{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lab 3: Computation of Rotating Unbalance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interactive iPython tools will not work without IPython.display and ipywidgets installed.\n", "Interactive iPython tools will not work without IPython.display and ipywidgets installed.\n" ] } ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import vibration_toolbox as vtb\n", "#from vibration_toolbox import sdof_cf\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import scipy.io as sio\n", "import math as math\n", "import scipy.linalg as la" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using Vibration Toolbox\n", "--------------------------------" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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/VFV7WbnwOiYVLKFKsfFdr+s5a/ZdWC36WEdr/Iih7Ov9Mbe+9xkn7nueaZb1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pAAAC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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "mat_contents=sio.loadmat('Case3-1.mat')\n", "Freq_domain = mat_contents['Freq_domain']\n", "Hf_chan_2 = mat_contents['Hf_chan_2']\n", "Fmin=500;\n", "Fmax=1000;\n", "[z,nf,a]=vtb.sdof_cf(Freq_domain,Hf_chan_2,Fmin,Fmax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Power Spectrum Density plot\n", "-------------------------------------\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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byZicuxDifOD8AYevlFI+IoT4GjAP+BAwYcA5Q7rjrq4uuru7AQgEArjdbtra2gDwer2E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\n9p6Q9Pv9pe5C0RlK8/x9ojz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DJwB/B5BSvglEhBCVtOomAZwKbO1zbBFwX/bx/RgfgiOB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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "%matplotlib inline\n", "mat_contents=sio.loadmat('Case3-5-Fn+10.mat')\n", "Freq_domain = mat_contents['Freq_domain']\n", "PSD_chan_2 = mat_contents['PSD_chan_2']\n", "\n", "A=20*np.log10(PSD_chan_2);\n", "plt.plot(Freq_domain,A)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Experimental Data Analysis\n", "--------------------------------" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 219.6875], dtype=float32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents=sio.loadmat('Case3-5-Fn+10.mat')\n", "Freq_domain = mat_contents['Freq_domain']\n", "PSD_chan_2 = mat_contents['PSD_chan_2']\n", "#Driving Freqency\n", "\n", "y=np.where(PSD_chan_2==PSD_chan_2.max())\n", "\n", "wr=(Freq_domain[y]) #in Hz\n", "[a,b]=np.where(A==max(A));\n", "#PSD_chan_2\n", "wr\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 343.37321191]])" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_contents=sio.loadmat('Case3-1')\n", "Freq_domain = mat_contents['Freq_domain']\n", "Hf_chan_2 = mat_contents['Hf_chan_2']\n", "HI=abs(Hf_chan_2);\n", "h=HI[a]#inertance in 1/kg\n", "h" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acceleration Response Plot\n", "---------------------------------" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "mat_contents=sio.loadmat('Case3-5-Fn+10')\n", "Time_domain = mat_contents['Time_domain']\n", "Time_chan_2 = mat_contents['Time_chan_2']\n", "plt.plot(Time_domain,(Time_chan_2*9.81))\n", "plt.title('Case 3-5-Fn+10: Acc vs Time')\n", "plt.xlabel('$T(sec)$')\n", "plt.ylabel('$Acc$')# Disp vs Time\n", "\n", "\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data Analysis\n", "---------------" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2.25356289e-05], dtype=float32)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Amax=max((Time_chan_2*9.81)-1);\n", "Xmax=(Amax)/(wr*2*math.pi)**2 #max displ\n", "Xmax" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.06112647], dtype=float32)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r=wr/nf #freq ratio\n", "r" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 6.56301310e-08]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "moe=Xmax/h #experimental rotating unbalance\n", "moe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Beam Properties\n", "-------------------" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4811651667" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l=21.75*0.0254;# length in meters\n", "h=0.5*0.0254;# height in meters\n", "w=1*0.0254;# width in meters\n", "rho=2700;# density in kg/cubicmeter\n", "V = l*w*h;# volume (m^3)\n", "m=rho*V\n", "m\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analytical rotating unbalance\n", "-----------------------------------" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 1.21395442e-06], dtype=float32)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "moeA=m*Xmax*math.sqrt((1-r**2)**2+(2*z*r)**2)/(r**2)\n", "moeA" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }