{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Variable Properties\n", "\n", "This example shows step by step how to set up and run a model of a cooling\n", "planetesimal without using a parameters file. This example uses temperature\n", "dependent material properties in the mantle and reproduces the results of\n", "[Murphy Quinlan et al. (2021)](https://doi.org/10.1029/2020JE006726).\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we import the required `pytesimal` modules. To allow this\n", "example to run without the package installed, we import these\n", "modules from the `context.py` script, which adds the package\n", "directory to the python path. This isn't required once the\n", "package is installed; instead modules can be loaded with\n", "`import pytesimal.numerical_methods` etc.\n", "\n", "As we're setting this up step-by-step instead of using the\n", "`pytesimal.quick_workflow` module, we need to import a\n", "selection of modules:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Roundabout import without package installed\n", "from context import setup_functions\n", "from context import load_plot_save\n", "from context import numerical_methods\n", "from context import analysis\n", "from context import core_function\n", "from context import mantle_properties" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of creating and loading a parameter file, we're going to\n", "define variables here. The values match those of the constant\n", "thermal properties case in Murphy Quinlan et al. (2021):\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "timestep = 1E11 # s\n", "r_planet = 250_000.0 # m\n", "core_size_factor = 0.5 # fraction of r_planet\n", "reg_fraction = 0.032 # fraction of r_planet\n", "max_time = 400 # Myr\n", "temp_core_melting = 1200.0 # K\n", "core_cp = 850.0 # J/(kg K)\n", "core_density = 7800.0 # kg/m^3\n", "temp_init = 1600.0 # K\n", "temp_surface = 250.0 # K\n", "core_temp_init = 1600.0 # K\n", "core_latent_heat = 270_000.0 # J/kg\n", "kappa_reg = 5e-8 # m^2/s\n", "dr = 1000.0 # m" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `setup_functions.set_up()` function creates empty arrays to\n", "be filled with resulting temperatures:\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "(r_core,\n", " radii,\n", " core_radii,\n", " reg_thickness,\n", " where_regolith,\n", " times,\n", " mantle_temperature_array,\n", " core_temperature_array) = setup_functions.set_up(timestep,\n", " r_planet,\n", " core_size_factor,\n", " reg_fraction,\n", " max_time,\n", " dr)\n", "\n", "# We define an empty list of latent heat that will\n", "# be filled as the core freezes\n", "latent = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we instantiate the core object. This will keep track of the\n", "temperature of the core as the model runs, cooling as heat\n", "is extracted across the core-mantle boundary. This simple\n", "eutectic core model doesn't track inner core growth, but\n", "this is still a required argument to allow for future\n", "incorporation of more complex core objects:\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "core_values = core_function.IsothermalEutecticCore(\n", " initial_temperature=core_temp_init,\n", " melting_temperature=temp_core_melting,\n", " outer_r=r_core,\n", " inner_r=0,\n", " rho=core_density,\n", " cp=core_cp,\n", " core_latent_heat=core_latent_heat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define the mantle properties. We want to use temperature-dependent\n", "properties, so we need to specify this:\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "(mantle_conductivity,\n", " mantle_heatcap,\n", " mantle_density) = mantle_properties.set_up_mantle_properties(\n", " cond_constant='n',\n", " density_constant='n',\n", " heat_cap_constant='n'\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can check (or change) the value of these properties after they've been\n", "set up using one of the `MantleProperties` methods:\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Mantle conductivity 3.411005666195278 W/(m K)\n" ] } ], "source": [ "print(f' Mantle conductivity {mantle_conductivity.getk()} W/(m K)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When temperature dependent properties are used, temperature can be passed in\n", "as an argument to return the value at that temperature. The default\n", "temperature is 295 K, so if no temperature argument is passed, the\n", "function is evaluated at `T=295`.\n", "\n", "We need to set up the boundary conditions for the mantle. For this example,\n", "we're using fixed temperature boundary conditions at both the\n", "surface and the core-mantle boundary.\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "top_mantle_bc = numerical_methods.surface_dirichlet_bc\n", "bottom_mantle_bc = numerical_methods.cmb_dirichlet_bc\n", "\n", "# Now we let the temperature inside the planestesimal evolve. This is the\n", "# slowest part of the code, because it has to iterate over all radii and\n", "# time.\n", "# This will take a minute or two!\n", "# The mantle property objects are passed in in the same way as in the\n", "# example with constant thermal properties.\n", "\n", "(mantle_temperature_array,\n", " core_temperature_array,\n", " latent,\n", " ) = numerical_methods.discretisation(\n", " core_values,\n", " latent,\n", " temp_init,\n", " core_temp_init,\n", " top_mantle_bc,\n", " bottom_mantle_bc,\n", " temp_surface,\n", " mantle_temperature_array,\n", " dr,\n", " core_temperature_array,\n", " timestep,\n", " r_core,\n", " radii,\n", " times,\n", " where_regolith,\n", " kappa_reg,\n", " mantle_conductivity,\n", " mantle_heatcap,\n", " mantle_density)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function fills the empty arrays produced by\n", "`setup_functions.set_up()` with calculated temperatures for the mantle and\n", "core.\n", "\n", "Now we can use the `pytesimal.analysis` module to find out more\n", "about the model run. We can check when the core was freezing,\n", "so we can compare this to the cooling history of meteorites\n", "and see whether they can be expected to record magnetic remnants\n", "of a core dynamo:\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "(core_frozen,\n", " times_frozen,\n", " time_core_frozen,\n", " fully_frozen) = analysis.core_freezing(core_temperature_array,\n", " max_time,\n", " times,\n", " latent,\n", " temp_core_melting,\n", " timestep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, we can calculate arrays of cooling rates from the temperature arrays:\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "mantle_cooling_rates = analysis.cooling_rate(mantle_temperature_array,\n", " timestep)\n", "core_cooling_rates = analysis.cooling_rate(core_temperature_array,\n", " timestep)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Meteorite data (the diameter of 'cloudy-zone particles') can be used to\n", "estimate the rate at which the meteorites cooled through a specific\n", "temperature (C. W. Yang et al., 1997). The\n", "`analysis.cooling_rate_cloudyzone_diameter` function calculates the cooling\n", "rate in K/Myr, while the `analysis.cooling_rate_to_seconds` function\n", "converts this to K/s which allows comparison to our result arrays.\n", "\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "d_im = 147 # cz diameter in nm\n", "d_esq = 158 # cz diameter in nm\n", "\n", "imilac_cooling_rate = analysis.cooling_rate_to_seconds(\n", " analysis.cooling_rate_cloudyzone_diameter(d_im))\n", "esquel_cooling_rate = analysis.cooling_rate_to_seconds(\n", " analysis.cooling_rate_cloudyzone_diameter(d_esq))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use this cooling rate information to find out how deep within their\n", "parent bodies these meteorites originally formed, and when they passed\n", "through the temperature of tetrataenite formation (when magnetism\n", "can be recorded). The `analysis.meteorite_depth_and_timing()` function\n", "returns the source depth of the meteorite material in the parent body\n", "based on the metal cooling rates at 800 K (as a depth from surface in km and\n", "as a radial value from the center of the planet in m), the time that the\n", "meteorite cools through the tetrataenite formation temperature in\n", "comparison to the core crystallisation period, and a string defining\n", "this relation between paleomagnetism recording and dynamo activity:\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Imilac depth: 61.0; Imilac timing: Core has not started solidifying yet\n", "Esquel depth: 68.0; Esquel timing: Core has started solidifying\n" ] } ], "source": [ "(im_depth,\n", " im_string_result,\n", " im_time_core_frozen,\n", " im_Time_of_Crossing,\n", " im_Critical_Radius) = analysis.meteorite_depth_and_timing(\n", " imilac_cooling_rate,\n", " mantle_temperature_array,\n", " mantle_cooling_rates,\n", " radii,\n", " r_planet,\n", " core_size_factor,\n", " time_core_frozen,\n", " fully_frozen,\n", " dr=1000,\n", ")\n", "\n", "(esq_depth,\n", " esq_string_result,\n", " esq_time_core_frozen,\n", " esq_Time_of_Crossing,\n", " esq_Critical_Radius) = analysis.meteorite_depth_and_timing(\n", " esquel_cooling_rate,\n", " mantle_temperature_array,\n", " mantle_cooling_rates,\n", " radii,\n", " r_planet,\n", " core_size_factor,\n", " time_core_frozen,\n", " fully_frozen,\n", " dr=1000,\n", ")\n", "\n", "print(f\"Imilac depth: {im_depth}; Imilac timing: {im_string_result}\")\n", "print(f\"Esquel depth: {esq_depth}; Esquel timing: {esq_string_result}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you need to save the meteorite results, they can be saved to a dictionary\n", "which can then be passed to the `load_plot_save.save_params_and_results`.\n", "This allows for any number of meteorites to be analysed and only the\n", "relevant data stored:\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "meteorite_results_dict = {'Esq results':\n", " {'depth': esq_depth,\n", " 'text result': esq_string_result},\n", " 'Im results':\n", " {'depth': im_depth,\n", " 'text result': im_string_result,\n", " 'critical radius': im_Critical_Radius}}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get an overview of the cooling history of the body, it's very useful\n", "to plot the temperatures and cooling rates as a heatmap through time.\n", "In order to plot the results, we need to define a figure height and width,\n", "then call `load_plot_save.plot_temperature_history()`,\n", "`load_plot_save.plot_coolingrate_history()` or `load_plot_save.two_in_one()`.\n", "These functions convert the cooling rate from K/timestep to K/Myr to make\n", "the results more human-readable.\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig_w = 6\n", "fig_h = 9\n", "\n", "load_plot_save.two_in_one(\n", " fig_w,\n", " fig_h,\n", " mantle_temperature_array,\n", " core_temperature_array,\n", " mantle_cooling_rates,\n", " core_cooling_rates, )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are a few formats or ways to save the results. The temperature and\n", "cooling rate arrays can be saved as compressed `.npz` arrays, to be loaded\n", "at a later time and replotted/new meteorite formation depths calculated etc.\n", "The input parameters can be saved as a `.json` file, which allows the run to\n", "be documented and provides all the metadata needed to reproduce the results.\n", "For either of these, a results folder and results filename is needed. The\n", "folder can be defined relative to the working directory, or with an absolute\n", "path. An absolute path usually results in less confusion!\n", "\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# define folder and check it exists:\n", "folder = 'workflow'\n", "load_plot_save.check_folder_exists(folder)\n", "# define a results filename prefix:\n", "result_filename = 'variable_workflow_results'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The result arrays can now be saved:\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "load_plot_save.save_result_arrays(result_filename,\n", " folder,\n", " mantle_temperature_array,\n", " core_temperature_array,\n", " mantle_cooling_rates,\n", " core_cooling_rates)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to save the result parameter file, we also need to define a\n", "`run_ID`, a descriptive string to identify the model run, and clarify\n", "whether we used constant or variable thermal properties:\n", "\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "run_ID = 'Example run with default variable properties'\n", "cond_constant = 'n'\n", "density_constant = 'n'\n", "heat_cap_constant = 'n'\n", "\n", "load_plot_save.save_params_and_results(\n", " result_filename, run_ID, folder, timestep, r_planet, core_size_factor,\n", " reg_fraction, max_time, temp_core_melting, mantle_heatcap.getcp(),\n", " mantle_density.getrho(), mantle_conductivity.getk(), core_cp, core_density,\n", " temp_init, temp_surface, core_temp_init, core_latent_heat,\n", " kappa_reg, dr, cond_constant, density_constant,\n", " heat_cap_constant, time_core_frozen, fully_frozen,\n", " meteorite_results=meteorite_results_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This results file can then be loaded as a parameter file if you want to\n", "repeat the same set up.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 1 }