{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Planetesimal Without a Core\n", "\n", "This example shows step by step how to set up and run a model of a cooling\n", "planetesimal without a core. This example uses constant\n", "material properties in the mantle, see [Murphy Quinlan et al. (2021)](https://doi.org/10.1029/2020JE006726) and references therein.\n", "\n", "While the material properties used in this example are suitable for modelling\n", "the olivine mantle of a differentiated body, the model set-up may be useful\n", "for modelling undifferentiated meteorite parent bodies with appropriate\n", "thermal properties.\n", "\n", "This model set-up also allows for comparison to an analytical solution for\n", "conductive cooling in a sphere (see [Murphy Quinlan et al. (2021)](https://doi.org/10.1029/2020JE006726)).\n", "\n", "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." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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:\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "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", "except for the `core_size_factor` value:\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "timestep = 1E11 # s\n", "r_planet = 250_000.0 # m\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": [ "As we want to model an undifferentiated body, we don't want to include\n", "a core in our model set up. Because we wanted it to be easy to swap\n", "between different model set-ups, the `numerical_methods.discretisation()`\n", "function still requires a core object to be instantiated. When the boundary\n", "conditions are set up correctly, the core object does not interact with\n", "the mantle and does not influence the cooling. Setting `core_size_factor=0`\n", "can lead to scalar overflow errors, which can be avoided by setting\n", "`core_size_factor` to a small, non-zero number that is smaller than the grid\n", "spacing (so the core_temperature_array has dimension 0 along radius):\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "core_size_factor = 0.001" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example, we won't save any outputs. If you're working\n", "on your local machine, you can specify the folder you want to\n", "save your outputs to, and check that this folder exists on your\n", "machine (if it doesn't, a folder will be created):\n", "\n", " folder = 'workflow'\n", " load_plot_save.check_folder_exists(folder)\n", "\n", "This folder can also be specified in the parameters file.\n", "Any variable loaded from the parameters file can be\n", "overwritten before the model runs, which is useful if looping over\n", "a parameter space.\n", "\n", "The `setup_functions.set_up()` function creates empty arrays to\n", "be filled with resulting temperatures:\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "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\n", "latent = []" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check that our \"no-core\" set-up has worked:\n", "\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 126229)\n" ] } ], "source": [ "print(core_temperature_array.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we instantiate the core object. As explained previously,\n", "this model set up does not include a core, but in order to make\n", "the code as modular as possible, a core object still needs to be passed\n", "in to the main timestepping function, `numerical_methods.discretisation`.\n", "We've just copied across the default arguments for convenience:\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "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. The default is to have constant\n", "values, so we don't require any arguments for this example:\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "(mantle_conductivity,\n", " mantle_heatcap,\n", " mantle_density) = mantle_properties.set_up_mantle_properties()" ] }, { "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": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.0\n" ] } ], "source": [ "print(mantle_conductivity.getk())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If temperature dependent properties are used, temperature can be passed in\n", "as an argument to return the value at that temperature.\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 the\n", "surface, and a zero-flux condition at the bottom to ensure symmetry.\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "top_mantle_bc = numerical_methods.surface_dirichlet_bc\n", "bottom_mantle_bc = numerical_methods.cmb_neumann_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", "# Note that this function call is the exact same as in the default constant\n", "# and variable cases that included a core.\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.\n", "\n", "Our next step is to calculate cooling rates for the body. Usually, we\n", "calculate cooling rates for the mantle and the core in the same way.\n", "As the core temperature array is empty, but we still need a\n", "`core_cooling_rates` array to pass to our plotting function, we just set\n", "`core_cooling_rates = core_temperature_array` to save time computing a\n", "meaningless empty array:\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "mantle_cooling_rates = analysis.cooling_rate(mantle_temperature_array, timestep)\n", "core_cooling_rates = core_temperature_array" ] }, { "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": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "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": [ "Results can be saved in the same way as for the constant and variable\n", "properties examples.\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 }