{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 211 オットーサイクル\n", "\n", "Example: Spark-Ignition. Four-Stroke Engine\n", "\n", "## 読み込み" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import math\n", "\n", "import sys\n", "sys.path.append(r'~\\Lib\\site-packages') # Path to library directory\n", "import CoolProp\n", "import CoolProp.CoolProp as CP \n", "from CoolProp.Plots import PropertyPlot\n", "\n", "import matplotlib.pyplot as plt \n", "%matplotlib inline\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## パラメータ定義" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "WF = 'air'\n", "K = 273.15\n", "bore = 0.1 # m\n", "stroke = 0.09 #m\n", "CR = 8.3 # Compression ratio\n", "N_cyl = 4 # number of cylinder\n", "N = 60 # engine speed 1/s\n", "AF = 16 # air-fuel ratio\n", "HC = 44*10**6 # heat of combusion J/kg\n", "T_amb = 32 + K # outdoor air temperature\n", "P_atm = 100*10**3 # atomospheric air temperature Pa\n", "\n", "Vol_dis_cyl = math.pi*bore**2 *stroke/4 # Displacement of each cylinder\n", "Vol_cl = Vol_dis_cyl/(CR-1) # clearance volume\n", "Vol_BDC = Vol_dis_cyl+Vol_cl #bottom dead center volume\n", "Vol_TDC = Vol_cl # top dead center volume" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### データフレーム用意" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index = list(range(1,4+1))\n", "states = pd.DataFrame(columns=['T', 'P', 's', 'v', 'Vol', 'u', 'm'], index=index)#K, Pa, J/kg/K, m3/kg, m3/mol?, J/kg, kg/mol" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Point 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "states.ix[1, 'T'] = T_amb\n", "states.ix[1, 'P'] = P_atm\n", "states.ix[1, 's'] = CP.PropsSI('S', 'T', states.ix[1, 'T'], 'P', states.ix[1, 'P'], WF)\n", "states.ix[1, 'u'] = CP.PropsSI('U', 'T', states.ix[1, 'T'], 'P', states.ix[1, 'P'], WF)\n", "states.ix[1, 'v'] = 1/CP.PropsSI('D', 'T', states.ix[1, 'T'], 'P', states.ix[1, 'P'], WF)\n", "states.ix[1, 'Vol'] = Vol_BDC\n", "states.ix[1, 'm'] = states.ix[1, 'Vol'] / states.ix[1, 'v']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Point 2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "states.ix[2, 'm'] = states.ix[1, 'm']\n", "states.ix[2, 'Vol'] = Vol_TDC\n", "states.ix[2, 'v'] = states.ix[2, 'Vol'] / states.ix[2, 'm']\n", "states.ix[2, 's'] = states.ix[1, 's']\n", "states.ix[2, 'u'] = CP.PropsSI('U', 'D', 1/states.ix[2, 'v'], 'S', states.ix[2, 's'], WF)\n", "states.ix[2, 'T'] = CP.PropsSI('T', 'D', 1/states.ix[2, 'v'], 'S', states.ix[2, 's'], WF)\n", "states.ix[2, 'P'] = CP.PropsSI('P', 'D', 1/states.ix[2, 'v'], 'S', states.ix[2, 's'], WF)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Work etc." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "m_in:8.07E-04\n", "m_f:4.75E-05\n" ] } ], "source": [ "W_comp = states.ix[2, 'm'] * states.ix[2, 'u'] - states.ix[1, 'm'] * states.ix[1, 'u'] #Compression work\n", "m_in = (Vol_BDC - Vol_TDC) / (1/CP.PropsSI('D', 'T', T_amb, 'P', P_atm, WF)) #mass of incoming air\n", "m_f = m_in / (AF+1) # mass of fuel\n", "print('m_in:{:.02E}'.format(m_in))\n", "print('m_f:{:.02E}'.format(m_f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## State 3" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "states.ix[3, 'm'] = states.ix[2, 'm']\n", "states.ix[3, 'Vol'] = Vol_TDC\n", "states.ix[3, 'v'] = states.ix[3, 'Vol'] / states.ix[3, 'm']\n", "states.ix[3, 'u'] = (m_f * HC - states.ix[2, 'm'] * states.ix[2, 'u']) / states.ix[3, 'm'] # from Energy balance\n", "states.ix[3, 'T'] = CP.PropsSI('T', 'U', states.ix[3, 'u'], 'D', 1/states.ix[3, 'v'], WF)\n", "states.ix[3, 'P'] = CP.PropsSI('P', 'U', states.ix[3, 'u'], 'D', 1/states.ix[3, 'v'], WF)\n", "states.ix[3, 's'] = CP.PropsSI('S', 'U', states.ix[3, 'u'], 'D', 1/states.ix[3, 'v'], WF)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## State 4" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def find_P_from_v_s(WF, v, s, Pstart, Pstop, eps=1e-6):\n", " v1 = 1/CP.PropsSI('D','P',Pstart,'S', s, WF)\n", " v2 = 1/CP.PropsSI('D','P',Pstop,'S', s, WF)\n", " while abs(v2-v1)/v > eps:\n", " Pm = (Pstart+Pstop)/2.0\n", " vm = 1/CP.PropsSI('D','P',Pm,'S',s,WF)\n", " if (vm-v)*(v2-v) < 0: Pstart,v1 = Pm,vm\n", " else: Pstop,v2 = Pm,vm\n", " return Pm\n", "\n", "states.ix[4, 'm'] = states.ix[3, 'm']\n", "states.ix[4, 'Vol'] = Vol_BDC\n", "states.ix[4, 'v'] = states.ix[4, 'Vol'] / states.ix[4, 'm']\n", "states.ix[4, 's'] = states.ix[3, 's']\n", "\n", "#states.ix[4, 'P'] = CP.PropsSI('P', 'D', 1/states.ix[4, 'v'], 'S', 1/states.ix[4, 's'], WF)\n", "states.ix[4, 'P'] = find_P_from_v_s(WF, states.ix[4, 'v'], states.ix[4, 's'], states.ix[1, 'P'], states.ix[3, 'P'])\n", "\n", "#states.ix[4, 'T'] = CP.PropsSI('T', 'D', 1/states.ix[4, 'v'], 'S', 1/states.ix[4, 's'], WF)\n", "states.ix[4, 'T'] = CP.PropsSI('T', 'D', 1/states.ix[4, 'v'], 'P', states.ix[4, 'P'], WF)\n", "\n", "#states.ix[4, 'u'] = CP.PropsSI('U', 'D', 1/states.ix[4, 'v'], 'S', 1/states.ix[4, 's'], WF)\n", "states.ix[4, 'u'] = states.ix[3, 'u'] - states.ix[2, 'u']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 結果テーブルの表示" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "W_net -848.25 = W_exp -581.94 - W_comp 266.31\n", "eta: -0.41\n" ] }, { "data": { "text/html": [ "
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TPsvVolum
1305.151000003907.630.8756980.0008036883439140.000917768
2695.5021.90557e+063907.630.1055069.68299e-056340850.000917768
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4929.613050234754.130.8756980.0008036881.00823e+060.000917768
\n", "
" ], "text/plain": [ " T P s v Vol u \\\n", "1 305.15 100000 3907.63 0.875698 0.000803688 343914 \n", "2 695.502 1.90557e+06 3907.63 0.105506 9.68299e-05 634085 \n", "3 1831.19 5.02999e+06 4754.13 0.105506 9.68299e-05 1.64231e+06 \n", "4 929.61 305023 4754.13 0.875698 0.000803688 1.00823e+06 \n", "\n", " m \n", "1 0.000917768 \n", "2 0.000917768 \n", "3 0.000917768 \n", "4 0.000917768 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "W_exp = states.ix[4, 'm'] * states.ix[4, 'u'] - states.ix[3, 'm'] * states.ix[3, 'u'] # expansion work\n", "W_net =W_exp - W_comp # net work per power stroke\n", "eta = W_net / (m_f * HC)\n", "print('W_net {:.02f} = W_exp {:.02f} - W_comp {:.02f}'.format(W_net, W_exp, W_comp, ))\n", "print('eta: {:.02f}'.format(eta))\n", "display(states)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## PVグラフ" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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ft0Pu/VdrS0cFF06y0+KYkNuD55btpKEx4EdjO53X4+XXk35NbkoudxTewcoD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\nufvSPO6YNoRF6/bz3LKdzFm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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_points = 1000\n", "P12 = np.linspace(states.ix[1, 'P'], states.ix[2, 'P'], nb_points)\n", "P23 = np.linspace(states.ix[2, 'P'], states.ix[3, 'P'], nb_points)\n", "P34 = np.linspace(states.ix[3, 'P'], states.ix[4, 'P'], nb_points)\n", "P41 = np.linspace(states.ix[4, 'P'], states.ix[1, 'P'], nb_points)\n", "\n", "v12 = 1/CP.PropsSI('D', 'P', P12, 'S', states.ix[1, 's'], WF)\n", "v23 = np.linspace(states.ix[2, 'v'], states.ix[3, 'v'], nb_points)\n", "v34 = 1/CP.PropsSI('D', 'P', P34, 'S', states.ix[3, 's'], WF)\n", "v41 = np.linspace(states.ix[4, 'v'], states.ix[1, 'v'], nb_points)\n", "L_P = [P12, P23, P34, P41]\n", "L_v = [v12, v23, v34, v41]\n", "\n", "for i in range(len(L_v)):\n", " plt.plot(L_v[i]*1e3,L_P[i]/1e5,label='{}$\\\\to${}'.format(i+1,(i+1)%5+1)) \n", " \n", "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)\n", "plt.xlabel('Volume $v$ m$^3/$kg')\n", "plt.ylabel('Pressure bar (=x$10^2$ kPa)')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tsグラフ" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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aD7Xna40xda5aSVdVVUTex0mGqOoX/u5IRCKB53EG2cgCMkQkTVU3+VS7AchW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\nBwc7jvrKzk/V7PxUzc6PqUt2edkYY4w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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nb_points = 1000\n", "T12 = np.linspace(states.ix[1, 'T'], states.ix[2, 'T'], nb_points)\n", "T23 = np.linspace(states.ix[2, 'T'], states.ix[3, 'T'], nb_points)\n", "T34 = np.linspace(states.ix[3, 'T'], states.ix[4, 'T'], nb_points)\n", "T41 = np.linspace(states.ix[4, 'T'], states.ix[1, 'T'], nb_points)\n", "\n", "s12 = np.linspace(states.ix[1, 's'], states.ix[2, 's'], nb_points)\n", "s23 = CP.PropsSI('S', 'T', T23, 'D', 1/states.ix[2, 'v'], WF)\n", "s34 = np.linspace(states.ix[3, 's'], states.ix[4, 's'], nb_points)\n", "s41 = CP.PropsSI('S', 'T', T41, 'D', 1/states.ix[1, 'v'], WF)\n", "\n", "L_T = [T12, T23, T34, T41]\n", "L_s = [s12, s23, s34, s41]\n", "\n", "for i in range(len(L_v)):\n", " plt.plot(L_s[i]*1e3,L_T[i]/1e5,label='{}$\\\\to${}'.format(i+1,(i+1)%5+1))\n", "\n", "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)\n", "plt.xlabel('Entropy $s$ J/kg/K')\n", "plt.ylabel('Temperature $T$ K')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py3.6]", "language": "python", "name": "conda-env-py3.6-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }