{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "

OpenSees Examples Manual Examples for OpenSeesPy

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OpenSees Example 1b. Elastic Portal Frame -- Earthquake Ground Motion

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\n", "\n", "You can find the original Examples:
\n", "https://opensees.berkeley.edu/wiki/index.php/Examples_Manual
\n", "Original Examples by By Silvia Mazzoni & Frank McKenna, 2006, in Tcl
\n", "Converted to OpenSeesPy by SilviaMazzoni, 2020
\n", "

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Simulation Process

\n", "\n", "Each example script does the following:\n", "

A. Build the model

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    \n", "
  1. model dimensions and degrees-of-freedom
  2. \n", "
  3. nodal coordinates
  4. \n", "
  5. nodal constraints -- boundary conditions
  6. \n", "
  7. nodal masses
  8. \n", "
  9. elements and element connectivity
  10. \n", "
  11. recorders for output
  12. \n", "
\n", "

B. Define & apply gravity load

\n", "
    \n", "
  1. nodal or element load
  2. \n", "
  3. static-analysis parameters (tolerances & load increments)
  4. \n", "
  5. analyze
  6. \n", "
  7. hold gravity loads constant
  8. \n", "
  9. reset time to zero
  10. \n", "
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C. Define and apply lateral load

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\n", "
  • Time Series and Load Pattern (nodal loads for static analysis, support ground motion for earthquake)
  • \n", "
  • lateral-analysis parameters (tolerances and displacement/time increments)
  • \n", "Static Lateral-Load Analysis\n", "
  • define the displacement increments and displacement path
  • \n", "Dynamic Lateral-Load Analysis\n", "
  • define the input motion and all associated parameters, such as scaling and input type
  • \n", "
  • define analysis duration and time increment
  • \n", "
  • define damping
  • \n", "
  • analyze
  • \n", "

    \n", " \n", "Introductory Examples\n", "The objective of Example 1a and Example 1b is to give an overview of input-file format in OpenSees using simple scripts.\n", "These scripts do not take advantage of the Tcl scripting capabilities shown in the later examples. However, they do provide starting a place where the input file is similar to that of more familiar Finite-Element Analysis software. Subsequent examples should be used as the basis for user input files.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

    OpenSees Example 1b.
    \n", " 2D Elastic Portal Frame -- Earthquake Ground Motion

