{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Heat Capacity of Cementite ($Fe_3C$)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Bengt Hallstedt, Dejan Djurovic, Jörg von Appen, Richard Dronskowski, Alexey Dick, Fritz Körmann, Tilmann Hickel, Jörg Neugebauer, Thermodynamic properties of cementite, Calphad, Volume 34, Issue 1, March 2010, Pages 129-133, ISSN 0364-5916, http://dx.doi.org/10.1016/j.calphad.2010.01.004. (http://www.sciencedirect.com/science/article/pii/S0364591610000052)\n", "\n", "The TDB file used here differs slightly from the published TDB to ensure compatibility with pycalphad's TDB parser. All phases except cementite are omitted. The numerical results should be the same." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TDB = \"\"\"\n", " ELEMENT C GRAPHITE 12.011 1054.0 5.7423 ! \n", " ELEMENT FE BCC_A2 55.847 4489.0 27.2797 ! \n", " TYPE_DEFINITION % SEQ * !\n", " TYPE_DEFINITION A GES AMEND_PHASE_DESCRIPTION @ MAGNETIC -3 0.28 !\n", " PHASE CEMENTITE_D011 %A 2 3 1 !\n", " CONSTITUENT CEMENTITE_D011 : FE : C : !\n", " PARAMETER G(CEMENTITE_D011,FE:C;0) 0.01 +GFECEM; 6000 N !\n", " PARAMETER TC(CEMENTITE_D011,FE:C;0) 0.01 485.00; 6000 N !\n", " PARAMETER BMAGN(CEMENTITE_D011,FE:C;0) 0.01 1.008; 6000 N !\n", " FUNCTION GFECEM 0.01 +11369.937746-5.641259263*T-8.333E-6*T**4;\n", " 43.00 Y +11622.647246-59.537709263*T+15.74232*T*LN(T)\n", " -0.27565*T**2;\n", " 163.00 Y -10195.860754+690.949887637*T-118.47637*T*LN(T)\n", " -0.0007*T**2+590527*T**(-1);\n", " 6000.00 N !\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Do some initial setup, including reading the database." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Only needed in a Jupyter Notebook\n", "%matplotlib inline\n", "# Optional plot styling\n", "import matplotlib\n", "matplotlib.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from pycalphad import Database, calculate\n", "\n", "db = Database(TDB)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute the molar heat capacity at all temperatures from 1K to 2000K with a step size of 0.1K.\n", "\n", "We do this with the `calculate` routine instead of `equilibrium` because the cementite phase has zero internal degrees of freedom. Since there's nothing to minimize, we can do the computation faster with `calculate`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "result = calculate(db, ['FE', 'C'], 'CEMENTITE_D011', T=(1, 2000, 0.1), P=101325, N=1, output='heat_capacity')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Note: 4 moles of atoms per formula unit (Fe3C1). That's why we multiply times 4\n", "fig = plt.figure(figsize=(9,6))\n", "fig.gca().set_xlabel('Temperature (K)')\n", "fig.gca().set_ylabel('Isobaric Heat Capacity (J/mol-formula-K)')\n", "fig.gca().plot(result['T'], np.squeeze(4.0 * result['heat_capacity']))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }