Skip to content

Instantly share code, notes, and snippets.

@selimb
Created October 17, 2014 02:34
Show Gist options
  • Save selimb/ac6bd0b8a11813dc1c05 to your computer and use it in GitHub Desktop.
Save selimb/ac6bd0b8a11813dc1c05 to your computer and use it in GitHub Desktop.
V2_260450544
{
"metadata": {
"name": "",
"signature": "sha256:0ce06ac17869ce94b9abf2f0f55037a6b134311c017964e006d989bb2664a3c3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Theoretical Results\n",
"===================\n",
"\n",
"First three natural frequencies\n",
"-------------------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from numpy import cos,sin,sinh,cosh\n",
"from scipy.optimize import fsolve"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Apparatus specifications\n",
"m = 0.051; l = 0.32; w = 0.015; \n",
"h = 0.004; rho = 2.7*10**3;\n",
"E = 6.9*10**10\n",
"I = (w*h**3)/12\n",
"A0 = w*h\n",
"#Set a to 20cm\n",
"a = 0.1"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Calculated constants\n",
"alpha = m/(rho*A0*l)\n",
"xi = a/l"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Must solve equation below iteratively for $\\lambda$\n",
"\n",
"$$f(\\lambda) = 2\\sin(\\lambda)\\sinh(\\lambda)-\\alpha\\lambda \\Big[\n",
"\\sin(\\lambda\\xi)\\sin(\\lambda\\cdot(1-\\xi))\\sinh(\\lambda) -\n",
"\\sinh(\\lambda\\xi)\\sinh(\\lambda\\cdot(1-\\xi))\\sin(\\lambda)\n",
"\\Big]$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#f(lambda)\n",
"def f(x):\n",
" f1 = 2*sin(x)*sinh(x)\n",
" f2 = sin(x*xi)*sin(x*(1-xi))*sinh(x)\n",
" f3 = sinh(x*xi)*sinh(x*(1-xi))*sin(x)\n",
" return f1-alpha*x*(f2-f3)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's get a feel for what the function looks like. \n",
"\n",
"This also allows us to guess roots!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lamb = np.arange(0,20,0.01) \n",
"plt.plot(lamb,f(lamb))\n",
"plt.axis([0,20,-100,100])\n",
"plt.plot([0,20],[0,0],'--k')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEACAYAAAC6d6FnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text": [
"<matplotlib.figure.Figure at 0x57c3c30>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Guess 3,6,9 from plot above.\n",
"lambdas = fsolve(f,[3,6,9])\n",
"print \"First three roots are:\"\n",
"print lambdas"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"First three roots are:\n",
"[ 2.51645529 5.51000928 9.3770376 ]\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do a sanity check as well to make sure that $f(root) = 0$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for root in lambdas:\n",
" print \"\"\"f(%.4f) = %f\n",
" \"\"\" % (root,np.abs(f(root)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"f(2.5165) = 0.000000\n",
" \n",
"f(5.5100) = 0.000000\n",
" \n",
"f(9.3770) = 0.000000\n",
" \n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_frequency(lamb):\n",
" lamb = np.array(lamb)\n",
" omega = lamb**2 * \\\n",
" np.sqrt(E*I/(rho*A0*l**4))\n",
" return omega/(2*np.pi)\n",
"\n",
"frequencies = get_frequency(lambdas)\n",
"print \"The first three natural frequencies are :\"\n",
"print frequencies"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The first three natural frequencies are :\n",
"[ 57.45272634 275.44625168 797.74395362]\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First three mode shapes\n",
"-----------------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_mode_shapes(lamb,quiet = False):\n",
" step = 0.005\n",
" x1 = np.arange(0,a,step)\n",
" x2 = np.arange(a,l+step,step)\n",
" x = np.hstack((x1,x2))\n",
" C = 1.0\n",
" lamb = np.array(lamb)\n",
" s = (sinh(lamb)*sin(lamb*(1-xi))) / \\\n",
" (sin(lamb)*sinh(lamb*(1-xi)))\n",
" s1= (sinh(lamb)*sin(lamb*xi)) / \\\n",
" (sin(lamb)*sinh(lamb*xi))\n",
" D = (C*sinh(lamb*xi)) / \\\n",
" (sinh(lamb*(1-xi)))\n",
" y1 = C * (sinh(lamb*x1/l)- \\\n",
" s*sin(lamb*x1/l))\n",
" y2 = D * (sinh(lamb*(1-x2/l)) - \\\n",
" s1*(sin(lamb*(1-x2/l))))\n",
" y = np.hstack((y1,y2))\n",
" index = np.argmax(np.abs(y))\n",
" y_max = y[index]\n",
" y_normed = y/y_max\n",
" freq = get_frequency(lamb)\n",
" if not quiet:\n",
" print \"For lambda = %f\" % lamb\n",
" print \"sigma = \",s\n",
" print \"sigma_1 = \",s1\n",
" print \"D = \",D\n",
" print \"Frequency [Hz] : %f\" % freq \n",
" print \"Frequency [rad/s] %f: \" % (freq*2*np.pi) \n",
" print \"Location of maximum displacement %f [cm]\" % (x[index]*100)\n",
" print \"\\n\"\n",
" return (x,y_normed)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"shapes = [get_mode_shapes(a_lamb) for a_lamb in lambdas]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"For lambda = 2.516455\n",
"sigma = 3.79935450968\n",
"sigma_1 = 8.55271616828\n",
"D = 0.318459086627\n",
"Frequency [Hz] : 57.452726\n",
"Frequency [rad/s] 360.986126: \n",
"Location of maximum displacement 15.000000 [cm]\n",
"\n",
"\n",
"For lambda = 5.510009\n",
"sigma = 4.82848241891\n",
"sigma_1 = -64.5907157029\n",
"D = 0.122676224475\n",
"Frequency [Hz] : 275.446252\n",
"Frequency [rad/s] 1730.679841: \n",
"Location of maximum displacement 22.500000 [cm]\n",
"\n",
"\n",
"For lambda = 9.377038\n",
"sigma = 63.9085399576\n",
"sigma_1 = 2779.0062469\n",
"D = 0.0296219379008\n",
"Frequency [Hz] : 797.743954\n",
"Frequency [rad/s] 5012.373088: \n",
"Location of maximum displacement 16.000000 [cm]\n",
"\n",
"\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = plt.figure()\n",
"ax = plt.axes()\n",
"def plot_th(an_ax,ls='-'):\n",
" lines = []\n",
" for a_shape in shapes:\n",
" lines.append(an_ax.plot(a_shape[0]*100,a_shape[1],'-'))\n",
" return lines\n",
"plot_th(ax)\n",
"ax.legend(('n = 1','n = 2','n = 3'),loc='best', fancybox=True, framealpha=0.5)\n",
"ax.plot([0,l],[0,0],'--k')\n",
"ax.set_title('Theoretical normalized mode shapes')\n",
"ax.grid()\n",
"plt.show()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nU7Wl+4jzAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xedac230>"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig.savefig('latex/pics/plot_three_theo')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fourth mode shape\n",
"-----------------"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fourth_lambda = fsolve(f,12)\n",
"print \"The fourth root is: %.2f\" % fourth_lambda"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"The fourth root is: 11.75\n"
]
}
],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fourth_shape = get_mode_shapes(fourth_lambda,quiet=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig_fourth = plt.figure()\n",
"ax_fourth = plt.axes()\n",
"ax_fourth.plot(*fourth_shape)\n",
"ax_fourth.legend(('n = 4',),loc='best', fancybox=True, framealpha=0.5)\n",
"ax_fourth.plot([0,l],[0,0],'--k')\n",
"ax_fourth.set_title('Fourth mode shape')\n",
"ax_fourth.grid()\n",
"plt.show()\n",
"fig_fourth.savefig('latex/pics/plot_four_theo')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDpnP/S8AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xedb59f0>"
]
}
],
"prompt_number": 64
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"-----\n",
"\n",
"Experimental Results\n",
"===================="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#We read from a file containing the theoretical data for \n",
"#testing purposes.\n",
"data = pd.read_csv('results_trial.csv',)\n",
"data.dropna(how=\"all\", inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print data.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Index([u'x_first', u'first', u'x_third', u'third'], dtype='object')\n"
]
}
],
"prompt_number": 115
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# print data['third']\n",
"# data['third'] = data['third'][::-1]\n",
"# print data['third']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Reduce by constant value\n",
"for col in ['first','third']:\n",
" base = data[col][0]\n",
" data[col] -= base"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 117
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Normalize data\n",
"for col in ['first','third']:\n",
" y_max = data[col][np.argmax(np.abs(data[col]))]\n",
" data[col] = data[col]/y_max"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Reduce by constant value\n",
"# x[1:] -= x[1:,0].reshape(2,1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# data['first'][11:13] = np.nan"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig_exp = plt.figure()\n",
"ax_exp = plt.axes()\n",
"ax_exp.plot(data['x_first'],data['first'],'-o')\n",
"ax_exp.plot(data['x_third'],data['third'],'-o')\n",
"ax_exp.legend(('n = 1','n = 3'),loc='best', fancybox=True, framealpha=0.5)\n",
"ax_exp.plot([0,l],[0,0],'--k')\n",
"ax_exp.set_title('Experimental normalized mode shapes')\n",
"ax_exp.grid()\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEKCAYAAAD+XoUoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x57df410>"
]
}
],
"prompt_number": 136
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Plot both\n",
"fig_both = plt.figure()\n",
"ax_both = plt.axes()\n",
"ax_both.plot(shapes[0][0]*100,shapes[0][1],'-')\n",
"ax_both.plot(shapes[1][0]*100,shapes[1][1],'-')\n",
"ax_both.plot(data['x_first'],data['first'],'-ob')\n",
"ax_both.plot(data['x_third'],data['third'],'-og')\n",
"ax_both.plot()\n",
"ax_both.legend(('n=1','n=2'),loc='best', fancybox=True, framealpha=0.5)\n",
"ax_both.set_title('Experimental (solid) and Theoretical (dashed) mode shapes')\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngUAgEAgEAoHA2vh/aVKrNumLWxAAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xead2350>"
]
}
],
"prompt_number": 140
}
],
"metadata": {}
}
]
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment