the pipe is horizontal= 20.4 kpa\n",
+        "b) The angle of the hill the pipe must be on if the oil is to flow at the same rate as a) but with (p1=p2) = -13.34 degree\n",
+        "c) For conditions of part b), the pressure at x3=5 m =  200.0 kpa\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.3 Page no.416"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "%pylab inline "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
+        "For more information, type 'help(pylab)'.\n"
+       ]
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.3\n",
+      "#(a) plot K(t) vs T\n",
+      "#(b) Determine wall shear stress and pressure drop.\n",
+      "#(c)Determine the net pressure force.\n",
+      "\n",
+      "from numpy import*\n",
+      "T=array([60,80,100,120,140,160])        #degree F\n",
+      "d=array([2.07,2.06,2.05,2.04,2.03,2.02])#(slugs/(ft**3))\n",
+      "vis=array([0.04,0.019,0.0038,0.00044,0.000092,0.000023])#lb*sec/(ft**2)\n",
+      "Q=0.5                            #(ft**3)/sec\n",
+      "T1=100.0                           #degree F\n",
+      "l=6.0                              #ft\n",
+      "D=3.0                              #in\n",
+      "\n",
+      "#Calculation\n",
+      "import math\n",
+      "pdiff=128*vis[2]*l*Q/(math.pi*(D/12)**4)\n",
+      "print \"The pressure difference is \",round(pdiff,0),\"lb/ft**2\"\n",
+      "V=Q/(math.pi*((D/12)**2)/4)     #ft/sec\n",
+      "Re=d[2]*V*(D/12)/vis[2]\n",
+      "print\"The reynolds number=\",round(Re,0),\"< 2100 hence the flow is laminar\"\n",
+      "stress=pdiff*(D/12)/(4*l)      #lb/(ft**2)\n",
+      "print \"The wall stress for the given Q and T =\",round(stress,2),\"lb/(ft**2)\"\n",
+      "Fp=(math.pi/4)*((D/12)**2)*pdiff          #lb\n",
+      "Fv=(2*math.pi)*((D/24))*l*stress        #lb\n",
+      "\n",
+      "#result\n",
+      "print \"The net pressure force =\",round(Fp,2),\"lb\"\n",
+      "print \"The net viscous/shear force =\",round(Fv,2),\"lb\"\n",
+      "\n",
+      "#PLot\n",
+      "T=[60,80,100,120,140,160]\n",
+      "K=[0.0004125,0.00086842,0.0043421,0.0375,0.173,0.695]\n",
+      "xlabel(\"T  F\") \n",
+      "ylabel(\"K  ft**5/lbs\") \n",
+      "plt.xlim((60,180))\n",
+      "plt.ylim((0.0001,1))\n",
+      "a=plot(T,K)\n",
+      "show(a)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The pressure difference is  119.0 lb/ft**2\n",
+        "The reynolds number= 1374.0 < 2100 hence the flow is laminar\n",
+        "The wall stress for the given Q and T = 1.24 lb/(ft**2)\n",
+        "The net pressure force = 5.84 lb\n",
+        "The net viscous/shear force = 5.84 lb\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n"
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.4 Page no.427"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.4\n",
+      "#Determine (a) The thickness of the viscous sublayer\n",
+      "#(b)The approximate centerline velocity\n",
+      "#C ratio of laminar to turbulant shear stress.\n",
+      "\n",
+      "#Given\n",
+      "T=20.0                                       #degree C\n",
+      "d=998.0                                     #kg/(m**3)\n",
+      "kvis=1.004*(10**-6)                   #(m**2)/s where kvis=kinematic viscosity\n",
+      "D=0.1                                        #m\n",
+      "Q=0.04                                      #(m**3)/sec\n",
+      "pgrad=2.59                                # kPa/m where pgrad is pressure gradient\n",
+      "l=1                                         #m length\n",
+      "#calculation\n",
+      "import math\n",
+      "from scipy import integrate\n",
+      "stress=D*(pgrad*1000)/(4*l)           #N/(m**2)\n",
+      "uf=(stress/d)**0.5                    #m/sec where uf is frictional velocity\n",
+      "ts=5*kvis*1000/(uf)                   #mm where ts is the thickness of the viscous sublayer\n",
+      "print \"The thickness of the viscous sublayer=\",round(ts,2),\"mm\"\n",
+      "V=Q/(math.pi*(D**2)/4)               #m/s\n",
+      "Re=V*D/kvis\n",
+      "print \"The reynolds number=\",round(Re,0),\"hence the flow is turbulent.\"\n",
+      "\n",
+      "n=8.4                                #from turbulent flow velocity profile diagram\n",
+      "#Q=(math.pi)*(R**2)*V\n",
+      "R=1                                  #say                                        \n",
+      "def f1(r):\n",
+      "    return(1/R**2*(((1-r/R)**(1/n))*2*math.pi*r))\n",
+      "q=integrate.quad(f1,0.0,R)\n",
+      "q_=q[0]\n",
+      "x=round(q_,2)/math.pi                          #x=V/Vc\n",
+      "Vc=V/x\n",
+      "print \"The approximate centerline velocity=\",round(Vc,2),\"m/s\"\n",
+      "\n",
+      "#C\n",
+      "r=0.025                                     #m\n",
+      "stress1=(2*stress*r)/D               #N/(m**2)\n",
+      "#d(uavg)/dr=urate=-(Vc/(n*R))*((1-(r/R))**((1-n)/n)) where uavg=average velocity\n",
+      "urate=-(Vc/(n*(D/2)))*((1-(r/(D/2)))**((1-n)/n))           #s**(-1)\n",
+      "stresslam=-(kvis*d*urate)                                          #N/(m**2)\n",
+      "stressratio=(stress1-stresslam)/stresslam\n",
+      "\n",
+      "#result\n",
+      "print \"The ratio of teh turbulent to laminar stress at a point\\nmidway between the centreline and the pipe wall =\",round(stressratio,0)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The thickness of the viscous sublayer= 0.02 mm\n",
+        "The reynolds number= 507267.0 hence the flow is turbulent.\n",
+        "The approximate centerline velocity= 6.04 m/s\n",
+        "The ratio of teh turbulent to laminar stress at a point\n",
+        "midway between the centreline and the pipe wall = 1219.0\n"
+       ]
+      }
+     ],
+     "prompt_number": 18
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.5 Page no.435"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.5\n",
+      "# find The pressure drop if the flow is laminar and turbulent.\n",
+      "\n",
+      "#result\n",
+      "D=4.0                         #mm\n",
+      "V=50.0                      #m/sec\n",
+      "l=0.1                      #m\n",
+      "d=1.23                   #kg/(m**3)\n",
+      "vis=1.79/100000    #N*sec/(m**2)\n",
+      "Re=d*V*(D*10**-3)/vis\n",
+      "#if flow is laminar\n",
+      "f=64/Re\n",
+      "pdiff=f*l*0.5*d*(V**2)/((D*10**-3)*1000)        #kPa\n",
+      "\n",
+      "#result\n",
+      "print \"The pressure drop if the flow is laminar=\",round(pdiff,3),\"kpa\"\n",
+      "\n",
+      "#if flow is turbulent\n",
+      "#roughness=0.0015 hence f=0.028\n",
+      "f1=0.028\n",
+      "pdiff1=f1*l*0.5*d*(V**2)/((D/1000)*1000)   #kPa\n",
+      "print \"The pressure drop if flow is turbulent=\",round(pdiff1,3),\"kpa\"\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The pressure drop if the flow is laminar= 0.179 kpa\n",
+        "The pressure drop if flow is turbulent= 1.076 kpa\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.6 Page no.446"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.6\n",
+      "#find the pressure drop.\n",
+      "#The horsepower supplied to the fluid by the fan.\n",
+      "\n",
+      "#Given\n",
+      "#Area and Velocity at location 1 to 9\n",
+      "A=[22,28,35,35,4,4,10,18,22]\n",
+      "V=[36.4,28.6,22.9,22.9,200,200,80,44.4,36.4]\n",
+      "g=32.2                        #ft/s**2  gravitational constant         \n",
+      "SG=0.0765                      #spesific gravity\n",
+      "hl=(0.2*(V[1]**2+V[2]**2+V[6]**2+V[7]**2)+0.6*V[5]**2+0.2*V[4]**2+4*V[3]**2)/(2*g)\n",
+      "pdiff=SG*hl/144                          #psi\n",
+      "Pa=SG*A[4]*V[4]*hl\n",
+      "\n",
+      "#result\n",
+      "print \"The value of (p1-p9)=\",round(pdiff,3),\"psi\"\n",
+      "print \"The horsepower supplied to the fluid by the fan=\",round(Pa,1)/550,\"hp\"\n",
+      "\n",
+      "#Plot\n",
+      "V=[50,100,150,200,250,300]\n",
+      "Pa=[0,10,25,62.3,120,220]\n",
+      "a=plot(V,Pa)\n",
+      "xlabel(\"q ft**3/s\") \n",
+      "ylabel(\"d (ft)\") \n",
+      "plt.xlim((0,300))\n",
+      "plt.ylim((0,250))\n",
+      "          \n",
+      "show(a)\n",
+      "\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The value of (p1-p9)= 0.297 psi\n",
+        "The horsepower supplied to the fluid by the fan= 62.272 hp\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEMCAYAAADal/HVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFBQiIuLW/wOZ1ZDRj1PfyAAAAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.7 Page no.449"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.7\n",
+      "#Find the duct size(a) for the square duct if the head loss per foot remains the same for the pipe and the duct.\n",
+      "\n",
+      "#given\n",
+      "T=120.0                                #degree F\n",
+      "D=8.0                                 #in\n",
+      "vavg=10.0                             #ft/s\n",
+      "roughness=0\n",
+      "kvis=1.89*10**-4                      #(ft**2)/s\n",
+      "Re=vavg*(D/12)/kvis\n",
+      "g=32.2                                #ft/s**2 gravitational constant\n",
+      "#Calculation\n",
+      "import math\n",
+      "#from this value of Re and roughness/D=0, and using Moody's chart\n",
+      "f=0.022\n",
+      "x=f*(vavg**2)/(D*2*32.2/12)            # x=hl/l\n",
+      "#Dh=4*A/P=4*(a**2)/(4*a)=a\n",
+      "y=(math.pi*((D/12)**2)*vavg)/(4)  # y=Vs/a**2 \n",
+      "z=x*2*g/y**2                            #z=f/a**5\n",
+      "f=0.023\n",
+      "a=(f/z)**(0.2)\n",
+      "\n",
+      "#result\n",
+      "print \"The duct size(a) for the square duct if the head loss per foot\\n remains the same for the pipe and the duct=\",round(a,3),\"ft\" "
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The duct size(a) for the square duct if the head loss per foot\n",
+        " remains the same for the pipe and the duct= 0.611 ft\n"
+       ]
+      }
+     ],
+     "prompt_number": 5
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.8 Page no.451"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.8\n",
+      "#Determine the pressure at point 1 if (a) all losses are neglected \n",
+      "#(b) major losses are included (c) all losses are included.\n",
+      "\n",
+      "#Given\n",
+      "T=60                          #degree F\n",
+      "D=0.0625                  #ft\n",
+      "Q=0.0267                 #(ft**3)/sec \n",
+      "Df=0.5                     #in\n",
+      "l1=15.0                    #ft\n",
+      "l2=10.0                     #ft\n",
+      "l3=5.0                      #ft\n",
+      "l4=10.0                   #ft\n",
+      "l5=10.0                   #ft\n",
+      "l6=10.0                   #ft\n",
+      "\n",
+      "#calculation\n",
+      "import math\n",
+      "V1=Q/(math.pi*(D**2)/4)            #ft/sec\n",
+      "V2=Q/(math.pi*((Df/12)**2)/4)    #ft/sec\n",
+      "d=1.94                                        #slugs/ft\n",
+      "vis=2.34*10**-5                         #lb*sec/(ft**2)\n",
+      "Re=d*V1*D/vis\n",
+      "print \"The reynolds number =\",round(Re,3),\"hence the flow is turbulent\"\n",
+      "\n",
+      "#applying energy equation between points 1 and 2\n",
+      "#when all head losses are excluded\n",
+      "p1=(d*32.2*(l2+l4))+(0.5*d*((V2**2)-(V1**2)))         #lb/(ft**2)\n",
+      "print \"a)The pressure at point 1 when all head losses are neglected=\",round(p1,3),\"lb/ft**2\"\n",
+      "\n",
+      "#if major losses are included\n",
+      "f=0.0215\n",
+      "hLmajor=f*(l1+l2+l3+l4+l5+l6)*(V1**2)/(D*2*32.2)\n",
+      "p11=p1+(d*32.2*hLmajor)           #lb/(ft**2)\n",
+      "\n",
+      "#result\n",
+      "print \"b)The pressure at point 1 when only major head losses are included=\",round(p11/144,1),\"psi\"\n",
+      "\n",
+      "#if major and minor losses are included\n",
+      "KLelbow=1.5\n",
+      "KLvalve=10\n",
+      "KLfaucet=2\n",
+      "hLminor=(KLvalve+(4*KLelbow)+KLfaucet)*(V1**2)/(2*32.2)\n",
+      "p12=p11+(d*32.2*hLminor)#lb/(ft**2)\n",
+      "print \"c)The pressure at point 1 when both major and minor head losses are included=\",round(p12/144,1),\"psi\"\n",
+      "H=(p1/(32.2*1.94))+(V1*V1/(2*32.2))#ft\n",
+      "\n",
+      "#Plot\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "D=[0,15,15,25,25,30,30,40,40,50,50,60]\n",
+      "P=[30.5,27.8,27.1,21.0,20.2,19.3,18.5,12.4,11.7,9.93,4.84,3.09]\n",
+      "xlabel(\"distance along pipe from point  ft\") \n",
+      "ylabel(\"P  hP\") \n",
+      "plt.xlim((-0.5,60))\n",
+      "plt.ylim((0,32))\n",
+      "a=plot(D,P)\n",
+      "plt.text(40,28, '-(a)no losses')\n",
+      "plt.text(40,25, '-(c)including all losses')\n",
+      "\n",
+      "\n",
+      "plt.text(0,30.5, '30.5')\n",
+      "plt.text(15,27.8, '27.8')\n",
+      "plt.text(15,27.1, '27.1')\n",
+      "plt.text(25,21.0, '21.0')\n",
+      "plt.text(25,20.2, '20.2')\n",
+      "plt.text(30,19.3, '19.3')\n",
+      "plt.text(30,18.5, '18.5')\n",
+      "plt.text(40,12.4, '12.4')\n",
+      "plt.text(40,11.7, '11.7')\n",
+      "plt.text(50,9.93, '9.93')\n",
+      "plt.text(50,4.84, '4.84')\n",
+      "plt.text(60,3.09, '3.09')\n",
+      "plt.text(0,22, 'pressure loss')\n",
+      "plt.grid()\n",
+      "\n",
+      "D=[0,15,25,30,40,50,60]\n",
+      "P=[10.7,10.7,6.37,6.37,2.07,2.07,2.07]\n",
+      "xlabel(\"distance along pipe from point  ft\") \n",
+      "ylabel(\"P  hP\") \n",
+      "plt.xlim((-0.5,60))\n",
+      "plt.ylim((0,32))\n",
+      "a=plot(D,P)\n",
+      "plt.text(0,10.7,  '10.7')\n",
+      "plt.text(15,10.7, '10.7')\n",
+      "plt.text(25,6.37, '6.37')\n",
+      "plt.text(30,6.37, '6.37')\n",
+      "plt.text(40,2.07, '2.07')\n",
+      "plt.text(50,2.07, '2.07')\n",
+      "plt.text(60,2.07, '2.07')\n",
+      "plt.text(5,5, 'Elevation and Kinetic Energy')\n",
+      "show(a)\n",
+      "# 2nd plot\n",
+      "import numpy as np\n",
+      "import matplotlib.pyplot as plt\n",
+      "\n",
+      "fig = plt.figure()\n",
+      "ax = fig.add_subplot(111)\n",
+      "\n",
+      "D1=[0,15,15,25,25,30,30,40,40,50,50,60,60]\n",
+      "P1=[71.6,67,63,60,58,57,55,53,50,45,31,28,26]\n",
+      "xlabel(\"distance along pipe from point 1 ft\") \n",
+      "ylabel(\"Elevetion to Energy line H ft\") \n",
+      "plt.xlim((0,60))\n",
+      "plt.ylim((0,80))\n",
+      "ax.annotate('slope due to pipe friction', xy=(8, 70), xytext=(15,75),\n",
+      "            arrowprops=dict(facecolor='black', shrink=0.000),\n",
+      "            )\n",
+      "ax.annotate('sharp drop due to component loss', xy=(15, 65), xytext=(20,65),\n",
+      "            arrowprops=dict(facecolor='black', shrink=0.000),\n",
+      "            )\n",
+      "ax.annotate('Energy line including all losses', xy=(35, 55), xytext=(40,53),\n",
+      "            arrowprops=dict(facecolor='black', shrink=0.000),\n",
+      "            )\n",
+      "\n",
+      "a1=plot(D1,P1)\n",
+      "\n",
+      "D1=[0,60]\n",
+      "P1=[26,26]\n",
+      "xlabel(\"distance along pipe from point 1 ft\") \n",
+      "ylabel(\"Elevetion to Energy line H ft\") \n",
+      "plt.xlim((0,60))\n",
+      "plt.ylim((0,80))\n",
+      "a1=plot(D1,P1)\n",
+      "\n",
+      "ax.annotate('Energy line with no losses', xy=(10, 25), xytext=(10,10),\n",
+      "            arrowprops=dict(facecolor='black', shrink=0.000),\n",
+      "            )\n",
+      "\n",
+      "show(a1)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The reynolds number = 45094.88 hence the flow is turbulent\n",
+        "a)The pressure at point 1 when all head losses are neglected= 1547.821 lb/ft**2\n",
+        "b)The pressure at point 1 when only major head losses are included= 21.3 psi\n",
+        "c)The pressure at point 1 when both major and minor head losses are included= 30.5 psi\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEICAYAAABI7RO5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhmEYplv+Hxp2XRumWCodAAAAAElFTkSuQmCC\n"
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEMCAYAAACP9hKLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrvgfIHgLBUAjJl4AAAAASUVORK5CYII=\n"
+      }
+     ],
+     "prompt_number": 1
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.9 Page no.453"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.9\n",
+      "# Determine the horsepower required to drive the system\n",
+      "\n",
+      "#Given\n",
+      "T=140.0                           #degree F\n",
+      "sw=53.7                           #lb/(ft**3)\n",
+      "vis=8*10**-5                    #lb*sec/(ft**2)\n",
+      "l=799                               #miles\n",
+      "D=4.0                              #ft\n",
+      "Q=117.0                          #(ft**3)/sec\n",
+      "V=9.31                            #ft/sec\n",
+      "#energy equation=> hp=hL=f*(l/D)*((V**2)/(2*g))\n",
+      "f=0.0125\n",
+      "hp=f*(l*5280/D)*((V**2)/(2*32.2))        #ft,  in book ,calculation  mistake\n",
+      "Pa=sw*Q*round(hp,2)/550                                  #hp\n",
+      "\n",
+      "#Result\n",
+      "print \"The horsepower required to drive the system=\",round(Pa,3),\"hp\"\n",
+      "\n",
+      "#Plot\n",
+      "import matplotlib.pyplot as plt\n",
+      "fig = plt.figure()\n",
+      "ax = fig.add_subplot(111)\n",
+      "\n",
+      "D=[2,3,4,5,6]\n",
+      "P=[3.2*10**6,0.8*10**6,0.202*10**6,0.15*10**6,0.1*10**6]\n",
+      "xlabel(\"D  ft\") \n",
+      "ylabel(\"P  hP\") \n",
+      "plt.xlim((0,6))\n",
+      "plt.ylim((0,4*10**6))\n",
+      "ax.plot([4], [0.202*10**6], 'o')\n",
+      "ax.annotate('(4ft,2.02*10**5 hp)', xy=(4,0.25*10**6))\n",
+      "a=plot(D,P)\n",
+      "show(a)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The horsepower required to drive the system= 202694.35 hp\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAEKCAYAAAAmfuNnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n"
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.10 Page no.454"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.10\n",
+      "#Find the flow rate.\n",
+      "#Given\n",
+      "D=4.0                        #in\n",
+      "l=20                           #ft\n",
+      "n=4.0                         #number of 90 degree elbows\n",
+      "h=0.2                        #in\n",
+      "T=100                      #degree F \n",
+      "\n",
+      "#calculation\n",
+      "import math\n",
+      "#energy equation between the inside of the dryer and the exit of the vent pipe\n",
+      "p1=(h/12)*62.4         #lb/(ft**2)\n",
+      "KLentrance=0.5\n",
+      "KLelbow=1.5\n",
+      "sw=0.0709              #lb/(ft**3)\n",
+      "f=0.022                  #assumption\n",
+      "#hence,\n",
+      "V=((p1/sw)*2*32.2/(1+(f*l/(D/12))+KLentrance+(n*KLelbow)))**0.5       #ft/sec\n",
+      "Q=V*(math.pi*((D/12)**2)/4)          #(ft**3)/sec\n",
+      "\n",
+      "#result\n",
+      "print \"The flowrate=\",round(Q,2),\"ft**3/s\"\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The flowrate= 0.9 ft**3/s\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.11 Page no.456"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.11\n",
+      "#What is the possible flowrate.\n",
+      "\n",
+      "#Given\n",
+      "Pa=50.0                    #hp\n",
+      "D=1.0                      #ft\n",
+      "l=300.0                    #ft\n",
+      "f=0.02                     #moody factor\n",
+      "z1=90.0                   #ft\n",
+      "g=32.2                     #ft/s**2, gravitational constant\n",
+      "sw=62.4                    #lb/ft**3, specific heat\n",
+      "import numpy\n",
+      "#calculation\n",
+      "from scipy.optimize import fsolve\n",
+      "#energy equation between the surface of the lake and the outlet of the pipe\n",
+      "#p1=V1=p2=z2=0  V2=V\n",
+      "a=f*l/(D*2*g)                       #a=hl/V**2\n",
+      "b=Pa*550/(sw*math.pi*(D**2)/4)    #b=ht/V\n",
+      "#from bernouli eq.  f=0.109*V**3-90*V+561\n",
+      "def f(V1):\n",
+      "    f=0.109*V1**3-90*V1+561\n",
+      "    return(f)\n",
+      "V1=fsolve(f,10)\n",
+      "def f1(V2):\n",
+      "    f1=0.109*V2**3-90*V2+561\n",
+      "    return(f)\n",
+      "V2=fsolve(f,20)\n",
+      "\n",
+      "Q1=(math.pi*(D**2)/4)*V1         #(ft**3)/sec\n",
+      "Q2=(math.pi*(D**2)/4)*V2        #(ft**3)/sec\n",
+      "print \"The possible flowrates are=\",round(Q1,1),\"ft**3/s\" \"and \",round(Q2,1),\"ft**3/s\"\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The possible flowrates are= 5.166 ft**3/sand  19.537 ft**3/s\n"
+       ]
+      }
+     ],
+     "prompt_number": 70
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.12 Page no.457"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.12\n",
+      "#What is the diameter of the pipe \n",
+      "\n",
+      "#Given\n",
+      "roughness=0.0005                #ft\n",
+      "Q=2.0                            #(ft**3)/sec\n",
+      "pd=0.5*144                      #lb/ft**2  where pd=pressure drop\n",
+      "l=100                           #ft\n",
+      "d=0.00238                      #slugs/(ft**3)\n",
+      "vis=3.74*(10**(-7))            #lb*sec/(ft**2)\n",
+      "\n",
+      "#calculation\n",
+      "x=Q/(math.pi/4)                #where x =V*(D**2)\n",
+      "#energy equation with z1=z2 and V1=V2\n",
+      "D=0.404*f**(1/5)\n",
+      "#Calculation\n",
+      "y=(l*d*(x**2)*0.5/(pd))       #where y=(D**5)/f\n",
+      "f=0.027                        #using reynolds number, roughness and moody's chart\n",
+      "D=((l*d*(x**2)*0.5/(pd))*f)**(0.2)\n",
+      "\n",
+      "#Result\n",
+      "print \"The diameter of the pipe should be =\",round(D,3),\"ft\"\n",
+      "\n",
+      "#Plot\n",
+      "q=[0.1,0.5,2,3]\n",
+      "d=[0.06,0.09,0.196,0.225]\n",
+      "a=plot(q,d)\n",
+      "xlabel(\"q ft**3/s\") \n",
+      "ylabel(\"d (ft)\") \n",
+      "plt.xlim((0,3))\n",
+      "plt.ylim((0,0.25))\n",
+      "          \n",
+      "show(a)\n",
+      "\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "The diameter of the pipe should be = 0.196 ft\n"
+       ]
+      },
+      {
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEMCAYAAADXiYGSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.13 Page no.458"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.13\n",
+      "#find the diameter of pipe.\n",
+      "#Given\n",
+      "T=60.0                       #degree F\n",
+      "kvis=1.28*(10**(-5))         #(ft**2)/sec\n",
+      "l=1700.0                     #ft\n",
+      "roughness=0.0005             #ft\n",
+      "Q=26.0                       #(ft**3)/sec\n",
+      "n=4.0                        #number of flanged 45 degree elbows\n",
+      "z1=44.0                      #ft\n",
+      "\n",
+      "#Calculation\n",
+      "#V=31.1/D*2                   #where V=Q/A\n",
+      "#f=0.0052*D**5-0.00135*D      #eleminating V\n",
+      "\n",
+      "#calculation\n",
+      "#assume f=0.052               #(moody chart) hit and trial method\n",
+      "from scipy.optimize import fsolve\n",
+      "def f(D):\n",
+      "    f=0.0052*D**5-0.00135*D-0.052\n",
+      "    return(f)\n",
+      "D=fsolve(f,1)\n",
+      "\n",
+      "#result\n",
+      "print \"Pipe Diameter is \",round(D,1),\"ft\"\n",
+      "\n",
+      "#Plot\n",
+      "l=[300,1000,1700,2000]\n",
+      "d=[1.2,1.45,1.63,1.72]\n",
+      "a=plot(l,d)\n",
+      "xlabel(\"l ft\") \n",
+      "ylabel(\"d (ft)\") \n",
+      "plt.xlim((0,2000))\n",
+      "plt.ylim((0,1.8))\n",
+      "show(a)\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "DPipe Diameter is  1.6 m\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAYoAAAEPCAYAAABcA4N7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x8554780>"
+       ]
+      }
+     ],
+     "prompt_number": 30
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.14 Page no.462"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.14\n",
