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     "source": [
      "### Nomenclature:  \n",
      "$d$ means a derivative ALONG the saturation line,  \n",
      "$\\partial$ means a partial derivative AT the saturation line (or anywhere in the single phase region).\n",
      "\n",
      "### References:  \n",
      "Krafcik and Velasco, DOI 10.1119/1.4858403  \n",
      "Thorade and Saadat, DOI 10.1007/s12665-013-2394-z  \n",
      "https://en.wikipedia.org/wiki/Product_rule  \n",
      "https://en.wikipedia.org/wiki/Quotient_rule  \n",
      "https://en.wikipedia.org/wiki/Triple_product_rule  \n",
      "\n",
      "### Clausius-Clapeyron\n",
      "Clausius-Clapeyron p/T\n",
      "\n",
      "\\begin{equation}\n",
      "\\frac{dp}{dT} = \\frac{s''-s'}{v''-v'}\n",
      "\\end{equation}\n",
      "\n",
      "derivative of Clausius-Clapeyron\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{d^2 p}{d T^2} &= \n",
      "    \\frac{\\left(\\frac{ds''}{dT}-\\frac{ds'}{dT}\\right) (v''-v')}{(v''-v')^2} \n",
      "  - \\frac{(s''-s') \\left(\\frac{dv''}{dT}-\\frac{dv'}{dT}\\right)}{(v''-v')^2} \\\\\n",
      " &= \\frac{\\left(\\frac{ds''}{dT}-\\frac{ds'}{dT}\\right)}{(v''-v')} \n",
      "  - \\frac{dp}{dT} \\frac{\\left(\\frac{dv''}{dT}-\\frac{dv'}{dT}\\right)}{(v''-v')}\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "\n",
      "Clausius-Clapeyron T/p\n",
      "\\begin{equation}\n",
      "\\frac{dT}{dp} = \\frac{v''-v'}{s''-s'}\n",
      "\\end{equation}\n",
      "\n",
      "derivative of Clausius-Clapeyron\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{d^2 T}{d p^2} &= \n",
      "    \\frac{\\left(\\frac{dv''}{dp}-\\frac{dv'}{dp}\\right) (s''-s')}{(s''-s')^2} \n",
      "  - \\frac{(v''-v') \\left(\\frac{ds''}{dp}-\\frac{ds'}{dp}\\right)}{(s''-s')^2} \\\\\n",
      " &= \\frac{\\left(\\frac{dv''}{dp}-\\frac{dv'}{dp}\\right)}{(s''-s')} \n",
      "  - \\frac{dT}{dp} \\frac{\\left(\\frac{ds''}{dp}-\\frac{ds'}{dp}\\right)}{(s''-s')}\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "\n",
      "where\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{ds}{dT} &= \\left(\\frac{\\partial s}{\\partial T}\\right)_p  + \\left(\\frac{\\partial s}{\\partial p}\\right)_T \\frac{dp}{dT}\\\\\n",
      "\\frac{dv}{dT} &= \\left(\\frac{\\partial v}{\\partial T}\\right)_p  + \\left(\\frac{\\partial v}{\\partial p}\\right)_T \\frac{dp}{dT}\\\\\n",
      "\\frac{dv}{dp} &= \\left(\\frac{\\partial v}{\\partial p}\\right)_T  + \\left(\\frac{\\partial v}{\\partial T}\\right)_p \\frac{dT}{dp}\\\\\n",
      "\\frac{ds}{dp} &= \\left(\\frac{\\partial s}{\\partial p}\\right)_T  + \\left(\\frac{\\partial s}{\\partial T}\\right)_p \\frac{dT}{dp}\n",
      "\\end{split}\n",
      "\\end{equation}"
     ]
    },
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     "source": [
      "### Temporary Names\n",
      "\n",
      "Introduce temporary names for some of the partial derivatives wrt $p$ and $T$:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "A &= \\left(\\frac{\\partial \\rho}{\\partial T}\\right)_p \\\\\n",
      "B &= \\left(\\frac{\\partial \\rho}{\\partial p}\\right)_T \\\\\n",
      "C &= \\left(\\frac{\\partial s}{\\partial T}\\right)_p \\\\\n",
      "E &= \\left(\\frac{\\partial s}{\\partial p}\\right)_T \\\\\n",
      "G &= \\left(\\frac{\\partial h}{\\partial T}\\right)_p \\\\\n",
      "K &= \\left(\\frac{\\partial h}{\\partial p}\\right)_T\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "\n",
      "Introduce temporary names for some of the partial derivatives wrt $\\rho$ and $T$:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "W &= \\left(\\frac{\\partial p}{\\partial T}\\right)_{\\rho} \\\\\n",
      "X &= \\left(\\frac{\\partial p}{\\partial \\rho}\\right)_T \\\\\n",
      "Y &= \\left(\\frac{\\partial s}{\\partial T}\\right)_{\\rho} \\\\\n",
      "Z &= \\left(\\frac{\\partial s}{\\partial \\rho}\\right)_T \n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "\n",
      "Introduce temporary names for some of the derivatives along the saturation line:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "M &= \\frac{d \\rho}{d h} = \\frac{{d \\rho}/{dT}}{{dh}/{dT}} \\\\\n",
      "N &= \\frac{d s}{d h} = \\frac{{ds}/{dT}}{{dh}/{dT}}\n",
      "\\end{split}\n",
      "\\end{equation}"
     ]
    },
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     "cell_type": "markdown",
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     "source": [
      "Now the rest is not too hard, but with intermediate steps it is quite long.\n",
      "\n",
      "### First example: $d^2 \\rho / dT^2$  \n",
      "The corresponding first derivative can be written in two ways:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{d \\rho}{dT} \n",
      "  &= \\left(\\frac{\\partial \\rho}{\\partial T}\\right)_p + \\left(\\frac{\\partial \\rho}{\\partial p}\\right)_T \\frac{d p}{dT} \n",
      "   = A + B \\frac{dp}{dT}\\\\\n",
      "  &= {\\frac{dp}{dT} \\left(\\frac{\\partial p}{\\partial T}\\right)_{\\rho}} / {\\left(\\frac{\\partial p}{\\partial \\rho}\\right)_T}\n",
