in-development/Properties of Steam.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Properties of Steam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from iapws import IAPWS97 as water\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "910.52754086\n"
     ]
    }
   ],
   "source": [
    "h1 =  water(P=1.0,T = 50.0+273.15).h\n",
    "h2 = water(P=10.0,T = 50.0+273.15).h\n",
    "v = water(P=1.0,T = 50.0+273.15).v\n",
    "p2 = 1000000.0\n",
    "p1 = 100000.0\n",
    "print v*(p2-p1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IAPWS97"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standard Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Tcen = np.array([50.0, 100.0, 150.0, 200.0, 250.0, 300.0, 350.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Pbar = np.array([0.1, 1.0, 5.0, 10.0, 20.0, 40.0, 60.0, 100.0, 150.0, 200.0, 220.64])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specific Enthalpy [kJ/kg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>50.0</th>\n",
       "      <th>100.0</th>\n",
       "      <th>150.0</th>\n",
       "      <th>200.0</th>\n",
       "      <th>250.0</th>\n",
       "      <th>300.0</th>\n",
       "      <th>350.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>2591.993619</td>\n",
       "      <td>2687.430931</td>\n",
       "      <td>2783.020096</td>\n",
       "      <td>2879.590157</td>\n",
       "      <td>2977.445660</td>\n",
       "      <td>3076.734320</td>\n",
       "      <td>3177.543459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>209.411799</td>\n",
       "      <td>2675.767367</td>\n",
       "      <td>2776.591815</td>\n",
       "      <td>2875.475065</td>\n",
       "      <td>2974.537058</td>\n",
       "      <td>3074.540384</td>\n",
       "      <td>3175.817394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>209.756771</td>\n",
       "      <td>419.398530</td>\n",
       "      <td>632.266303</td>\n",
       "      <td>2855.896196</td>\n",
       "      <td>2961.129843</td>\n",
       "      <td>3064.596220</td>\n",
       "      <td>3168.061200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>210.187911</td>\n",
       "      <td>419.774152</td>\n",
       "      <td>632.574920</td>\n",
       "      <td>2828.267538</td>\n",
       "      <td>2943.222165</td>\n",
       "      <td>3051.703186</td>\n",
       "      <td>3158.163294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.00</th>\n",
       "      <td>211.049937</td>\n",
       "      <td>420.525628</td>\n",
       "      <td>633.193130</td>\n",
       "      <td>852.572484</td>\n",
       "      <td>2903.231389</td>\n",
       "      <td>3024.251876</td>\n",
       "      <td>3137.641215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.00</th>\n",
       "      <td>212.772979</td>\n",
       "      <td>422.029495</td>\n",
       "      <td>634.433388</td>\n",
       "      <td>853.387444</td>\n",
       "      <td>1085.686080</td>\n",
       "      <td>2961.651480</td>\n",
       "      <td>3093.318226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.00</th>\n",
       "      <td>214.494670</td>\n",
       "      <td>423.534545</td>\n",
       "      <td>635.678641</td>\n",
       "      <td>854.217021</td>\n",
       "      <td>1085.650088</td>\n",
       "      <td>2885.490458</td>\n",
       "      <td>3043.858380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>217.933999</td>\n",
       "      <td>426.548024</td>\n",
       "      <td>638.183552</td>\n",
       "      <td>855.917879</td>\n",
       "      <td>1085.717160</td>\n",
       "      <td>1343.096609</td>\n",
       "      <td>2923.957847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.00</th>\n",
       "      <td>222.225554</td>\n",
       "      <td>430.320796</td>\n",
       "      <td>641.340324</td>\n",
       "      <td>858.117093</td>\n",
       "      <td>1086.035596</td>\n",
       "      <td>1338.063261</td>\n",
       "      <td>2692.999815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.00</th>\n",
       "      <td>226.508658</td>\n",
       "      <td>434.099605</td>\n",
       "      <td>644.523777</td>\n",
       "      <td>860.391147</td>\n",
       "      <td>1086.583565</td>\n",
       "      <td>1334.139525</td>\n",
       "      <td>1645.951051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220.64</th>\n",
       "      <td>228.274262</td>\n",
       "      <td>435.661130</td>\n",
       "      <td>645.845268</td>\n",
       "      <td>861.350248</td>\n",
       "      <td>1086.870071</td>\n",
       "      <td>1332.778346</td>\n",
       "      <td>1635.608804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                50           100          150          200          250  \\\n",
       "0.10    2591.993619  2687.430931  2783.020096  2879.590157  2977.445660   \n",
       "1.00     209.411799  2675.767367  2776.591815  2875.475065  2974.537058   \n",
       "5.00     209.756771   419.398530   632.266303  2855.896196  2961.129843   \n",
       "10.00    210.187911   419.774152   632.574920  2828.267538  2943.222165   \n",
       "20.00    211.049937   420.525628   633.193130   852.572484  2903.231389   \n",
       "40.00    212.772979   422.029495   634.433388   853.387444  1085.686080   \n",
       "60.00    214.494670   423.534545   635.678641   854.217021  1085.650088   \n",
       "100.00   217.933999   426.548024   638.183552   855.917879  1085.717160   \n",
       "150.00   222.225554   430.320796   641.340324   858.117093  1086.035596   \n",
       "200.00   226.508658   434.099605   644.523777   860.391147  1086.583565   \n",
       "220.64   228.274262   435.661130   645.845268   861.350248  1086.870071   \n",
       "\n",
       "                300          350  \n",
       "0.10    3076.734320  3177.543459  \n",
       "1.00    3074.540384  3175.817394  \n",
       "5.00    3064.596220  3168.061200  \n",
       "10.00   3051.703186  3158.163294  \n",
       "20.00   3024.251876  3137.641215  \n",
       "40.00   2961.651480  3093.318226  \n",
       "60.00   2885.490458  3043.858380  \n",
       "100.00  1343.096609  2923.957847  \n",
       "150.00  1338.063261  2692.999815  \n",
       "200.00  1334.139525  1645.951051  \n",
       "220.64  1332.778346  1635.608804  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "H = pd.DataFrame([[water(P = P/10.0, T=T + 273.15).h for T in Tcen] \\\n",
    "                  for P in Pbar],index=Pbar,columns=Tcen)\n",
    "display(H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specific Internal Energy [kJ/kg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>50.0</th>\n",
       "      <th>100.0</th>\n",
       "      <th>150.0</th>\n",
       "      <th>200.0</th>\n",
       "      <th>250.0</th>\n",
       "      <th>300.0</th>\n",
       "      <th>350.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>2443.319834</td>\n",
       "      <td>2515.463685</td>\n",
       "      <td>2587.884391</td>\n",
       "      <td>2661.330209</td>\n",
       "      <td>2736.080752</td>\n",
       "      <td>2812.274211</td>\n",
       "      <td>2889.993725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>209.310589</td>\n",
       "      <td>2506.171426</td>\n",
       "      <td>2582.919154</td>\n",
       "      <td>2658.225581</td>\n",
       "      <td>2733.917933</td>\n",
       "      <td>2810.653601</td>\n",
       "      <td>2888.720170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>209.250811</td>\n",
       "      <td>418.876904</td>\n",
       "      <td>631.721059</td>\n",
       "      <td>2643.379363</td>\n",
       "      <td>2723.915454</td>\n",
       "      <td>2803.294836</td>\n",
       "      <td>2882.992295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>209.176213</td>\n",
       "      <td>418.731154</td>\n",
       "      <td>631.484769</td>\n",
       "      <td>2622.263888</td>\n",
       "      <td>2710.483232</td>\n",
       "      <td>2793.723996</td>\n",
       "      <td>2875.671118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.00</th>\n",
       "      <td>209.027430</td>\n",
       "      <td>418.440648</td>\n",
       "      <td>631.014173</td>\n",
       "      <td>850.260364</td>\n",
       "      <td>2680.263374</td>\n",
       "      <td>2773.249520</td>\n",
       "      <td>2860.452349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.00</th>\n",
       "      <td>208.731498</td>\n",
       "      <td>417.863575</td>\n",
       "      <td>630.080813</td>\n",
       "      <td>848.771284</td>\n",
       "      <td>1080.679309</td>\n",
       "      <td>2726.179635</td>\n",
       "      <td>2827.422101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.00</th>\n",
       "      <td>208.437719</td>\n",
       "      <td>417.291678</td>\n",
       "      <td>629.157699</td>\n",
       "      <td>847.304698</td>\n",
       "      <td>1078.161612</td>\n",
       "      <td>2668.343652</td>\n",
       "      <td>2790.337532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>207.856493</td>\n",
       "      <td>416.163070</td>\n",
       "      <td>627.341314</td>\n",
       "      <td>844.436118</td>\n",
       "      <td>1073.305609</td>\n",
       "      <td>1329.116257</td>\n",
       "      <td>2699.535833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.00</th>\n",
       "      <td>207.141521</td>\n",
       "      <td>414.779933</td>\n",
       "      <td>625.124609</td>\n",
       "      <td>840.964658</td>\n",
       "      <td>1067.540461</td>\n",
       "      <td>1317.389383</td>\n",
       "      <td>2520.789278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.00</th>\n",
       "      <td>206.438968</td>\n",
       "      <td>413.426345</td>\n",
       "      <td>622.964795</td>\n",
       "      <td>837.611432</td>\n",
       "      <td>1062.076387</td>\n",
       "      <td>1306.917760</td>\n",
       "      <td>1612.653716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220.64</th>\n",
       "      <td>206.152473</td>\n",
       "      <td>412.875928</td>\n",
       "      <td>622.089145</td>\n",
       "      <td>836.259715</td>\n",
       "      <td>1059.900566</td>\n",
       "      <td>1302.890485</td>\n",
       "      <td>1599.556222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                50           100          150          200          250  \\\n",
       "0.10    2443.319834  2515.463685  2587.884391  2661.330209  2736.080752   \n",
       "1.00     209.310589  2506.171426  2582.919154  2658.225581  2733.917933   \n",
       "5.00     209.250811   418.876904   631.721059  2643.379363  2723.915454   \n",
       "10.00    209.176213   418.731154   631.484769  2622.263888  2710.483232   \n",
       "20.00    209.027430   418.440648   631.014173   850.260364  2680.263374   \n",
       "40.00    208.731498   417.863575   630.080813   848.771284  1080.679309   \n",
       "60.00    208.437719   417.291678   629.157699   847.304698  1078.161612   \n",
       "100.00   207.856493   416.163070   627.341314   844.436118  1073.305609   \n",
       "150.00   207.141521   414.779933   625.124609   840.964658  1067.540461   \n",
       "200.00   206.438968   413.426345   622.964795   837.611432  1062.076387   \n",
       "220.64   206.152473   412.875928   622.089145   836.259715  1059.900566   \n",
       "\n",
       "                300          350  \n",
       "0.10    2812.274211  2889.993725  \n",
       "1.00    2810.653601  2888.720170  \n",
       "5.00    2803.294836  2882.992295  \n",
       "10.00   2793.723996  2875.671118  \n",
       "20.00   2773.249520  2860.452349  \n",
       "40.00   2726.179635  2827.422101  \n",
       "60.00   2668.343652  2790.337532  \n",
       "100.00  1329.116257  2699.535833  \n",
       "150.00  1317.389383  2520.789278  \n",
       "200.00  1306.917760  1612.653716  \n",
       "220.64  1302.890485  1599.556222  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U = pd.DataFrame([[water(P = P/10.0, T=T + 273.15).u for T in Tcen] \\\n",
    "                  for P in Pbar],index=Pbar,columns=Tcen)\n",
    "U"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specific Volume [m3/kg]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>50.0</th>\n",
       "      <th>100.0</th>\n",
       "      <th>150.0</th>\n",
       "      <th>200.0</th>\n",
       "      <th>250.0</th>\n",
       "      <th>300.0</th>\n",
       "      <th>350.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>14.867378</td>\n",
       "      <td>17.196725</td>\n",
       "      <td>19.513570</td>\n",
       "      <td>21.825995</td>\n",
       "      <td>24.136491</td>\n",
       "      <td>26.446011</td>\n",
       "      <td>28.754973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.001012</td>\n",
       "      <td>1.695959</td>\n",
       "      <td>1.936727</td>\n",
       "      <td>2.172495</td>\n",
       "      <td>2.406191</td>\n",
       "      <td>2.638868</td>\n",
       "      <td>2.870972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.425034</td>\n",
       "      <td>0.474429</td>\n",
       "      <td>0.522603</td>\n",
       "      <td>0.570138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>0.001012</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>0.206004</td>\n",
       "      <td>0.232739</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>0.282492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.00</th>\n",
       "      <td>0.001011</td>\n",
       "      <td>0.001042</td>\n",
       "      <td>0.001089</td>\n",
       "      <td>0.001156</td>\n",
       "      <td>0.111484</td>\n",
       "      <td>0.125501</td>\n",
       "      <td>0.138594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.00</th>\n",
       "      <td>0.001010</td>\n",
       "      <td>0.001041</td>\n",
       "      <td>0.001088</td>\n",
       "      <td>0.001154</td>\n",
       "      <td>0.001252</td>\n",
       "      <td>0.058868</td>\n",
       "      <td>0.066474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.00</th>\n",
       "      <td>0.001009</td>\n",
       "      <td>0.001040</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>0.001152</td>\n",
       "      <td>0.001248</td>\n",
       "      <td>0.036191</td>\n",
       "      <td>0.042253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>0.001008</td>\n",
       "      <td>0.001038</td>\n",
       "      <td>0.001084</td>\n",
       "      <td>0.001148</td>\n",
       "      <td>0.001241</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>0.022442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.00</th>\n",
       "      <td>0.001006</td>\n",
       "      <td>0.001036</td>\n",
       "      <td>0.001081</td>\n",
       "      <td>0.001143</td>\n",
       "      <td>0.001233</td>\n",
       "      <td>0.001378</td>\n",
       "      <td>0.011481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.00</th>\n",
       "      <td>0.001003</td>\n",
       "      <td>0.001034</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>0.001139</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>0.001361</td>\n",
       "      <td>0.001665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220.64</th>\n",
       "      <td>0.001003</td>\n",
       "      <td>0.001033</td>\n",
       "      <td>0.001077</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.001222</td>\n",
       "      <td>0.001355</td>\n",
       "      <td>0.001634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              50         100        150        200        250        300  \\\n",
       "0.10    14.867378  17.196725  19.513570  21.825995  24.136491  26.446011   \n",
       "1.00     0.001012   1.695959   1.936727   2.172495   2.406191   2.638868   \n",
       "5.00     0.001012   0.001043   0.001090   0.425034   0.474429   0.522603   \n",
       "10.00    0.001012   0.001043   0.001090   0.206004   0.232739   0.257979   \n",
       "20.00    0.001011   0.001042   0.001089   0.001156   0.111484   0.125501   \n",
       "40.00    0.001010   0.001041   0.001088   0.001154   0.001252   0.058868   \n",
       "60.00    0.001009   0.001040   0.001087   0.001152   0.001248   0.036191   \n",
       "100.00   0.001008   0.001038   0.001084   0.001148   0.001241   0.001398   \n",
       "150.00   0.001006   0.001036   0.001081   0.001143   0.001233   0.001378   \n",
       "200.00   0.001003   0.001034   0.001078   0.001139   0.001225   0.001361   \n",
       "220.64   0.001003   0.001033   0.001077   0.001137   0.001222   0.001355   \n",
       "\n",
       "              350  \n",
       "0.10    28.754973  \n",
       "1.00     2.870972  \n",
       "5.00     0.570138  \n",
       "10.00    0.282492  \n",
       "20.00    0.138594  \n",
       "40.00    0.066474  \n",
       "60.00    0.042253  \n",
       "100.00   0.022442  \n",
       "150.00   0.011481  \n",
       "200.00   0.001665  \n",
       "220.64   0.001634  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "V = pd.DataFrame([[water(P = P/10.0, T=T + 273.15).v for T in Tcen] \\\n",
    "                  for P in Pbar],index=Pbar,columns=Tcen)\n",
    "V.name='Specific Volume'\n",
    "display(V)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specific Entropy [kJ/kg-K]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>50.0</th>\n",
       "      <th>100.0</th>\n",
       "      <th>150.0</th>\n",
       "      <th>200.0</th>\n",
       "      <th>250.0</th>\n",
       "      <th>300.0</th>\n",
       "      <th>350.0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.10</th>\n",
       "      <td>8.174144</td>\n",
       "      <td>8.448771</td>\n",
       "      <td>8.689152</td>\n",
       "      <td>8.904837</td>\n",
       "      <td>9.101417</td>\n",
       "      <td>9.282656</td>\n",
       "      <td>9.451271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00</th>\n",
       "      <td>0.703753</td>\n",
       "      <td>7.360999</td>\n",
       "      <td>7.614673</td>\n",
       "      <td>7.835560</td>\n",
       "      <td>8.034576</td>\n",
       "      <td>8.217124</td>\n",
       "      <td>8.386524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>0.703568</td>\n",
       "      <td>1.306702</td>\n",
       "      <td>1.841925</td>\n",
       "      <td>7.061071</td>\n",
       "      <td>7.272553</td>\n",
       "      <td>7.461448</td>\n",
       "      <td>7.634518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00</th>\n",
       "      <td>0.703337</td>\n",
       "      <td>1.306311</td>\n",
       "      <td>1.841366</td>\n",
       "      <td>6.695488</td>\n",
       "      <td>6.926623</td>\n",
       "      <td>7.124712</td>\n",
       "      <td>7.302809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.00</th>\n",
       "      <td>0.702874</td>\n",
       "      <td>1.305530</td>\n",
       "      <td>1.840252</td>\n",
       "      <td>2.330092</td>\n",
       "      <td>6.547418</td>\n",
       "      <td>6.768515</td>\n",
       "      <td>6.958244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40.00</th>\n",
       "      <td>0.701950</td>\n",
       "      <td>1.303976</td>\n",
       "      <td>1.838037</td>\n",
       "      <td>2.326932</td>\n",
       "      <td>2.793329</td>\n",
       "      <td>6.363826</td>\n",
       "      <td>6.584264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60.00</th>\n",
       "      <td>0.701028</td>\n",
       "      <td>1.302430</td>\n",
       "      <td>1.835839</td>\n",
       "      <td>2.323811</td>\n",
       "      <td>2.788482</td>\n",
       "      <td>6.070211</td>\n",
       "      <td>6.335573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100.00</th>\n",
       "      <td>0.699186</td>\n",
       "      <td>1.299363</td>\n",
       "      <td>1.831498</td>\n",
       "      <td>2.317683</td>\n",
       "      <td>2.779094</td>\n",
       "      <td>3.248366</td>\n",
       "      <td>5.945796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150.00</th>\n",
       "      <td>0.696890</td>\n",
       "      <td>1.295575</td>\n",
       "      <td>1.826165</td>\n",
       "      <td>2.310222</td>\n",
       "      <td>2.767879</td>\n",
       "      <td>3.227477</td>\n",
       "      <td>5.443497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200.00</th>\n",
       "      <td>0.694602</td>\n",
       "      <td>1.291835</td>\n",
       "      <td>1.820933</td>\n",
       "      <td>2.302969</td>\n",
       "      <td>2.757179</td>\n",
       "      <td>3.208684</td>\n",
       "      <td>3.728849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220.64</th>\n",
       "      <td>0.693659</td>\n",
       "      <td>1.290305</td>\n",
       "      <td>1.818801</td>\n",
       "      <td>2.300031</td>\n",
       "      <td>2.752899</td>\n",
       "      <td>3.201419</td>\n",
       "      <td>3.706790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             50        100       150       200       250       300       350\n",
       "0.10    8.174144  8.448771  8.689152  8.904837  9.101417  9.282656  9.451271\n",
       "1.00    0.703753  7.360999  7.614673  7.835560  8.034576  8.217124  8.386524\n",
       "5.00    0.703568  1.306702  1.841925  7.061071  7.272553  7.461448  7.634518\n",
       "10.00   0.703337  1.306311  1.841366  6.695488  6.926623  7.124712  7.302809\n",
       "20.00   0.702874  1.305530  1.840252  2.330092  6.547418  6.768515  6.958244\n",
       "40.00   0.701950  1.303976  1.838037  2.326932  2.793329  6.363826  6.584264\n",
       "60.00   0.701028  1.302430  1.835839  2.323811  2.788482  6.070211  6.335573\n",
       "100.00  0.699186  1.299363  1.831498  2.317683  2.779094  3.248366  5.945796\n",
       "150.00  0.696890  1.295575  1.826165  2.310222  2.767879  3.227477  5.443497\n",
       "200.00  0.694602  1.291835  1.820933  2.302969  2.757179  3.208684  3.728849\n",
       "220.64  0.693659  1.290305  1.818801  2.300031  2.752899  3.201419  3.706790"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S = pd.DataFrame([[water(P = P/10.0, T=T + 273.15).s for T in Tcen] \\\n",
    "                  for P in Pbar],index=Pbar,columns=Tcen)\n",
    "S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## T-S Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bb2k06Ufv3sCbb5rv001n/cABSkn0y4oJmzHToUO4fHk7y5cD55/P385U7mDk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6a2b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,10))\n",
    "plt.hold(True)\n",
    "\n",
    "H = np.linspace(200,3600,18)\n",
    "\n",
    "for h in H:\n",
    "    P = np.linspace(0.01,100,100)\n",
    "    T = np.array([water(h=h,P=p).T - 273.15 for p in P])\n",
    "    S = np.array([water(h=h,P=p).s for p in P])\n",
    "    plt.plot(S,T,'b',lw=0.5)\n",
    "\n",
    "P = np.array([0.01,0.02,0.05,0.1,0.2,0.5,1.0,2.0,5.0,10.0,20.0,50.0,100.0])\n",
    "for p in P:\n",
    "    H = np.linspace(200,3600,15)\n",
    "    T = np.array([water(h=h,P=p).T - 273.15 for h in H])\n",
    "    S = np.array([water(h=h,P=p).s for h in H])\n",
    "    plt.plot(S,T,'b',lw=0.5)\n",
    "\n",
    "T = np.linspace(1.0,374.0,20)\n",
    "for x in np.linspace(0.001,0.999,11):\n",
    "    S = np.array([water(T=t,x=x).s for t in (T+273.15)])\n",
    "    plt.plot(S,T,'r--',lw=0.5)\n",
    "\n",
    "for x in np.array([0.001,0.999]):\n",
    "    S = np.array([water(T=t,x=x).s for t in (T+273.15)])\n",
    "    plt.plot(S,T,'r',lw=1.0)\n",
    "\n",
    "plt.xlim(0,9)\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}