+      "#Determine the floerate into and out of each reservoir.\n",
+      "\n",
+      "#Given\n",
+      "D=1              #ft\n",
+      "f=0.02\n",
+      "z1=100           #ft\n",
+      "z2=20            #ft\n",
+      "z3=0             #ft\n",
+      "l1=1000          #ft\n",
+      "l2=500           #ft\n",
+      "l3=400           #ft\n",
+      "#assuming fluid flows into B\n",
+      "#applying energy equation bwtween (1 and 3) and (1 and 2) and using the relation V1=V2+V3\n",
+      "\n",
+      "#Calculation\n",
+      "from scipy.optimize import fsolve\n",
+      "import math\n",
+      "#V1+V2=V3                      #from eq of continuity ,because diameter are same\n",
+      "#V1**2+0.4*V3**2=322          #from energy eq..........(1)\n",
+      "#0.5*V2**2+0.4*V3**2=64.4     for fluid flowing B to c.........(2)\n",
+      "# V1**2+0.5*V2**2=258          #...............(3)\n",
+      "#From b and  bernouli eq.  f=V**4-460*V**2+3748\n",
+      "def f(V2):\n",
+      "    f1=V2**4-460*V2**2+3748\n",
+      "    return(f1)\n",
+      "V2=fsolve(f,2)\n",
+      "V1=math.sqrt(258-0.5*V2**2)       #from eq 3\n",
+      "A=(math.pi/4*(D**2))              #  ft**2, area\n",
+      "Q1=V1*A\n",
+      "Q2=V2*A\n",
+      "Q3=Q1-Q2\n",
+      "print \"Flow out of A reservoir =\",round(Q1,1), \"(ft**3)/sec\"\n",
+      "print \"Flow  into B reservoir =\",round(Q2,2), \"(ft**3)/sec\"\n",
+      "print \"Flow  into C reservoir =\" ,round(Q3,1),\"(ft**3)/sec\""
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Flow out of A reservoir = 12.5 (ft**3)/sec\n",
+        "Flow  into B reservoir = 2.26 (ft**3)/sec\n",
+        "Flow  into C reservoir = 10.3 (ft**3)/sec\n"
+       ]
+      }
+     ],
+     "prompt_number": 12
+    },
+    {
+     "cell_type": "heading",
+     "level": 3,
+     "metadata": {},
+     "source": [
+      "Example 8.15 Page no.467"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "#Example 8.15\n",
+      "#Find the diameter of nozzle.\n",
+      "#Given\n",
+      "D=60.0                 #mm\n",
+      "pdiff=4                  #kPa\n",
+      "Q=0.003               #(m**3)/sec\n",
+      "d=789                 #kg/(m**3)\n",
+      "\n",
+      "#calculation\n",
+      "import math\n",
+      "vis=1.19*(10**(-3))   #N*sec/(m**2)\n",
+      "Re=d*4*Q/(math.pi*D*vis)\n",
+      "#assuming B=dia/D=0.577, where dia=diameter of nozzle, and obtaining Cn from Re as 0.972\n",
+      "Cn=0.972\n",
+      "B=0.577\n",
+      "dia=((4*Q/(Cn*math.pi))/((2*pdiff*1000/(d*(1-(B**4))))**0.5))**0.5\n",
+      "\n",
+      "#result\n",
+      "print \"Diameter of the nozzle=\",round(dia*1000,1),\"mm\"\n"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Diameter of the nozzle= 34.1 mm\n"
+       ]
+      }
+     ],
+     "prompt_number": 3
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
+\ No newline at end of file
author	Jovina Dsouza	2014-06-18 12:43:07 +0530
committer	Jovina Dsouza	2014-06-18 12:43:07 +0530
commit	206d0358703aa05d5d7315900fe1d054c2817ddc (patch)
tree	f2403e29f3aded0caf7a2434ea50dd507f6545e2 /Fundamentals_of_Fluid_Mechanics/Ch_8.ipynb
parent	c6f0d6aeb95beaf41e4b679e78bb42c4ffe45a40 (diff)
download	Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.tar.gz Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.tar.bz2 Python-Textbook-Companions-206d0358703aa05d5d7315900fe1d054c2817ddc.zip