      "   = {\\frac{dp}{dT} W}/X\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "Both ways can be used as a starting point for the second derivative:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{d^2 \\rho}{dT^2} \n",
      "  &= \\frac{dA}{dT} + \\frac{dB}{dT}\\frac{dp}{dT} + B\\frac{d^2p}{dT^2}\\\\\n",
      "  &= \\frac{\\left(\\frac{d^2p}{dT^2} - \\frac{dW}{dT}\\right)X - \\left(\\frac{dp}{dT}-W\\right)\\frac{dX}{dT}}{X^2}\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "The first variant seems to be nicer, because the first deriv did not have a fraction in it.  \n",
      "The derivatives along the saturation line that appeared can be written as (again, using two different approaches):\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{dA}{dT} &= \\left(\\frac{\\partial A}{\\partial T}\\right)_p + \\left(\\frac{\\partial A}{\\partial p}\\right)_T \\frac{d p}{dT}\\\\\n",
      "\\frac{dB}{dT} &= \\left(\\frac{\\partial B}{\\partial T}\\right)_p + \\left(\\frac{\\partial B}{\\partial p}\\right)_T \\frac{d p}{dT}\\\\\n",
      "\\frac{dW}{dT} &= \\left(\\frac{\\partial W}{\\partial T}\\right)_{\\rho} + \\left(\\frac{\\partial W}{\\partial \\rho}\\right)_T \\frac{d\\rho}{dT}\\\\\n",
      "\\frac{dX}{dT} &= \\left(\\frac{\\partial X}{\\partial T}\\right)_{\\rho} + \\left(\\frac{\\partial X}{\\partial \\rho}\\right)_T \\frac{d\\rho}{dT}\\\\\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "and then the partial derivatives of A, B, W and X wrt p and T can be written as:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\left(\\frac{\\partial A}{\\partial T}\\right)_p &= \\left(\\frac{\\partial^2 \\rho}{\\partial T^2}\\right)_p \\\\\n",
      "\\left(\\frac{\\partial A}{\\partial p}\\right)_T &= \\left(\\frac{\\partial^2 \\rho}{\\partial p \\partial T}\\right) \\\\\n",
      "\\left(\\frac{\\partial B}{\\partial T}\\right)_p &= \\left(\\frac{\\partial^2 \\rho}{\\partial T \\partial p}\\right) = \\left(\\frac{\\partial A}{\\partial p}\\right)_T \\\\\n",
      "\\left(\\frac{\\partial B}{\\partial p}\\right)_T &= \\left(\\frac{\\partial^2 \\rho}{\\partial p^2}\\right)_T \\\\\n",
      "\\left(\\frac{\\partial W}{\\partial T}\\right)_{\\rho} &= \\left(\\frac{\\partial^2 p}{\\partial T^2}\\right)_{\\rho} \\\\\n",
      "\\left(\\frac{\\partial W}{\\partial \\rho}\\right)_T &= \\left(\\frac{\\partial^2 p}{\\partial \\rho \\partial T}\\right) \\\\\n",
      "\\left(\\frac{\\partial X}{\\partial T}\\right)_{\\rho} &= \\left(\\frac{\\partial^2 p}{\\partial T \\partial \\rho}\\right) = \\left(\\frac{\\partial W}{\\partial \\rho}\\right)_T \\\\\n",
      "\\left(\\frac{\\partial X}{\\partial \\rho}\\right)_T &= \\left(\\frac{\\partial^2 p}{\\partial {\\rho}^2}\\right)_T\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "So this time, the second way looks maybe nicer, because it uses partial derivatives wrt to density and temperature. On the other hand, all second partial derivatives are already implemented in Coolprop and the pT derivatives are internally rewritten as dT derivatives.\n",
      "\n",
      "### Second example: $d^2s/dT^2$  \n",
      "The corresponding first derivative can be written in two ways:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{ds}{dT} \n",
      "  &= \\left(\\frac{\\partial s}{\\partial T}\\right)_p + \\left(\\frac{\\partial s}{\\partial p}\\right)_T \\frac{dp}{dT} \n",
      "   = C + E\\frac{dp}{dT}\\\\\n",
      "  &= \\left(\\frac{\\partial s}{\\partial T}\\right)_{\\rho} + \\left(\\frac{\\partial s}{\\partial \\rho}\\right)_T \\frac{d \\rho}{dT}\n",
      "   = Y + Z\\frac{d \\rho}{dT}\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "Both can be used as starting point for the second derivatives.\n",
      "\\begin{split}\n",
      "\\frac{d^2 s}{dT^2} \n",
      "  &= \\frac{dC}{dT} + \\frac{dE}{dT}\\frac{dp}{dT} + E\\frac{d^2p}{dT^2}\\\\\n",
      "  &= \\frac{dY}{dT} + \\frac{dZ}{dT}\\frac{dp}{dT} + Z\\frac{d^2 \\rho}{dT^2}\n",
      "\\end{split}\n",
      "Now, which one is nicer to work with? Not sure. \n",
      "Just as shown above, the derivatives along the saturation line that appeared can be written as (again, using two different approaches):\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{dC}{dT} &= \\left(\\frac{\\partial C}{\\partial T}\\right)_p + \\left(\\frac{\\partial C}{\\partial p}\\right)_T \\frac{d p}{dT}\\\\\n",
      "\\frac{E}{dT} &= \\left(\\frac{\\partial E}{\\partial T}\\right)_p + \\left(\\frac{\\partial E}{\\partial p}\\right)_T \\frac{d p}{dT}\\\\\n",
      "\\frac{dY}{dT} &= \\left(\\frac{\\partial Y}{\\partial T}\\right)_{\\rho} + \\left(\\frac{\\partial Y}{\\partial \\rho}\\right)_T \\frac{d\\rho}{dT}\\\\\n",
      "\\frac{dZ}{dT} &= \\left(\\frac{\\partial Z}{\\partial T}\\right)_{\\rho} + \\left(\\frac{\\partial Z}{\\partial \\rho}\\right)_T \\frac{d\\rho}{dT}\\\\\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "Rewriting the the partial derivatives works just like shown above.\n",
      "\n",
      "### Third example: $d^2{\\rho}/dp^2$  \n",
      "The corresponding first derivative can be written in many ways, here is one:\n",
      "\\begin{equation}\n",
      "\\frac{d \\rho}{dp} \n",