    \n", "\n", " Objectives of Example 1b \n", " - Two element types
    \n", " - Distributed element loads
    \n", " \n", "\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done!\n" ] } ], "source": [ "############################################################\n", "# EXAMPLE: \n", "# pyEx1b.Portal2D.EQ.tcl.py\n", "# for OpenSeesPy\n", "# --------------------------------------------------------#\n", "# by: Silvia Mazzoni, 2020\n", "# silviamazzoni@yahoo.com\n", "############################################################\n", "\n", "# configure Python workspace\n", "import openseespy.opensees as ops\n", "import eSEESminiPy\n", "import os\n", "import math\n", "import numpy as numpy\n", "import matplotlib.pyplot as plt\n", "ops.wipe()\n", "# --------------------------------------------------------------------------------------------------\n", "# Example 1. portal frame in 2D\n", "# dynamic earthquake analysis of Portal Frame, with gravity.\n", "# all units are in kip, inch, second\n", "# elasticBeamColumn ELEMENT\n", "# Silvia Mazzoni and Frank McKenna, 2006\n", "#\n", "# ^Y\n", "# or\n", "# 3_________(3)________4 __\n", "# or | |\n", "# or | |\n", "# or | |\n", "# (1) (2) LCol\n", "# or | |\n", "# or | |\n", "# or | |\n", "# =1= =2= _or_ -------->X\n", "# or----------LBeam------------|\n", "#\n", "\n", "# SET UP ----------------------------------------------------------------------------\n", "ops.wipe() # clear opensees model\n", "ops.model('basic','-ndm',2,'-ndf',3) # 2 dimensions, 3 dof per node\n", "if not os.path.exists('Data'):\n", " os.mkdir('Data')\n", "\n", "\n", "# define GEOMETRY -------------------------------------------------------------\n", "# nodal coordinates:\n", "ops.node(1,0,0) # node , X Y\n", "ops.node(2,504,0)\n", "ops.node(3,0,432)\n", "ops.node(4,504,432)\n", "\n", "# Single point constraints -- Boundary Conditions\n", "ops.fix(1,1,1,1) # node DX DY RZ\n", "ops.fix(2,1,1,1) # node DX DY RZ\n", "ops.fix(3,0,0,0)\n", "ops.fix(4,0,0,0)\n", "\n", "# nodal masses:\n", "ops.mass(3,5.18,0.,0.) # node , Mx My Mz, Mass=Weight/g.\n", "ops.mass(4,5.18,0.,0.)\n", "\n", "# Define ELEMENTS -------------------------------------------------------------\n", "# define geometric transformation: performs a linear geometric transformation of beam stiffness and resisting force from the basic system to the global-coordinate system\n", "ops.geomTransf('Linear',1) # associate a tag to transformation\n", "\n", "# connectivity: (make A very large, 10e6 times its actual value)\n", "ops.element('elasticBeamColumn',1,1,3,3600000000,4227,1080000,1) # element elasticBeamColumn eleTag iNode jNode A E Iz transfTag\n", "ops.element('elasticBeamColumn',2,2,4,3600000000,4227,1080000,1)\n", "ops.element('elasticBeamColumn',3,3,4,5760000000,4227,4423680,1)\n", "\n", "# Define RECORDERS -------------------------------------------------------------\n", "ops.recorder('Node','-file','Data/DFreeEx1bEQ.out','-time','-node',3,4,'-dof',1,2,3,'disp') # displacements of free nodes\n", "ops.recorder('Node','-file','Data/DBaseEx1bEQ.out','-time','-node',1,2,'-dof',1,2,3,'disp') # displacements of support nodes\n", "ops.recorder('Node','-file','Data/RBaseEx1bEQ.out','-time','-node',1,2,'-dof',1,2,3,'reaction') # support reaction\n", "ops.recorder('Element','-file','Data/FColEx1bEQ.out','-time','-ele',1,2,'globalForce') # element forces -- column\n", "ops.recorder('Element','-file','Data/FBeamEx1bEQ.out','-time','-ele',3,'globalForce') # element forces -- beam\n", "\n", "# define GRAVITY -------------------------------------------------------------\n", "ops.timeSeries('Linear',1) # timeSeries Linear 1;\n", "# define Load Pattern\n", "ops.pattern('Plain',1,1) # \n", "ops.eleLoad('-ele',3,'-type','-beamUniform',-7.94) # distributed superstructure-weight on beam\n", "\n", "ops.wipeAnalysis() # adding this to clear Analysis module \n", "ops.constraints('Plain') # how it handles boundary conditions\n", "ops.numberer('Plain') # renumber dofs to minimize band-width (optimization), if you want to\n", "ops.system('BandGeneral') # how to store and solve the system of equations in the analysis\n", "ops.test('NormDispIncr',1.0e-8,6) # determine if convergence has been achieved at the end of an iteration step\n", "ops.algorithm('Newton') # use Newtons solution algorithm: updates tangent stiffness at every iteration\n", "ops.integrator('LoadControl',0.1) # determine the next time step for an analysis, apply gravity in 10 steps\n", "ops.analysis('Static') # define type of analysis static or transient\n", "ops.analyze(10) # perform gravity analysis\n", "ops.loadConst('-time',0.0) # hold gravity constant and restart time\n", "\n", "# DYNAMIC ground-motion analysis -------------------------------------------------------------\n", "# create load pattern\n", "accelSeries = 900\n", "ops.timeSeries('Path',accelSeries,'-dt',0.01,'-filePath','BM68elc.acc','-factor',1) # define acceleration vector from file (dt=0.01 is associated with the input file gm)\n", "ops.pattern('UniformExcitation',2,1,'-accel',accelSeries) # define where and how (pattern tag, dof) acceleration is applied\n", "ops.rayleigh(0.,0.,0.,2*0.02/math.pow(ops.eigen('-fullGenLapack',1)[0],0.5)) # set damping based on first eigen mode\n", "\n", "# create the analysis\n", "ops.wipeAnalysis() # clear previously-define analysis parameters\n", "ops.wipeAnalysis() # adding this to clear Analysis module \n", "ops.constraints('Plain') # how it handles boundary conditions\n", "ops.numberer('Plain') # renumber dofs to minimize band-width (optimization), if you want to\n", "ops.system('BandGeneral') # how to store and solve the system of equations in the analysis\n", "ops.test('NormDispIncr',1.0e-8,10) # determine if convergence has been achieved at the end of an iteration step\n", "ops.algorithm('Newton') # use Newtons solution algorithm: updates tangent stiffness at every iteration\n", "ops.integrator('Newmark',0.5,0.25) # determine the next time step for an analysis\n", "ops.analysis('Transient') # define type of analysis: time-dependent\n", "ops.analyze(1000,0.02) # apply 1000 0.02-sec time steps in analysis\n", "\n", "\n", "print('Done!')\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "eSEESminiPy.drawModel()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot deformed shape at end of analysis (it may have returned to rest)\n", "# amplify the deformtions by 5\n", "eSEESminiPy.drawDeformedShape(5)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "End of Run: pyEx1b.Portal2D.EQ.tcl.py\n" ] }, { "data": { "image/png": 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\n", 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    " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ops.wipe() # the wipe command here closes all recorder files\n", "plt.close('all')\n", "fname3 = 'Data/DFreeEx1bEQ.out'\n", "dataDFree = numpy.loadtxt(fname3)\n", "plt.plot(dataDFree[:,0],dataDFree[:,1])\n", "plt.xlabel('Pseudo-Time (~Force)')\n", "plt.ylabel('Free-Node Disp.')\n", "plt.title('Ex1b.Portal2D.EQ.tcl')\n", "plt.grid(True)\n", "print('End of Run: pyEx1b.Portal2D.EQ.tcl.py')" ] } ], "metadata": { "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }