{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Heat Capacity\n", "\n", "The heat capacity of a material/object describes the temperature change of the object if heat is added to (or removed from) the object. \n", "\n", "In geothermics, the heat capacity is of particular interest, as we extract heat from a system (the geothermal reservoir) and thus cool it. The heat capacity directly tells us, how much energy we theoretically can extract, if the reservoir body is cooled by X Kelvin. For example, imagine a cubic kilometer granite with a temperature of 200 °C. If this 1 km³ is cooled down by 20 °C (so 200 °C -> 180 °C) the available energy equals 1 billion litres oil. \n", "
Or: Cooling down 1 km³ granite by 20 °C provides about 10 MW electrical power for a period of about 20 years.
\n", "\n", "Heat capacity changes over temperature ranges. But if the temperature change is sufficiently small, it can be defined as: \n", "$$ C = \\frac{Q}{\\Delta T} \\qquad \\textrm{in $\\frac{J}{K}$} $$ \n", "\n", "It is an extensive quantity, meaning it is size-dependent (being the size of our observed system). Logical, if you imagine 1 cm³ granite cooled down by 20 °C would provide way less energy.\n", "\n", "Thus we usually look at heat capacity as an intensive property, which is independent of the extent of our system. For doing this, we have to relate heat capacity to a quantity, such as mass or volume. The **specific heat capacity** $c_p$ (related to mass) is maybe the most often used formulation. It has the unit $\\frac{J}{kg K}$. The specific heat capacity (also isobaric heat capacity), can easily be measured in a lab (it is one response function, see the notebook about Legendre transformation and Maxwell relations). \n", "\n", "In this notebook, we look at measurements of the specific heat capacity over a temperature range and fit an expression (a polynom), which describes the change in specific heat capacity with temperature for a particular rock sample.\n", "\n", "The specific heat capacity was measured in a temperature sweep from 40 °C to 250 °C. Measured specific heat capacities and temperatures are used for a nonlinear regression in order to fit a polynom of form (Hirono and Hamada, 2010): \n", "\n", "$$c_p(T) = a + bT + cT^2 + dT^{-1} + eT^{-2}$$ \n", "\n", "with $c_p$ in J kg$^{-1}$ K$^{-1}$ and $T$ in K. \n", "\n", "We can also use a simpler equation to fit the $c_p$ data (Clauser 2003): \n", "\n", "$$c_p(T) = a + bT + cT^2$$ \n", "\n", "with $c_p$ in J kg$^{-1}$ K$^{-1}$ and $T$ in °C. \n", "\n", "Here we will use the formulation of Hirono and Hamada." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import libraries\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as p\n", "%matplotlib inline\n", "\n", "# for plotting stile, we utilize the seaborn library\n", "import seaborn as sns\n", "sns.set_context('talk')\n", "sns.set_style('ticks')\n", "from scipy import optimize" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# define the function\n", "def fun(x,a,b,c,d,e):\n", " return a + b*x + c*x**2 + d/x + e/x**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `fun` returns the specific heat capacity with known parameters a to e. x in the function is a temperature range, i.e. a vector. However, for calculating the specific heat capacity over a certain temperature range, we need to assess the coefficients a to e. \n", "We do this by fitting the polynom to measured data. First off, let's import the data and give it a look:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Temp[C]cp[J/kgK]
040.0881.113
145.0889.392
250.0896.739
355.0904.091
460.0911.635
\n", "