      "  = \\left(\\frac{\\partial \\rho}{\\partial p}\\right)_T + \\left(\\frac{\\partial \\rho}{\\partial T}\\right)_p \\frac{dT}{dp} \n",
      "  = B + A\\frac{dT}{dp}\n",
      "\\end{equation}\n",
      "\n",
      "The second derivative is then:\n",
      "\\begin{equation}\n",
      "\\frac{d^2 \\rho}{dp^2} \n",
      "  = \\frac{dB}{dp} + \\frac{dA}{dp}\\frac{dT}{dp} + A\\frac{d^2T}{dp^2}\n",
      "\\end{equation}\n",
      "The derivatives wrt p along the saturation line can be written as:\n",
      "\\begin{equation}\n",
      "\\begin{split}\n",
      "\\frac{dB}{dp} &= \\left(\\frac{\\partial B}{\\partial p}\\right)_T + \\left(\\frac{\\partial B}{\\partial T}\\right)_p \\frac{d T}{dp}\\\\\n",
      "\\frac{dA}{dp} &= \\left(\\frac{\\partial A}{\\partial p}\\right)_T + \\left(\\frac{\\partial A}{\\partial T}\\right)_p \\frac{d T}{dp}\n",
      "\\end{split}\n",
      "\\end{equation}\n",
      "Rewriting the partial derivatives works just like shown above.\n",
      "\n",
      "### Fourth example: $d^2s/dp^2$  \n",
      "The corresponding first derivative can be written in many ways, here is one:\n",
      "\\begin{equation}\n",
      "\\frac{ds}{dp} \n",
      "  = \\left(\\frac{\\partial s}{\\partial p}\\right)_T + \\left(\\frac{\\partial s}{\\partial T}\\right)_p \\frac{dT}{dp} \n",
      "  = E + C\\frac{dT}{dp}\n",
      "\\end{equation}\n",
      "\n",
      "The second derivative is then:\n",
      "\\begin{equation}\n",
      "\\frac{d^2 s}{dp^2} \n",
      "  = \\frac{dE}{dp} + \\frac{dC}{dp}\\frac{dT}{dp} + C\\frac{d^2T}{dp^2}\n",
      "\\end{equation}\n",
      "Rewriting the derivatives along the sat line and the partial derivatives works just like shown above.\n",
      "\n",
      "### Fifth example: $d^2h/dT^2$  \n",
      "First:\n",
      "\\begin{equation}\n",
      "\\frac{dh}{dT} \n",
      "  = \\left(\\frac{\\partial h}{\\partial T}\\right)_p + \\left(\\frac{\\partial h}{\\partial p}\\right)_T \\frac{dp}{dT} \n",
      "  = G + K\\frac{dp}{dT}\n",
      "\\end{equation}\n",
      "\n",
      "Second:\n",
      "\\begin{equation}\n",
      "\\frac{d^2 h}{dT^2} \n",
      "  = \\frac{dG}{dT} + \\frac{dK}{dT}\\frac{dp}{dT} + K\\frac{d^2p}{dT^2}\n",
      "\\end{equation}\n",
      "\n",
      "### Sixth example: $d^2{\\rho}/dh^2$  \n",
      "This is a bit different, but also not difficult.  \n",
      "First:\n",
      "\\begin{equation}\n",
      "  M = \\frac{d\\rho}{dh} = \\frac{{d\\rho}/{dT}}{{dh}/{dT}}\n",
      "\\end{equation}\n",
      "\n",
      "Intermediate:\n",
      "\\begin{equation}\n",
      "\\frac{dM}{dT} \n",
      "  = \\frac{\\frac{d^2 \\rho}{dT^2}\\frac{dh}{dT}-\\frac{d \\rho}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^2}\n",
      "\\end{equation}\n",
      "\n",
      "Second:\n",
      "\\begin{equation}\n",
      "\\frac{d^2 \\rho}{dh^2} \n",
      "  = \\frac{dM}{dh}\n",
      "  = \\frac{{dM}/{dT}}{{dh}/{dT}}\n",
      "  = \\frac{\\frac{d^2 \\rho}{dT^2}\\frac{dh}{dT}-\\frac{d \\rho}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^3}\n",
      "\\end{equation}\n",
      "\n",
      "### Seventh example: $d^2s/dh^2$  \n",
      "First:\n",
      "\\begin{equation}\n",
      "  N = \\frac{ds}{dh} = \\frac{{ds}/{dT}}{{dh}/{dT}}\n",
      "\\end{equation}\n",
      "\n",
      "Intermediate:\n",
      "\\begin{equation}\n",
      "\\frac{dN}{dT} \n",
      "  = \\frac{\\frac{d^2s}{dT^2}\\frac{dh}{dT}-\\frac{ds}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^2}\n",
      "\\end{equation}\n",
      "\n",
      "Second:\n",
      "\\begin{equation}\n",
      "\\frac{d^2s}{dh^2} \n",
      "  = \\frac{dN}{dh}\n",
      "  = \\frac{{dN}/{dT}}{{dh}/{dT}}\n",
      "  = \\frac{\\frac{d^2s}{dT^2}\\frac{dh}{dT}-\\frac{ds}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^3}\n",
      "\\end{equation}\n",
      "\n",
      "### Eighth example: $d^2s/(dh dp)$  \n",
      "First:\n",
      "\\begin{equation}\n",
      "  N = \\frac{ds}{dh} = \\frac{{ds}/{dT}}{{dh}/{dT}}\n",
      "\\end{equation}\n",
      "\n",
      "Intermediate:\n",
      "\\begin{equation}\n",
      "\\frac{dN}{dT} \n",
      "  = \\frac{\\frac{d^2s}{dT^2}\\frac{dh}{dT}-\\frac{ds}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^2}\n",
      "\\end{equation}\n",
      "\n",
      "Second:\n",
      "\\begin{equation}\n",
      "\\frac{d^2s}{dh dp} \n",
      "  = \\frac{dN}{dp}\n",
      "  = \\frac{{dN}/{dT}}{{dp}/{dT}}\n",
      "  = \\frac{\\frac{d^2s}{dT^2}\\frac{dh}{dT}-\\frac{ds}{dT}\\frac{d^2h}{dT^2}}{\\left(\\frac{dh}{dT}\\right)^2 \\left(\\frac{dp}{dT}\\right)}\n",
      "\\end{equation}"
     ]
    },
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     "collapsed": false,
     "input": [
      "# load some bits and pieces\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "import CoolProp as CP\n",
      "from CoolProp.CoolProp import PropsSI\n",
      "\n",
      "# Check: CoolProp version\n",
      "print(CP.__version__)\n",
      "print(CP.__gitrevision__)\n",
      "\n",
      "# Constants\n",
      "eps = 1e-9\n",
      "kilo = 1e3\n",
      "Mega = 1e6\n",
      "golden = (1 + 5 ** 0.5) / 2\n",
      "nPoints = 1000\n",
      "width = 12.5\n",
      "\n",
      "# helper function: get slope from two sorted arrays\n",
      "def numSlopeAr(xAr, yAr):\n",
      " deltaX = np.ones(len(xAr))\n",
      " deltaY = np.ones(len(yAr))\n",
      " slopeAr = np.ones(len(yAr))\n",
      " for index in range(1,len(xAr)-1):\n",
      "     deltaX[index] = xAr[index-1] - xAr[index+1]\n",
      "     deltaY[index] = yAr[index-1] - yAr[index+1]\n",
      "     slopeAr[index] = deltaY[index]/deltaX[index]\n",
      " # inaccurate, but who cares?\n",
      " slopeAr[0]=slopeAr[1]\n",
      " slopeAr[-1]=slopeAr[-2]  \n",
      " return slopeAr\n",
      "\n",
      "# helper function: draw tangent from array\n",
      "def drawTangent(xAr, yAr, slopeAr, index: int):\n",
      " xVal = xAr[index]\n",
      " yVal = yAr[index]\n",
      " slopeVal = slopeAr[index]\n",
      " # line length as percentage of length of x axis\n",
      " xRange = 0.15*abs(max(xAr)-min(xAr))\n",
      " xRangeAr = np.arange(xVal-xRange, xVal+xRange)\n",
      " tang = yVal + slopeVal*(xRangeAr-xVal)\n",
      " plt.plot(xVal,yVal,'om',xRangeAr,tang,'-k')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5.1.1\n",
        "12f006445f234e572e64cc820146ab5d2c2a9d10\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# All calculations happen in this cell\n",
      "# All following cells are used for plotting only\n",
      "# Run this cell twice to get rid of error message\n",
      "\n",
      "# Set FluidName\n",
      "FluidName = 'Propane'\n",
      "\n",
      "# Triple and critical data\n",
      "T_crt = PropsSI('TCRIT',FluidName)\n",
      "T_trp = PropsSI('TTRIPLE',FluidName)\n",
      "p_crt = PropsSI('PCRIT',FluidName)\n",
      "p_trp = PropsSI('PTRIPLE',FluidName)\n",
      "d_crt = PropsSI('RHOCRIT',FluidName)\n",
      "d_trp_liq = PropsSI('D','T',T_trp,'Q',0,FluidName)\n",
      "d_trp_vap  = PropsSI('D','T',T_trp,'Q',1,FluidName)\n",
      "print(\"T_crt = \" + str(T_crt))\n",
      "print(\"T_trp = \" + str(T_trp))\n",
      "\n",
      "# Properties at saturation, liq and vap\n",
      "# All the way to the crt point or keep some distance?\n",
      "T_sat = np.linspace(T_trp, T_crt, num=nPoints)\n",
      "p_sat = CP.CoolProp.PropsSI('P','T',T_sat,'Q',0,FluidName)\n",
      "d_sat_liq = CP.CoolProp.PropsSI('D','T',T_sat,'Q',0,FluidName)\n",
      "d_sat_vap = CP.CoolProp.PropsSI('D','T',T_sat,'Q',1,FluidName)\n",
      "v_sat_liq = 1/d_sat_liq\n",
      "v_sat_vap = 1/d_sat_vap\n",
      "s_sat_liq = CP.CoolProp.PropsSI('S','T',T_sat,'Q',0,FluidName)\n",
      "s_sat_vap = CP.CoolProp.PropsSI('S','T',T_sat,'Q',1,FluidName)\n",
      "h_sat_liq = CP.CoolProp.PropsSI('H','T',T_sat,'Q',0,FluidName)\n",
      "h_sat_vap = CP.CoolProp.PropsSI('H','T',T_sat,'Q',1,FluidName)\n",
      "u_sat_liq = CP.CoolProp.PropsSI('U','T',T_sat,'Q',0,FluidName)\n",
      "u_sat_vap = CP.CoolProp.PropsSI('U','T',T_sat,'Q',1,FluidName)\n",
      "\n",
      "# Clausius-Clapeyron\n",
      "# at crt point, vap=liq, so D becomes zero\n",
      "Ds_vl = (s_sat_vap-s_sat_liq)\n",
      "Dv_vl = (v_sat_vap-v_sat_liq)\n",
      "dp_dT_q =  Ds_vl/Dv_vl \n",
      "dT_dp_q = Dv_vl/Ds_vl # = 1/dp_dT_q\n",
      "dp_dT_qn = numSlopeAr(T_sat,p_sat)\n",
      "dT_dp_qn = numSlopeAr(p_sat,T_sat)\n",
      "\n",
      "# derivs wrt pT, single-phase AT the sat liq line\n",
      "dd_dp_Tl = CP.CoolProp.PropsSI('d(Dmass)/d(P)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dd_dT_pl = CP.CoolProp.PropsSI('d(Dmass)/d(T)|P','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "ds_dp_Tl = CP.CoolProp.PropsSI('d(Smass)/d(P)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "ds_dT_pl = CP.CoolProp.PropsSI('d(Smass)/d(T)|P','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dh_dp_Tl = CP.CoolProp.PropsSI('d(Hmass)/d(P)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dh_dT_pl = CP.CoolProp.PropsSI('d(Hmass)/d(T)|P','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dv_dp_Tl = -dd_dp_Tl/d_sat_liq**2\n",
      "dv_dT_pl = -dd_dT_pl/d_sat_liq**2\n",
      "\n",
      "# derivs wrt pT, single-phase AT the sat vap line\n",
      "dd_dp_Tv = CP.CoolProp.PropsSI('d(Dmass)/d(P)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dd_dT_pv = CP.CoolProp.PropsSI('d(Dmass)/d(T)|P','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "ds_dp_Tv = CP.CoolProp.PropsSI('d(Smass)/d(P)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "ds_dT_pv = CP.CoolProp.PropsSI('d(Smass)/d(T)|P','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dh_dp_Tv = CP.CoolProp.PropsSI('d(Hmass)/d(P)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dh_dT_pv = CP.CoolProp.PropsSI('d(Hmass)/d(T)|P','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dv_dp_Tv = -dd_dp_Tv/d_sat_vap**2\n",
      "dv_dT_pv = -dd_dT_pv/d_sat_vap**2\n",
      "\n",
      "# derivs wrt dT, single-phase AT the sat liq line\n",
      "dp_dd_Tl = CP.CoolProp.PropsSI('d(P)/d(Dmass)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dp_dT_dl = CP.CoolProp.PropsSI('d(P)/d(T)|Dmass','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "ds_dd_Tl = CP.CoolProp.PropsSI('d(Smass)/d(D)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "ds_dT_dl = CP.CoolProp.PropsSI('d(Smass)/d(T)|D','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dh_dd_Tl = CP.CoolProp.PropsSI('d(Hmass)/d(D)|T','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dh_dT_dl = CP.CoolProp.PropsSI('d(Hmass)/d(T)|D','D',d_sat_liq,'T',T_sat,FluidName)\n",
      "dp_dv_Tl = -d_sat_liq**2 * dp_dd_Tl\n",
      "dp_dT_vl = dp_dT_dl\n",
      "\n",
      "# derivs wrt dT, single-phase AT the sat vap line\n",
      "dp_dd_Tv = CP.CoolProp.PropsSI('d(P)/d(Dmass)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dp_dT_dv = CP.CoolProp.PropsSI('d(P)/d(T)|Dmass','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "ds_dd_Tv = CP.CoolProp.PropsSI('d(Smass)/d(D)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "ds_dT_dv = CP.CoolProp.PropsSI('d(Smass)/d(T)|D','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dh_dd_Tv = CP.CoolProp.PropsSI('d(Hmass)/d(D)|T','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dh_dT_dv = CP.CoolProp.PropsSI('d(Hmass)/d(T)|D','D',d_sat_vap,'T',T_sat,FluidName)\n",
      "dp_dv_Tv = -d_sat_vap**2 * dp_dd_Tv\n",
      "dp_dT_vv = dp_dT_dv\n",
      "\n",
      "# two ways for derivs along the sat line: using derivs wrt dT or pT\n",
      "\n",
      "# derivs wrt T ALONG the sat liq line (that is, at q=const=0)\n",
      "dd_dT_ql = (dp_dT_q - dp_dT_dl)/dp_dd_Tl\n",
      "dv_dT_ql = dv_dT_pl + dv_dp_Tl*dp_dT_q\n",
      "ds_dT_ql = ds_dT_dl + ds_dd_Tl*dd_dT_ql\n",
      "#ds_dT_ql = ds_dT_pl + ds_dp_Tl*dp_dT_q\n",
      "dh_dT_ql = dh_dT_pl + dh_dp_Tl*dp_dT_q\n",
      "\n",
      "# derivs wrt T ALONG the sat vap line (that is, at q=const=1)\n",
      "dd_dT_qv = (dp_dT_q - dp_dT_dv)/dp_dd_Tv\n",
      "dv_dT_qv = (dp_dT_q - dp_dT_dv)/dp_dd_Tv\n",
      "dv_dT_qv = dv_dT_pv + dv_dp_Tv*dp_dT_q\n",
      "ds_dT_qv = ds_dT_dv + ds_dd_Tv*dd_dT_qv\n",
      "dh_dT_qv = dh_dT_pv + dh_dp_Tv*dp_dT_q\n",
      "\n",
      "# derivs wrt p ALONG the sat liq line (that is, at q=const=0)\n",
      "dd_dp_ql = dd_dp_Tl + dd_dT_pl*dT_dp_q\n",
      "ds_dp_ql = ds_dp_Tl + ds_dT_pl*dT_dp_q\n",
      "dv_dp_ql = dv_dp_Tl + dv_dT_pl*dT_dp_q\n",
      "\n",
      "# derivs wrt p ALONG the sat vap line (that is, at q=const=1)\n",
      "dd_dp_qv = dd_dp_Tv + dd_dT_pv*dT_dp_q\n",
      "ds_dp_qv = ds_dp_Tv + ds_dT_pv*dT_dp_q\n",
      "dv_dp_qv = dv_dp_Tv + dv_dT_pv*dT_dp_q\n",
      "\n",
      "# derivs wrt h ALONG the sat line\n",
      "dd_dh_ql = dd_dT_ql/dh_dT_ql\n",
      "dd_dh_qv = dd_dT_qv/dh_dT_qv\n",
      "ds_dh_ql = ds_dT_ql/dh_dT_ql\n",
      "ds_dh_qv = ds_dT_qv/dh_dT_qv\n",
      "\n",
      "# derivs of Clausius-Clapeyron\n",
      "# d2p_dT2_q = ((ds_dT_qv-ds_dT_ql)*Dv_vl - Ds_vl*(dv_dT_qv-dv_dT_ql)) / Dv_vl**2\n",
      "d2p_dT2_q = (ds_dT_qv-ds_dT_ql)/Dv_vl - dp_dT_q*(dv_dT_qv-dv_dT_ql)/Dv_vl\n",
      "d2T_dp2_q = (dv_dp_qv-dv_dp_ql)/Ds_vl - dT_dp_q*(ds_dp_qv-ds_dp_ql)/Ds_vl\n",
      "d2p_dT2_qn = numSlopeAr(T_sat,dp_dT_q)\n",
      "d2T_dp2_qn = numSlopeAr(p_sat,dT_dp_q)\n",
      "\n",
      "# second derivs AT the sat line\n",
      "# AT the liq line\n",
      "# wrt pT \n",
      "d2d_dT2_pl = np.empty(nPoints)\n",
      "d2d_dp2_Tl = np.empty(len(T_sat))\n",
      "d2d_dpTl   = np.empty(len(T_sat))\n",
      "d2s_dT2_pl = np.empty(len(T_sat))\n",
      "d2s_dp2_Tl = np.empty(len(T_sat))\n",
      "d2s_dpTl   = np.empty(len(T_sat))\n",
      "d2h_dT2_pl = np.empty(len(T_sat))\n",
      "d2h_dp2_Tl = np.empty(len(T_sat))\n",
      "d2h_dpTl   = np.empty(len(T_sat))\n",
      "# wrt dT\n",
      "d2s_dT2_dl = np.empty(len(T_sat))\n",
      "d2s_dd2_Tl = np.empty(len(T_sat))\n",
      "d2s_ddTl   = np.empty(len(T_sat))\n",
      "# AT the vap line\n",
      "# wrt pT \n",
      "d2d_dT2_pv = np.empty(len(T_sat))\n",
      "d2d_dp2_Tv = np.empty(len(T_sat))\n",
      "d2d_dpTv   = np.empty(len(T_sat))\n",
      "d2s_dT2_pv = np.empty(len(T_sat))\n",
      "d2s_dp2_Tv = np.empty(len(T_sat))\n",
      "d2s_dpTv   = np.empty(len(T_sat))\n",
      "d2h_dT2_pv = np.empty(len(T_sat))\n",
      "d2h_dp2_Tv = np.empty(len(T_sat))\n",
      "d2h_dpTv   = np.empty(len(T_sat))\n",
      "# wrt dT\n",
      "d2s_dT2_dv = np.empty(len(T_sat))\n",
      "d2s_dd2_Tv = np.empty(len(T_sat))\n",
      "d2s_ddTv   = np.empty(len(T_sat))\n",
      "\n",
      "HEOS = CP.AbstractState(\"HEOS\", FluidName)\n",
      "for idx in range(0,len(T_sat)):\n",
      "    # AT the liq line\n",
      "    HEOS.update(CP.QT_INPUTS, 0, T_sat[idx])    \n",
      "    # wrt pT\n",
      "    d2d_dT2_pl[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2d_dp2_Tl[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2d_dpTl[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    d2s_dT2_pl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2s_dp2_Tl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2s_dpTl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    d2h_dT2_pl[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2h_dp2_Tl[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2h_dpTl[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    # wrt dT\n",
      "    d2s_dT2_dl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iT, CP.iDmass, CP.iT, CP.iDmass)\n",
      "    d2s_dd2_Tl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iDmass, CP.iT, CP.iDmass, CP.iT)\n",
      "    d2s_ddTl[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iDmass, CP.iT, CP.iT, CP.iDmass)\n",
      "    \n",
      "    # AT the vap line\n",
      "    HEOS.update(CP.QT_INPUTS, 1, T_sat[idx])    \n",
      "    # wrt pT\n",
      "    d2d_dT2_pv[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2d_dp2_Tv[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2d_dpTv[idx] = HEOS.second_partial_deriv(CP.iDmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    d2s_dT2_pv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2s_dp2_Tv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2s_dpTv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    d2h_dT2_pv[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iT, CP.iP, CP.iT, CP.iP)\n",
      "    d2h_dp2_Tv[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iP, CP.iT, CP.iP, CP.iT)\n",
      "    d2h_dpTv[idx] = HEOS.second_partial_deriv(CP.iHmass, CP.iP, CP.iT, CP.iT, CP.iP)\n",
      "    # wrt dT\n",
      "    d2s_dT2_dv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iT, CP.iDmass, CP.iT, CP.iDmass)\n",
      "    d2s_dd2_Tv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iDmass, CP.iT, CP.iDmass, CP.iT)\n",
      "    d2s_ddTv[idx] = HEOS.second_partial_deriv(CP.iSmass, CP.iDmass, CP.iT, CP.iT, CP.iDmass)    \n",
      "    \n",
      "\n",
      "# calculate 2nd derivs ALONG the sat line (analytically and numerically)\n",
      "\n",
      "# A=dd_dT_p and B=dd_dp_T\n",
      "# liq side\n",
      "dA_dT_ql = d2d_dT2_pl + d2d_dpTl*dp_dT_q\n",
      "dB_dT_ql = d2d_dpTl + d2d_dp2_Tl*dp_dT_q\n",
      "d2d_dT2_qla = dA_dT_ql + dB_dT_ql*dp_dT_q + dd_dp_Tl*d2p_dT2_q\n",
      "d2d_dT2_qln = numSlopeAr(T_sat, dd_dT_ql)\n",
      "# vap side\n",
      "dA_dT_qv = d2d_dT2_pv + d2d_dpTv*dp_dT_q\n",
      "dB_dT_qv = d2d_dpTv + d2d_dp2_Tv*dp_dT_q\n",
      "d2d_dT2_qva = dA_dT_qv + dB_dT_qv*dp_dT_q + dd_dp_Tv*d2p_dT2_q\n",
      "d2d_dT2_qvn = numSlopeAr(T_sat, dd_dT_qv)\n",
      "#print(d2d_dT2_qla - d2d_dT2_qln)\n",
      "#print(d2d_dT2_qva - d2d_dT2_qln)\n",
      "\n",
      "# C=ds_dT_p and E=ds_dp_T\n",
      "# liq side\n",
      "dC_dT_ql = d2s_dT2_dl + d2s_ddTl*dd_dT_ql\n",
      "dE_dT_ql = d2s_ddTl + d2s_dd2_Tl*dd_dT_ql\n",
      "d2s_dT2_qla = dC_dT_ql + dE_dT_ql*dd_dT_ql + ds_dd_Tl*d2d_dT2_qla\n",
      "d2s_dT2_qln = numSlopeAr(T_sat, ds_dT_ql)\n",
      "# vap side\n",
      "dC_dT_qv = d2s_dT2_dv + d2s_ddTv*dd_dT_qv\n",
      "dE_dT_qv = d2s_ddTv + d2s_dd2_Tv*dd_dT_qv\n",
      "d2s_dT2_qva = dC_dT_qv + dE_dT_qv*dd_dT_qv + ds_dd_Tv*d2d_dT2_qva\n",
      "d2s_dT2_qvn = numSlopeAr(T_sat, ds_dT_qv)\n",
      "#print(d2s_dT2_qla - d2s_dT2_qln)\n",
      "#print(d2s_dT2_qva - d2s_dT2_qvn)\n",
      "\n",
      "# B=dd_dp_T and A=dd_dT_p\n",
      "# liq side\n",
      "dB_dp_ql = d2d_dp2_Tl + d2d_dpTl*dT_dp_q\n",
      "dA_dp_ql = d2d_dpTl + d2d_dT2_pl*dT_dp_q\n",
      "d2d_dp2_qla = dB_dp_ql + dA_dp_ql*dT_dp_q + dd_dT_pl*d2T_dp2_q\n",
      "d2d_dp2_qln = numSlopeAr(T_sat, ds_dp_ql)\n",
      "# vap side\n",
      "dB_dp_qv = d2d_dp2_Tv + d2d_dpTv*dT_dp_q\n",
      "dA_dp_qv = d2d_dpTv + d2d_dT2_pv*dT_dp_q\n",
      "d2d_dp2_qva = dB_dp_qv + dA_dp_qv*dT_dp_q + dd_dT_pv*d2T_dp2_q\n",
      "d2d_dp2_qvn = numSlopeAr(T_sat, ds_dp_qv)\n",
      "#print(d2d_dp2_qla - d2d_dp2_qln)\n",
      "#print(d2d_dp2_qva - d2d_dp2_qvn)\n",
      "\n",
      "# E=ds_dp_T and C=ds_dT_p\n",
      "# liq side\n",
      "dE_dp_ql = d2s_dp2_Tl + d2s_dpTl*dT_dp_q\n",
      "dC_dp_ql = d2s_dpTl + d2s_dT2_pl*dT_dp_q\n",
      "d2s_dp2_qla = dE_dp_ql + dC_dp_ql*dT_dp_q + ds_dT_pl*d2T_dp2_q\n",
      "d2s_dp2_qln = numSlopeAr(p_sat, ds_dp_ql)\n",
      "# vap side\n",
      "dE_dp_qv = d2s_dp2_Tv + d2s_dpTv*dT_dp_q\n",
      "dC_dp_qv = d2s_dpTv + d2s_dT2_pv*dT_dp_q\n",
      "d2s_dp2_qva = dE_dp_qv + dC_dp_qv*dT_dp_q + ds_dT_pv*d2T_dp2_q\n",
      "d2s_dp2_qvn = numSlopeAr(p_sat, ds_dp_qv)\n",
      "#print(d2s_dp2_qla - d2s_dp2_qln)\n",
      "#print(d2s_dp2_qva - d2s_dp2_qvn)\n",
      "\n",
      "# G=dh_dT_p and K=dh_dp_T\n",
      "# liq side\n",
      "dG_dT_ql = d2h_dT2_pl + d2h_dpTl*dp_dT_q\n",
      "dK_dT_ql = d2h_dpTl + d2h_dp2_Tl*dp_dT_q\n",
      "d2h_dT2_qla = dG_dT_ql + dK_dT_ql*dp_dT_q + dh_dp_Tl*d2p_dT2_q\n",
      "d2h_dT2_qln = numSlopeAr(T_sat, dh_dT_ql)\n",
      "# vap side\n",
      "dG_dT_qv = d2h_dT2_pv + d2h_dpTv*dp_dT_q\n",
      "dK_dT_qv = d2h_dpTv + d2h_dp2_Tv*dp_dT_q\n",
      "d2h_dT2_qva = dG_dT_qv + dK_dT_qv*dp_dT_q + dh_dp_Tv*d2p_dT2_q\n",
      "d2h_dT2_qvn = numSlopeAr(T_sat, dh_dT_qv)\n",
      "#print(d2h_dT2_qla - d2h_dT2_qln)\n",
      "#print(d2h_dT2_qva - d2h_dT2_qvn)\n",
      "\n",
      "# liq side\n",
      "d2d_dh2_qla = (d2d_dT2_qla*dh_dT_ql - dd_dT_ql*d2h_dT2_qla) / dh_dT_ql**3\n",
      "d2d_dh2_qln = numSlopeAr(h_sat_liq, dd_dh_ql)\n",
      "# vap side\n",
      "d2d_dh2_qva = (d2d_dT2_qva*dh_dT_qv - dd_dT_qv*d2h_dT2_qva) / dh_dT_qv**3\n",
      "d2d_dh2_qvn = numSlopeAr(h_sat_vap, dd_dh_qv)\n",
      "#print(d2d_dh2_qla - d2d_dh2_qln)\n",
      "#print(d2d_dh2_qva - d2d_dh2_qvn)\n",
      "\n",
      "# liq side\n",
      "d2s_dh2_qla = (d2s_dT2_qla*dh_dT_ql - ds_dT_ql*d2h_dT2_qla) / dh_dT_ql**3\n",
      "d2s_dh2_qln = numSlopeAr(h_sat_liq, ds_dh_ql)\n",
      "# vap side\n",
      "d2s_dh2_qva = (d2s_dT2_qva*dh_dT_qv - ds_dT_qv*d2h_dT2_qva) / dh_dT_qv**3\n",
      "d2s_dh2_qvn = numSlopeAr(h_sat_vap, ds_dh_qv)\n",
      "#print(d2s_dh2_qla - d2s_dh2_qln)\n",
      "#print(d2s_dh2_qva - d2s_dh2_qvn)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "T_crt = 369.89\n",
        "T_trp = 85.525\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# vapor pressure, Clausius-Clapeyron\n",
      "\n",
      "# pick a point and set figure size\n",
      "mySatPoint = int(nPoints-50)\n",
      "print(\"T_sat = \" + str(T_sat[mySatPoint]))\n",
      "print(\"p_sat = \" + str(p_sat[mySatPoint]))\n",
      "plt.figure(figsize=(width,width*3/2/golden))\n",
      "\n",
      "plt.subplot(3,2,1)\n",
      "plt.plot(T_sat, p_sat, color='blue')\n",
      "#plt.yscale('log')\n",
      "plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('Pressure in Pa')\n",
      "drawTangent(T_sat, p_sat, dp_dT_q, mySatPoint)\n",
      "#print(dp_dT_q - dp_dT_qn)\n",
      "\n",
      "plt.subplot(3,2,2)\n",
      "plt.plot(p_sat, T_sat, color='blue')\n",
      "#plt.yscale('log')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('Temperature in K')\n",
      "drawTangent(p_sat, T_sat, dT_dp_q, mySatPoint)\n",
      "#print(dT_dp_q - dT_dp_qn)\n",
      "\n",
      "plt.subplot(3,2,3)\n",
      "plt.plot(T_sat, dp_dT_q, color='blue')\n",
      "#plt.yscale('log')\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('dp/dT in Pa/K')\n",
      "drawTangent(T_sat, dp_dT_q, d2p_dT2_q, mySatPoint)\n",
      "#print(d2p_dT2_q - d2p_dT2_qn)\n",
      "\n",
      "plt.subplot(3,2,4)\n",
      "plt.plot(p_sat, dT_dp_q, color='blue')\n",
      "plt.yscale('log')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('dT/dp in K/Pa')\n",
      "drawTangent(p_sat, dT_dp_q, d2T_dp2_q, mySatPoint)\n",
      "#print(d2T_dp2_q - d2T_dp2_qn)\n",
      "\n",
      "\n",
      "plt.subplot(3,2,5)\n",
      "plt.plot(T_sat, d2p_dT2_q, color='blue')\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('d2p/dT2 in Pa/K2')\n",
      "\n",
      "plt.subplot(3,2,6)\n",
      "plt.plot(p_sat, abs(d2T_dp2_q), color='blue')\n",
      "plt.yscale('log')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.grid(b=True, linestyle=':')\n",
      "plt.minorticks_on()\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('|d2T/dp2| in K/Pa2')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "T_sat = 355.942167167\n",
        "p_sat = 3298979.183\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0xda6d748>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rNsaYZIv+EJSXlxdXee9kxSBVrGJgjEmm336D3/3uBm6++UiGDv2733FsgbMk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\ndfNLHcVBm06WzjmXBOlxVHV1dXmt3+QYA0nPAfuZ2XuN5u8HjDWzzfP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v376MGDGi1LEK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\nLr009IaNGxdWTPb4A8dxygGPMegklOP4NddcPNqi2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9Edta0TLf8D3AtMSdLR27qA1941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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1bb57518>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pick a point and set figure size\n",
      "mySatPoint = int(nPoints-50)\n",
      "print(\"T_sat = \" + str(T_sat[mySatPoint]))\n",
      "print(\"p_sat = \" + str(p_sat[mySatPoint]))\n",
      "plt.figure(figsize=(width,width*3/2/golden))\n",
      "\n",
      "# d versus T\n",
      "plt.subplot(3,2,1)\n",
      "plt.plot(T_sat, d_sat_liq, color='blue')\n",
      "plt.plot(T_sat, d_sat_vap, color='red')\n",
      "#plt.plot(T_sat, (d_sat_liq+d_sat_vap)/2, color='black')\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('Density in kg/m\u00b3')\n",
      "drawTangent(T_sat, d_sat_liq, dd_dT_ql, mySatPoint)\n",
      "drawTangent(T_sat, d_sat_vap, dd_dT_qv, mySatPoint)\n",
      "\n",
      "# s versus T\n",
      "plt.subplot(3,2,2)\n",
      "plt.plot(T_sat, s_sat_liq, color='blue')\n",
      "plt.plot(T_sat, s_sat_vap, color='red')\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('Specific entropy in J/(kg\u00b7K)')\n",
      "drawTangent(T_sat, s_sat_liq, ds_dT_ql, mySatPoint)\n",
      "drawTangent(T_sat, s_sat_vap, ds_dT_qv, mySatPoint)\n",
      "\n",
      "# d versus p\n",
      "plt.subplot(3,2,3)\n",
      "plt.plot(p_sat, d_sat_liq, color='blue')\n",
      "plt.plot(p_sat, d_sat_vap, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('Density in kg/m\u00b3')\n",
      "drawTangent(p_sat, d_sat_liq, dd_dp_ql, mySatPoint)\n",
      "drawTangent(p_sat, d_sat_vap, dd_dp_qv, mySatPoint)\n",
      "#print(dd_dp_ql - numSlopeAr(p_sat, d_sat_liq))\n",
      "#print(dd_dp_qv - numSlopeAr(p_sat, d_sat_vap))\n",
      "\n",
      "# s versus p\n",
      "plt.subplot(3,2,4)\n",
      "plt.plot(p_sat, s_sat_liq, color='blue')\n",
      "plt.plot(p_sat, s_sat_vap, color='red')\n",
      "#plt.yscale('log')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('Specific entropy in J/(kg\u00b7K)')\n",
      "drawTangent(p_sat, s_sat_liq, ds_dp_ql, mySatPoint)\n",
      "drawTangent(p_sat, s_sat_vap, ds_dp_qv, mySatPoint)\n",
      "#print(ds_dp_ql - numSlopeAr(p_sat, s_sat_liq))\n",
      "#print(ds_dp_qv - numSlopeAr(p_sat, s_sat_vap))\n",
      "\n",
      "# d versus h\n",
      "plt.subplot(3,2,5)\n",
      "plt.plot(h_sat_liq, d_sat_liq, color='blue')\n",
      "plt.plot(h_sat_vap, d_sat_vap, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Specific enthalpy in J/kg')\n",
      "plt.ylabel('Density in kg/m\u00b3')\n",
      "drawTangent(h_sat_liq, d_sat_liq, dd_dh_ql, mySatPoint)\n",
      "drawTangent(h_sat_vap, d_sat_vap, dd_dh_qv, mySatPoint)\n",
      "\n",
      "# s versus h\n",
      "plt.subplot(3,2,6)\n",
      "plt.plot(h_sat_liq, s_sat_liq, color='blue')\n",
      "plt.plot(h_sat_vap, s_sat_vap, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Specific enthalpy in J/kg')\n",
      "plt.ylabel('Specific entropy in J/(kg\u00b7K)')\n",
      "drawTangent(h_sat_liq, s_sat_liq, ds_dh_ql, mySatPoint)\n",
      "drawTangent(h_sat_vap, s_sat_vap, ds_dh_qv, mySatPoint)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "T_sat = 355.942167167\n",
        "p_sat = 3298979.183\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7qj/33ecqCwZKSUnhzjvvZNWqVcycObPQrN4bZNL/rvcyS2OpqrfmMZZzcRN1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\n7v8D6bIyr6XahsEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbe30588>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Pick a point and set figure size\n",
      "mySatPoint = int(nPoints-50)\n",
      "print(\"T_sat = \" + str(T_sat[mySatPoint]))\n",
      "print(\"p_sat = \" + str(p_sat[mySatPoint]))\n",
      "plt.figure(figsize=(width,width*3/2/golden))\n",
      "\n",
      "# dd_dT versus T\n",
      "plt.subplot(3,2,1)\n",
      "plt.plot(T_sat, dd_dT_ql, color='blue')\n",
      "plt.plot(T_sat, dd_dT_qv, color='red')\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('dd/dT in kg/m\u00b3/K')\n",
      "drawTangent(T_sat, dd_dT_ql, d2d_dT2_qla, mySatPoint)\n",
      "drawTangent(T_sat, dd_dT_qv, d2d_dT2_qva, mySatPoint)\n",
      "\n",
      "# ds_dT versus T\n",
      "plt.subplot(3,2,2)\n",
      "plt.plot(T_sat, ds_dT_ql, color='blue')\n",
      "plt.plot(T_sat, ds_dT_qv, color='red')\n",
      "plt.xlabel('Temperature in K')\n",
      "plt.ylabel('ds/dT in J//(kg\u00b7K)/K')\n",
      "drawTangent(T_sat, ds_dT_ql, d2s_dT2_qla, mySatPoint)\n",
      "drawTangent(T_sat, ds_dT_qv, d2s_dT2_qva, mySatPoint)\n",
      "\n",
      "# dd_dp versus p\n",
      "plt.subplot(3,2,3)\n",
      "#plt.plot(p_sat, dd_dp_ql, color='blue')\n",
      "plt.plot(p_sat, dd_dp_qv, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('dd/dp in kg/m\u00b3/Pa')\n",
      "#drawTangent(p_sat, dd_dp_ql, d2d_dp2_qla, mySatPoint)\n",
      "drawTangent(p_sat, dd_dp_qv, d2d_dp2_qva, mySatPoint)\n",
      "\n",
      "# ds_dp versus p\n",
      "plt.subplot(3,2,4)\n",
      "plt.plot(p_sat, ds_dp_ql, color='blue')\n",
      "#plt.plot(p_sat, ds_dp_qv, color='red')\n",
      "#plt.yscale('log')\n",
      "plt.ylim([0,10*min(ds_dp_ql)])\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Pressure in Pa')\n",
      "plt.ylabel('ds/dp in J//(kg\u00b7K)/Pa')\n",
      "drawTangent(p_sat, ds_dp_ql, d2s_dp2_qla, mySatPoint)\n",
      "#drawTangent(p_sat, ds_dp_qv, d2s_dp2_qva, mySatPoint)\n",
      "\n",
      "# dd_dh versus h\n",
      "plt.subplot(3,2,5)\n",
      "plt.plot(h_sat_liq, dd_dh_ql, color='blue')\n",
      "#plt.plot(h_sat_vap, dd_dh_qv, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Specific enthalpy in J/kg')\n",
      "plt.ylabel('dd/dh')\n",
      "drawTangent(h_sat_liq, dd_dh_ql, d2d_dh2_qla, mySatPoint)\n",
      "#drawTangent(h_sat_vap, dd_dh_qv, d2d_dh2_qva, mySatPoint)\n",
      "\n",
      "# ds_dh versus h\n",
      "plt.subplot(3,2,6)\n",
      "plt.plot(h_sat_liq, ds_dh_ql, color='blue')\n",
      "#plt.plot(h_sat_vap, ds_dh_qv, color='red')\n",
      "plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
      "plt.xlabel('Specific enthalpy in J/kg')\n",
      "plt.ylabel('ds/dh')\n",
      "drawTangent(h_sat_liq, ds_dh_ql, d2s_dh2_qla, mySatPoint)\n",
      "#drawTangent(h_sat_vap, ds_dh_qv, d2s_dh2_qva, mySatPoint)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "T_sat = 355.942167167\n",
        "p_sat = 3298979.183\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UtlTns9X9fPFnCwuDpfgP+uT1sktF71XlOw0aBNNyNmwITZtCkybBa/J6Zfsq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NFHH82FF17I//3f/8UdTpUU95wGMzsKGO7uPaPzoQDuPiqhzJ+AN9z96eh8LlDg7sui832Bv7v7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\nAx3c/euk62rvpURr1oR5EM89F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       "text": [
        "<matplotlib.figure.Figure at 0x1bb56f98>"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}