{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obliczanie i graficzne przedstawienie na wykresie dwustopniowego obiegu pompy ciepła"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# get rid of error messages\n",
    "import warnings;warnings.simplefilter('ignore')\n",
    "\n",
    "# set up modules\n",
    "%pylab inline\n",
    "rcParams['savefig.dpi'] = 120\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "import math\n",
    "from scipy.integrate import odeint\n",
    "\n",
    "import CoolProp\n",
    "import CoolProp.CoolProp as CP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Założenia projektowe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Celem zadania jest zaprojektowanie systemu pompy ciepła. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Q_k = 15 * 1e3 # [kW] - wydajność grzewcza projektowanej pompy ciepła"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Obliczenia obiegu pompy ciepła"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "T_0 = 3 + 273.15 # [K] - temperatura wrzenia czynnika chłodniczego\n",
    "T_k = 40 + 273.15 # [K] - temperatura skraplania czynnika chłodniczego\n",
    "dT_sup = 20 # [°C] - przegrzanie\n",
    "\n",
    "n_iz = 0.5 # współczynnik sprawności izentropowej sprężarki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# czynnik chłodniczy\n",
    "refrigerant = \"R22\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obliczanie punktów charakterystycznych obiegu pompy ciepła"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Ciśnienie parowania: 548418 [Pa]\n",
      "* Ciśnienie międzystopniowe: 917084 [Pa]\n",
      "* Ciśnienie skraplania: 1533580 [Pa]\n",
      "* Strumień masy czynnika chłodniczego: 0.078 [kg/s]\n",
      "* Wydajność chłodnicza parownika: 13.363 [kW]\n"
     ]
    }
   ],
   "source": [
    "p_0 = CP.PropsSI('P','Q',1,'T',T_0,refrigerant)\n",
    "p_k = CP.PropsSI('P','Q',1,'T',T_k,refrigerant)\n",
    "p_m = math.sqrt(p_0 * p_k)\n",
    "print (\"* Ciśnienie parowania: %.0f [Pa]\" % p_0)\n",
    "print (\"* Ciśnienie międzystopniowe: %.0f [Pa]\" % p_m)\n",
    "print (\"* Ciśnienie skraplania: %.0f [Pa]\" % p_k)\n",
    "\n",
    "\n",
    "T_m = CP.PropsSI('T','Q',0,'P',p_m,refrigerant)\n",
    "\n",
    "if dT_sup > 0: h1 = CP.PropsSI('H','P',p_0,'T',T_0+dT_sup,refrigerant)\n",
    "else: h1 = CP.PropsSI('H','Q',1,'T',T_0,refrigerant)\n",
    "s1 = CP.PropsSI('S','H',h1,'P',p_0,refrigerant)\n",
    "h2 = CP.PropsSI('H','S',s1,'P',p_m,refrigerant)\n",
    "h3 = CP.PropsSI('H','P',p_m,'T',T_m+dT_sup,refrigerant)\n",
    "s3 = CP.PropsSI('S','H',h3,'P',p_m,refrigerant)\n",
    "h4 = CP.PropsSI('H','S',s3,'P',p_k,refrigerant)\n",
    "h5 = CP.PropsSI('H','Q',0,'T',T_k,refrigerant)\n",
    "h6 = h5\n",
    "\n",
    "q_0 = h1 - h6\n",
    "q_k = h4 - h5\n",
    "\n",
    "# strumień masy czynnika chłodniczego\n",
    "m_0 = Q_k / q_k\n",
    "print (\"* Strumień masy czynnika chłodniczego: %.3f [kg/s]\" % m_0)\n",
    "\n",
    "Q_0 = m_0 * q_0\n",
    "print (\"* Wydajność chłodnicza parownika: %.3f [kW]\" % (Q_0/1e3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generowanie wykresu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Ciśnienie krytyczne R22: 4990000 [Pa]\n"
     ]
    }
   ],
   "source": [
    "steps = 60\n",
    "pres = ones(steps)\n",
    "satl = zeros(steps)\n",
    "satv = zeros(steps)\n",
    "isotherm = zeros(steps)\n",
    "\n",
    "p_min = p_0 - 50000\n",
    "p_crit = CP.PropsSI(refrigerant,'pcrit')\n",
    "p_step = (p_crit - p_min)/(steps-1)\n",
    "\n",
    "for i in range(0,steps):\n",
    "    pres[i] = p_min + i * p_step\n",
    "    satl[i] = CP.PropsSI('H','Q',0,'P',pres[i],refrigerant)\n",
    "    satv[i] = CP.PropsSI('H','Q',1,'P',pres[i],refrigerant)\n",
    "\n",
    "print (\"* Ciśnienie krytyczne %s: %.0f [Pa]\" % (refrigerant, p_crit))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ea6c4a8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OAQccwKeffkqlSpWKdN0LLriAkSNHAlChQgWmTJmyQ/898s2YMYNGjRqtPx80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\n/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lGtPfroo8yfPx+Ac889N3AaKT9Y/EtaW1kZnH02VFRAw4bJFj8N3BVMkiRJkpSFUsW/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\nTObP7+9YZjmL+gJjUfinea66K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea6ea58>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 526,
       "width": 767
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot results\n",
    "plt.figure()\n",
    "\n",
    "# grid and axes\n",
    "plt.axes([0.025, 0.025, 0.95, 0.95])\n",
    "plt.grid(which='major', axis='x', linewidth=0.25, linestyle='-', color='0.65')\n",
    "plt.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.65')\n",
    "plt.grid(which='major', axis='y', linewidth=0.25, linestyle='-', color='0.65')\n",
    "plt.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.65')\n",
    "\n",
    "# data\n",
    "plt.plot([h6,h1], [p_0,p_0], 'k-', linewidth=0.75)\n",
    "plt.plot([h1,h2], [p_0,p_m], 'k-', linewidth=0.75)\n",
    "plt.plot([h2,h3], [p_m,p_m], 'k-', linewidth=0.75)\n",
    "plt.plot([h3,h4], [p_m,p_k], 'k-', linewidth=0.75)\n",
    "plt.plot([h4,h5], [p_k,p_k], 'k-', linewidth=0.75)\n",
    "plt.plot([h5,h6], [p_k,p_0], 'k-', linewidth=0.75)\n",
    "\n",
    "plt.semilogy(satl, pres, 'r-', linewidth=0.75)\n",
    "plt.semilogy(satv, pres, 'r-', linewidth=0.75)\n",
    "\n",
    "# opis punktów\n",
    "plt.text(h1, p_0, \"1\", fontsize=12, horizontalalignment='left', verticalalignment='top')\n",
    "plt.text(h2, p_m, \"2\", fontsize=12, horizontalalignment='left', verticalalignment='bottom')\n",
    "plt.text(h3, p_m, \"3\", fontsize=12, horizontalalignment='right', verticalalignment='bottom')\n",
    "plt.text(h4, p_k, \"4\", fontsize=12, horizontalalignment='left', verticalalignment='bottom')\n",
    "plt.text(h5, p_k, \"5\", fontsize=12, horizontalalignment='right', verticalalignment='bottom')\n",
    "plt.text(h6, p_0, \"6\", fontsize=12, horizontalalignment='right', verticalalignment='top')\n",
    "\n",
    "\n",
    "plt.annotate(refrigerant, \n",
    "             xy=(satl[round(0.7 * steps)],pres[round(0.7 * steps)]), xytext=(satl[round(0.7 * steps)]-50000,pres[round(0.8 * steps)]), bbox=dict(boxstyle=\"square\", fc=\"w\"),\n",
    "             arrowprops=dict(facecolor='red', arrowstyle=\"->\"), horizontalalignment='center', \n",
    "             verticalalignment='top', fontsize=10)\n",
    "\n",
    "# legend\n",
    "plt.xlim(h6-50000,h4+30000)\n",
    "plt.ylim(p_0-200000,p_crit+200000)\n",
    "\n",
    "plt.xlabel('Entalpia [J/kg]',fontsize=9)\n",
    "plt.ylabel('Cisnienie [Pa]',fontsize=9)"
   ]
  }
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