134 lines
15 KiB
Plaintext
134 lines
15 KiB
Plaintext
|
{
|
||
|
|
"metadata": {
|
||
|
|
"name": "",
|
||
|
|
"signature": "sha256:665d4d927eafbb088c892eb5950f2a0975c691bdbb983ca96c13a6336ba30584"
|
||
|
|
},
|
||
|
|
"nbformat": 3,
|
||
|
|
"nbformat_minor": 0,
|
||
|
|
"worksheets": [
|
||
|
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"from timedomaineuler import *"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 1
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"f=85.785 #Frequency of oscillation\n",
|
||
|
|
"L=1. #Length of tube\n",
|
||
|
|
"gp=300 #Number of gridpoints\n",
|
||
|
|
"\n",
|
||
|
|
"nr_p_period=50 #Number of save per oscillation period\n",
|
||
|
|
"CFL=0.5; # CFL number\n",
|
||
|
|
"periods=100 #Number of periods to compute\n",
|
||
|
|
"\n",
|
||
|
|
"T=1/f\n",
|
||
|
|
"gc=cvar.gc #Reference!\n",
|
||
|
|
"dx=L/(gp-1); # One left and right gp, so\n",
|
||
|
|
"\n",
|
||
|
|
"gc.setfreq(f)\n",
|
||
|
|
"tube=TubeLF(L,gp)\n",
|
||
|
|
"dt=min(CFL*dx/gc.c0(),T/nr_p_period)\n",
|
||
|
|
"intsteps=int(floor(1./(gc.getfreq()*dt)/nr_p_period))\n",
|
||
|
|
"x=linspace(0,L,gp)\n",
|
||
|
|
"# Create tube instance\n",
|
||
|
|
"tube=TubeLF(L,gp)\n",
|
||
|
|
"# To create a ni"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 5
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"dt"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "pyout",
|
||
|
|
"prompt_number": 6,
|
||
|
|
"text": [
|
||
|
|
"4.873360558484559e-06"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 6
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"tube.DoIntegration(dt,1000)"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 7
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"sol=tube.getSol()"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"prompt_number": 8
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [
|
||
|
|
"plot(sol.u())"
|
||
|
|
],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "pyout",
|
||
|
|
"prompt_number": 9,
|
||
|
|
"text": [
|
||
|
|
"[<matplotlib.lines.Line2D at 0x7f3d19ffaa90>]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data",
|
||
|
|
"png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEACAYAAABCl1qQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu3y536Pgmmv8mk8iDWHTJr9P
|
||
|
|
"text": [
|
||
|
|
"<matplotlib.figure.Figure at 0x7f3d1b8e7d30>"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"prompt_number": 9
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"collapsed": false,
|
||
|
|
"input": [],
|
||
|
|
"language": "python",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|