multifluid.ipynb

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   "source": [
    "# Multi-fluid EOS\n",
    "\n",
    "Peering into the innards of teqp"
   ]
  },
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   "source": [
    "import timeit, json\n",
    "import pandas\n",
    "import numpy as np\n",
    "import teqp\n",
    "teqp.__version__"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Ancillary Equations"
   ]
  },
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   "source": [
    "Ancillary equations are provided along with multiparameter equations of state. The give a good *approximation* to the phase equilibrium densities.  There are routines in teqp to use the ancillary equations provided with the EOS. First a class containing the ancillary equations is obtained, then methods on that class are called"
   ]
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   "source": [
    "model = teqp.build_multifluid_model([\"Methane\"], teqp.get_datapath())\n",
    "anc = model.build_ancillaries()\n",
    "T = 100.0 # [K]\n",
    "rhoL, rhoV = anc.rhoL(T), anc.rhoV(T)\n",
    "print('Densities are:', rhoL, rhoV, 'mol/m^3')"
   ]
  },
  {
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   "source": [
    "But those densities do not correspond to the *true* phase equilibrium solution, so we need to polish the solution:"
   ]
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   "source": [
    "Niter = 10\n",
    "rhoLtrue, rhoVtrue = model.pure_VLE_T(T, rhoL, rhoV, Niter)\n",
    "print('VLE densities are:', rhoLtrue, rhoVtrue, 'mol/m^3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And looking the densities, they are slightly different after the phase equilibrium calculation"
   ]
  },
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   "metadata": {},
   "source": [
    "## Pure fluid loading"
   ]
  },
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   "source": [
    "# By default teqp looks for fluids relative to the set of fluids in ROOT/dev/fluids\n",
    "# The name (case-sensitive) should match the .json file, without the json extension.\n",
    "%timeit model = teqp.build_multifluid_model([\"Methane\"], teqp.get_datapath())"
   ]
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   "source": [
    "# And if you provide valid aliases, alias lookup will be used to resolve the name\n",
    "# But beware, this is rather a lot slower than the above because all fluid files need to be read\n",
    "# in to build the alias map\n",
    "%timeit model = teqp.build_multifluid_model([\"n-C1H4\"], teqp.get_datapath())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, how to make it faster? Only do it once and cache"
   ]
  },
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   "source": [
    "# Here is the set of possible aliases to absolute paths of files\n",
    "# Building this map takes a little while (somewhat faster in C++) due to all the file reads\n",
    "# If you know your files will not change, good idea to build this alias map yourself.\n",
    "%timeit aliasmap = teqp.build_alias_map(teqp.get_datapath())\n",
    "aliasmap = teqp.build_alias_map(teqp.get_datapath())\n",
    "list(aliasmap.keys())[0:10] # the first 10 aliases in the dict"
   ]
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   "source": [
    "# Then load the absolute paths from the alias map, \n",
    "# which will guarantee that you hit exactly what you were looking for,\n",
    "# resolving aliases as needed\n",
    "identifiers = [aliasmap[n] for n in [\"n-C1H4\"]]\n",
    "%timeit model = teqp.build_multifluid_model(identifiers, teqp.get_datapath())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At some point soon teqp will support in-memory loading of JSON data for the pure components, without requiring reads from the operating system"
   ]
  },
  {
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   "source": [
    "# And you can also load the JSON that teqp is loading for the pure fluids\n",
    "pureJSON = teqp.collect_component_json(['Neon','Hydrogen'], teqp.get_datapath())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mixture model loading"
   ]
  },
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   "source": [
    "# Load the default JSON for the binary interaction parameters\n",
    "BIP = json.load(open(teqp.get_datapath()+'/dev/mixtures/mixture_binary_pairs.json'))"
   ]
  },
  {
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   "source": [
    "# You can obtain interaction parameters either by pairs of names, where name is the name that teqp uses, the [\"INFO\"][\"NAME\"] field\n",
    "params, swap_needed = teqp.get_BIPdep(BIP, ['Methane','Ethane'])\n",
    "params"
   ]
  },
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   "source": [
    "# Or also by CAS#\n",
    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'])\n",
    "params"
   ]
  },
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   "source": [
    "# But mixing is not allowed\n",
    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','Ethane'])\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimation of interaction parameters\n",
    "\n",
    "Estimation of interaction parameters can be used when no mixture model is present.  The ``flags`` keyword argument allows the user to control how estimation is applied. The ``flags`` keyword argument should be a dictionary, with keys of ``\"estimate\"`` to provide the desired estimation scheme as-needed. For now, the only allowed estimation scheme is ``Lorentz-Berthelot``. \n",
    "\n",
    "If it is desired to force the estimation, the ``\"force-estimate\"`` to force the use of the provided esimation scheme for all binaries, even when one is available. The value associated with ``\"force-estimate\"`` is ignored."
   ]
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   "source": [
    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'force-estimate':'yes', 'estimate': 'Lorentz-Berthelot'})\n",
    "params"
   ]
  },
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   "source": [
    "# And without the force, the forcing is ignored\n",
    "params, swap_needed = teqp.get_BIPdep(BIP, ['74-82-8','74-84-0'], flags={'estimate': 'Lorentz-Berthelot'})\n",
    "params"
   ]
  },
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   "source": [
    "# And the same flags can be passed to the multifluid model constructor\n",
    "model = teqp.build_multifluid_model(\n",
    "    ['74-82-8','74-84-0'], \n",
    "    teqp.get_datapath(), \n",
    "    flags={'force-estimate':'yes', 'estimate': 'Lorentz-Berthelot'})"
   ]
  }
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