" ], "text/plain": [ " Temp[C] cp[J/kgK]\n", "0 40.0 881.113\n", "1 45.0 889.392\n", "2 50.0 896.739\n", "3 55.0 904.091\n", "4 60.0 911.635" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = p.read_csv('data/03_cp.dat')\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The specific heat capacity of this sample increases with temperature almost linearly, so a linear fit might give satisfactory results. Let's have a look:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "from pylab import *" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vr6+cnJzUsGFDvffeewX3SQAAfyo2NtZW+Hx9fbVixQrVrFnT4FQACovJ+u99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\nlT4RERERE1DpExERETGBfwGjdVoJbeq+ugAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = linspace(1,100,100)\n", "y = 2*x +4\n", "plot(x,y,'k--')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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evbp1XwaLxUJUVJR16oKISHFlr21495Ba7D10MdvGD1pDo2Q5dOgQs2bNyrZb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\nAMB/8R2mGqxkp/YLHcnI1qqNB6p4RAAAr3DunI7eU1QsSdJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axs = plt.subplots(1,2,figsize=[12,6], sharey=True)\n", "axs[0].plot(data['Temp[C]'], data['cp[J/kgK]'], 'o')\n", "axs[0].set_xlabel('Temperature [°C]')\n", "axs[0].set_ylabel('c$_p$ [J/(kgK)]')\n", "\n", "# fit a linear equation to the data and calculate a simulated specific heat capacity cp_s\n", "m, b = np.polyfit(data['Temp[C]'], data['cp[J/kgK]'], 1) \n", "temp_s = np.linspace(40,250,100)\n", "cp_s = temp_s*m + b\n", "\n", "axs[1].plot(data['Temp[C]'], data['cp[J/kgK]'], 'o', label='data')\n", "axs[1].plot(temp_s, cp_s, 'r-',alpha=0.8, label='linear fit')\n", "axs[1].set_xlabel('Temperature [°C]')\n", "axs[1].legend()\n", "axs[1].text(42, 1100, \n", " 'c$_p$(T) = {0:.3f} T + {1:.3f} '\n", " .format(*tuple((m,b))),fontsize=13)\n", "plt.tight_layout()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While the fit is not too bad, it does not capture the change in specific heat capacities at lower temperatures, and likely also at higher temperatures over 250 °C. So let's look at a more complex fit using the function above:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Tdat = data['Temp[C]']+273.15 # for the function, we need temperatures in Kelvin\n", "cpdat = data['cp[J/kgK]']\n", "\n", "# after defining the data, we perform a nonlinear regression using optimize.curve_fit\n", "popt, pcov = optimize.curve_fit(fun,Tdat,cpdat)\n", "\n", "temp_sK = temp_s+273.15 # regression temperature must be in K for the function\n", "cpsim = fun(temp_sK,popt[0],popt[1],popt[2],popt[3],popt[4]) # fitted cp" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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atat7v969e2MYBhs2bLgIn0ZEpHZanJpORuYJbvnxMxL2b/HoO2ANYdGNd0Jo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\nCiW2/v7+mM1Fb/babDby8/MrHZSI1A7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6ZtSaTUZienZk8039CPj5KDbNqRURERGRq5zB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(nrows=1,ncols=1,figsize=(7,7))\n", "ax.plot(Tdat,cpdat,'o', label=\"measured data\") # plot just every nth point\n", "ax.plot(temp_sK,cpsim,'r-',linewidth=4, alpha=0.7, label=\"nonlinear fit\")\n", "ax.legend(fontsize=16,loc=4)\n", "ax.set_xlabel(\"Temperature [K]\")\n", "ax.set_ylabel(\"specific heat capacities J kg$^{-1}$ K$^{-1}$\")\n", "#m.rcParams.update({'font.size':15})\n", "plt.tight_layout()\n", "ax.text(540, 1125, \n", " 'c$_p$(T) = {0:.3f} + {1:.3f} T + {2:.5f} T$^2$+ {3:.3f} T$^-$$^1$ + {4:.3f} T$^-$$^2$'\n", " .format(*tuple(popt)),fontsize=13)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In contrast to the linear fit, the function of Hirono and Hamada fits the measured data well at lower temperatures, although there is still a slight deviation seen at higher temperatures. Nonetheless, this equation provides a satisfactory fit." ] } ], "metadata": { "hide_input": false, "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": "3.6.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "base_numbering": 1, "nav_menu": { "height": "21px", "width": "253px" }, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }