{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter13 - Fiber-optic sensors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 13.1: Page 327" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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fqDewpD7ge8A0YFNgb0mb1OnvWOBioKcPO5jV41RqNjKlnhOVtAHwAWAHYFLu\n/ABwFXBuRNzXYNjtgSMjYlp+/yWAiDimqr/PkM63bgv8NiJ+UWNcPidqlvlcqTXL50QHb25Qioi4\nF/j6CAdfE3io8P5h4M3FHiStCcwA3kZqRN1Smg2hkkqPOy6lUj+v1Ky+Tl4tmmkQjwe+lGOm8OFc\ns6b4XKlZc0pNokvoEWDtwvu1SWm0aBvgPEkAqwC7S3o5Ii6oHtnMmTMXvu7v76e/v3+Ui2vWeZxK\nrWhgYICBgYGyi9FWSv+d6EhJWgq4G9gVeBS4Htg7Iu6q0//pwIUR8csan/mcqNkQfK7UqvmcaBsc\nzpV0eTPdqkXEK8AhwCXAncBPI+KufGXvwaNfUrPe5it4zRZXWhKVtBzpTkVXAP2Fj1YCLo6IjVtY\nFidRs2FwKjVwEoVyk+jBwI3ARsCfCn8XkH7/aWZtyqnULCn9nKikT0fEiSWXwUnUbIScSnuXk2gb\nNKIAkjYj3XVo2Uq3iDirhdN3I2q2BObPT1fwHnusr+DtJW5E26ARlTQT2Bl4A+lh3LsDV0XEe1tY\nBjeiZqPAqbS3uBFtg6tzgfcCU4HHIuIAYEtgQrlFMrOR8LlS6zXt0Ij+IyLmA69IWhl4kkVvomBm\nHcR3O7Je0g6N6A2SJgKnkq7WvRm4ptwimdmSciq1XlD6OdEiSesCK0XErS2ers+Jmo0hnyvtTj4n\nWu5DubfJzxFd+AdMBPryazPrEk6l1q3KvGPRAA2exBIRu7SwLE6iZi3iVNo9nETb7HBuWdyImrWW\nf1faHdyIlptEtwUejojH8vsPAe8B7gdmRsSzLSyLG1GzEjiVdjY3ouVenftD4CUASW8FjgHOBJ7P\nn5lZl/O5Uut0ZSbRWyNiy/z6ZOCpiJhZ/VmLyuIkalYyp9LO4yRabhLtk7R0fj2V9Ei0iqVKKI+Z\nlcip1DpRmUn0K8AewNOkOxRtExELJG0InBERO7SwLE6iZm3EqbQzOImWmEQj4hvA54HTgR0jorLP\nKeBTZZXLzMrnVGqdwj9xwUnUrJ05lbYvJ9H2uHeumVldTqXWzpxEcRI16xROpe3FSdRJ1Mw6iFOp\ntRsnUZxEzTqRU2n5nESdRM2sQzmVWjtwEsVJ1KzTOZWWw0nUSdTMuoBTqZXFSRQnUbNuUkmlyy4L\np53mVDqWnESdRM2sy1RS6fTpKZWefLJTqY0dJ1GcRM26lVPp2HISdRI1sy7mVGpjzUkUJ1GzXuBU\nOvqcRJ0tvR97AAAPYklEQVREzaxHOJXaWHASxUnUrNc4lY4OJ1EnUTPrQU6lNlqcRHESNetlTqUj\n5yTqJGpmPa6SSvfYw6nUhs9JFCdRM0ucSofHSbQLkqikaZJmS7pH0hdrfL6PpFsl3SbpaklblFFO\nM2t/TqU2XB2dRCX1AXcDU4FHgBuAvSPirkI/2wN3RsTfJE0DZkbEdlXjcRI1s0U4lQ7NSbTzk+gU\n4N6IuD8iXgbOA2YUe4iIayPib/ntH4G1WlxGM+tATqXWjE5vRNcEHiq8fzh3q+dA4KIxLZGZdY2+\nPjjssNSYnnMO7Lor3Hdf2aWydrJU2QVYQk0fg5W0C/BhYIdan8+cOXPh6/7+fvr7+5ewaGbWLSqp\n9LvfhSlT4Kij4OMfh3GdHkOGaWBggIGBgbKL0VY6/ZzodqRznNPy+yOABRFxbFV/WwC/BKZFxL01\nxuNzombWlOK50lmzYL31yi5ReXxOtPMP594IbChpsqTxwF7ABcUeJK1DakD3rdWAmpkNR/Fc6ZQp\nPlfa6zo6iQJI2h04HugDZkXE0ZIOBoiIH0j6EfAu4ME8yMsRMaVqHE6iZjZsvZ5KnUS7oBEdDW5E\nzWyk5s9P50qPOab3zpW6EXUjCrgRNbMl14up1I1o558TNTNrCz5X2pucRHESNbPR1Sup1EnUSdTM\nbNQ5lfYOJ1GcRM1s7HRzKnUSdRI1MxtTTqXdzUkUJ1Eza41uS6VOok6iZmYt41TafZxEcRI1s9br\nhlTqJOokamZWCqfS7uAkipOomZWrU1Opk6iTqJlZ6ZxKO5eTKE6iZtY+OimVOok6iZqZtRWn0s7i\nJIqTqJm1p3ZPpU6iTqJmZm3LqbT9OYniJGpm7a8dU6mTqJOomVlHcCptT06iOImaWWcpptLTToN1\n1y2nHE6iTqJmZh2nmEq33daptExOojiJmlnnKjOVOok6iZqZdTSn0nI5ieIkambdodWp1EnUSdTM\nrGs4lbaekyhOombWfVqRSp1EnUTNzLqSU2lrOIniJGpm3W2sUqmTqJOomVnXcyodO06iOImaWe8Y\nzVTqJOokambWU5xKR5eTKE6iZtabljSVOok6iZqZ9Syn0iXnJIqTqJlZJZUut1x6XmkzqdRJ1EnU\nzMwYTKXTp6fnlZ5yilNpM5xEcRI1MytqNpU6iXZ4EpU0TdJsSfdI+mKdfk7Mn98qaatWl9HMrNM4\nlTavYxtRSX3A94BpwKbA3pI2qepnOrBBRGwIHAT8d8sL2mEGBgbKLkLbcF0Mcl0M6pW66OuDww6D\nP/wBzj4bpk6FOXPKLlX76dhGFJgC3BsR90fEy8B5wIyqft4JnAkQEX8EJkhatbXF7Cy9soFohuti\nkOtiUK/VhVNpY53ciK4JPFR4/3DuNlQ/a41xuczMuopTaX2d3Ig2eyVQ9UlvX0FkZjYC1anUOvjq\nXEnbATMjYlp+fwSwICKOLfTzfWAgIs7L72cDO0fEE1Xj6sxKMDMrWa9fnbtU2QVYAjcCG0qaDDwK\n7AXsXdXPBcAhwHm50Z1b3YCCFwIzMxuZjm1EI+IVSYcAlwB9wKyIuEvSwfnzH0TERZKmS7oXeBE4\noMQim5lZl+nYw7lmZmZl6+QLi0ZFMzds6EaS1pZ0haQ/S7pD0qdz91dLukzSXyRdKmlC2WVtFUl9\nkm6WdGF+35N1IWmCpPMl3SXpTklv7uG6OCKvI7dL+omkZXqlLiSdJukJSbcXutWd91xX9+Tt6W7l\nlLr1eroRbeaGDV3sZeCzEfEGYDvgk3nevwRcFhGvBy7P73vFocCdDF7B3at1cQJwUURsAmwBzKYH\n6yJfb/FRYOuI2Jx02ugD9E5dnE7aNhbVnHdJm5KuS9k0D3OKpJ5oX3piJhto5oYNXSkiHo+IW/Lr\nF4C7SL+rXXiDivz/38spYWtJWguYDvyIwZ9F9VxdSFoZ2CkiToN07UFE/I0erAvgedLO5qskLQW8\ninQRY0/URUT8AXiuqnO9eZ8BnBsRL0fE/cC9pO1r1+v1RrSZGzZ0vbzHvRXwR2DVwhXMTwC9coen\n7wKHA8V7sfRiXawLPCXpdEk3STpV0vL0YF1ExLPAfwEPkhrPuRFxGT1YFwX15n0N0vazome2pb3e\niPb8VVWSVgB+ARwaEfOKn+VH23R9HUl6B/BkRNzM4jfnAHqnLkhX7G8NnBIRW5Oual/kcGWv1IWk\n9YHPAJNJjcQKkvYt9tMrdVFLE/PeE/XS643oI8Dahfdrs+jeVFeTtDSpAT07In6dOz8habX8+erA\nk2WVr4XeArxT0hzgXOBtks6mN+viYeDhiLghvz+f1Kg+3oN18Sbgmoh4JiJeAX4JbE9v1kVFvXWi\nelu6Vu7W9Xq9EV14wwZJ40knxi8ouUwtIUnALODOiDi+8NEFwIfy6w8Bv64etttExJcjYu2IWJd0\n4cj/RsR+9GZdPA48JOn1udNU4M/AhfRYXZAuqNpO0nJ5fZlKuvCsF+uiot46cQHwAUnjJa0LbAhc\nX0L5Wq7nfycqaXfgeAZv2HB0yUVqCUk7AlcCtzF42OUI0oL/M2Ad4H7g/RExt4wylkHSzsDnI+Kd\nkl5ND9aFpC1JF1iNB/5KuklJH71ZF18gNRYLgJuAjwAr0gN1IelcYGdgFdL5z/8L/IY68y7py8CH\ngVdIp4cuKaHYLdfzjaiZmdlI9frhXDMzsxFzI2pmZjZCbkTNzMxGyI2omZnZCLkRNTMzGyE3omZm\nZiPkRrSHSXphGP3uLGn7UZz25OIjlhr0d4ak9+TXp47kKTuS9pd00kjK2WqSPpTvBDNUf0PWX7Ef\nSVvm30SPOkmvk/S7YQ5zlKRdx6I8IyVpxgiXr3dK+upYlMnanxvR3jacHwnvQro9XtPyky+W1ML7\nc0bERyPirhGOo1PsT7pP62jbivSUmrFwCHDGcAaIiCMj4vJm+h2l5agZ7yI9yqtp+XGKFwLvybfR\ntB7jRtQWIWlPSdflJ3hcllPGZOBg4LP5odU7SHptfnDz9fnvLXn4mZLOlnQVcKakSZKulPSn/Ddk\nmpX0vfxg38uA1xW6D0jaWtK4nFBvl3SbpEMLnx+fy3i7pG2bmb/cfYX85JLbJN0q6d25+26Srsll\n/1l+ogmS7pf0zTytG3O5LpV0r6SDC9M7PNfPrZJm5m6TlR54/UOlB6JfImlZSe8l3a/1x7l8y1aV\nfZs8nluATxS690n6dmE6B1UNtzTwNWCvXN73S9o2z9dNkq7W4G3+isOdKWlG4f2PJb2zxlf2XuB3\nuZ/9Jf0618UcSYdIOixP51pJE3N/xSMM2+Yy3JK/mxXyeC6QdDlwmaSJeby35vFsXqO8zU57fUn/\nk7+3KyVtlJffPYFv5zpat1Z/hbJ/X9J1wLH5RuzXAj3zIGoriAj/9egfMK9GtwmF1x8BvpNfHwl8\nrvDZT4Ad8ut1SPfgBZgJ3AAsk98vV3i9IXBDfj0ZuL3G9N8NXEp6msrqpOcZvjt/dgXpZujbAJcW\nhlmp8PkP8uudKuMnpbuThpi/Y4HjivVAut3Z74HlcrcvAl/Nr+cAB+fXx5Fun7h8Hubx3H23QnnG\nkRLLTnneXwa2yJ/9FNinOI9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MzLqIIhqJU+WTNAnojYjJuftoYGFEHF8Y5yhglYjozd2nA5dHxK+q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\nZtYEDqhmZmZN4IBqZmbWBP8ftXlamgMlDP4AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#plot\n", "lod=[0, 20, 40, 60, 80, 100, 160] #in micro meter\n", "slong=[1.0, 0.95, 0.92, 0.89, 0.86, 0.83, 0.80]\n", "lad=[0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100] #in micro meter\n", "slat=[0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\n", "add=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "sang=[0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, .12]\n", "from numpy import arange\n", "t=arange(0,201,20)\n", "s1=arange(1.0,0.7,-0.03)\n", "%matplotlib inline\n", "from matplotlib.pyplot import plot, subplot, title, xlabel, ylabel, show\n", "#subplot(131)\n", "plot(t,s1)\n", "title(\"Variation of Slong as a function of delta x (with deltay=fi and delta theta=fi) \")\n", "xlabel(\"Longitudinal displacement delta x (micro meter)\")\n", "ylabel(\"Slong (normalised)\")\n", "show()\n", "t1=arange(0,101,10)\n", "s2=arange(1,-0.1,-0.1)\n", "\n", "#subplot(132)\n", "plot(t1,s2)##\n", "title(\"Variation of Slat as a function of delta y (with deltax=fi and delta theta=fi) \")\n", "xlabel(\"Lateral displacement delta y (micro meter)\")\n", "ylabel(\"Slat (normalised)\")\n", "show()\n", "t2=arange(0,11,1)\n", "s3=arange(1.0,0.7,-0.03)\n", "#subplot(133)\n", "plot(t2,s3)##\n", "title(\"Variation of Sang as a function of delta theta (with deltax=fi and deltay=fi) \")\n", "xlabel(\"Angular displacement delta theta (deg)\")\n", "ylabel(\"Sang (normalised)\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 13.2: Page 332" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phase change = 106.60 rad/m°C\n" ] } ], "source": [ "from math import sqrt, pi\n", "#phase change\n", "#given data :\n", "n=1.45## index of core\n", "a=10**-5## in C**-1\n", "b=5.1*10**-7## in C**-1\n", "lamda=.633*10**-6## in m\n", "# formula:- (1/L)*(del_fi/del_T)=((2*PI)/lamda)[(n/L)*(del_L/del_T)+(del_n/del_T)]\n", "#let we assume a=del_n/del_T, b=(1/L)*(del_L/del_T), c=(1/L)*(del_fi/del_T)\n", "c=((2*pi)/lamda)*((n*b)+a)#\n", "print \"phase change = %0.2f rad/m°C\"%c" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 13.3: Page 335" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "phase shift, del_fi = 8.99e-05 rad\n" ] } ], "source": [ "#phase shift\n", "#given data :\n", "L=500## in m\n", "D=0.1##in m\n", "ohm=7.3*10**-5## in rad s**-1\n", "lamda=0.85*10**-6## in m\n", "c=3*10**8## in m/s\n", "del_fi=(2*pi*L*D*ohm)/(c*lamda)#\n", "print \"phase shift, del_fi = %0.2e rad\"%del_fi" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }