From b572494c817c6f8ca9d6c2b87c73dc5980c19b8f Mon Sep 17 00:00:00 2001 From: Nick Wogan Date: Mon, 3 Jun 2024 11:30:19 -0700 Subject: [PATCH] settings file can adjust default condensation params --- examples/ModernEarth/settings.yaml | 3 + examples/ModernEarth/settings_Atmosphere.yaml | 3 + examples/using_VULCAN_reactions.ipynb | 390 ------------------ src/input/photochem_input_read.f90 | 11 + src/photochem_types.f90 | 26 +- src/photochem_types_create.f90 | 57 +++ tests/testevo.f90 | 6 +- tests/testrun.f90 | 4 - 8 files changed, 92 insertions(+), 408 deletions(-) delete mode 100644 examples/using_VULCAN_reactions.ipynb diff --git a/examples/ModernEarth/settings.yaml b/examples/ModernEarth/settings.yaml index 98b8c0e..22b328e 100644 --- a/examples/ModernEarth/settings.yaml +++ b/examples/ModernEarth/settings.yaml @@ -35,6 +35,9 @@ planet: # If true, then water will condense. water-condensation: false +particles: +- {name: H2Oaer, RH-condensation: 0.5} + # Specifies boundary conditions. If a species is not specified, then # the model assumes zero-flux boundary conditions at the top and # bottom of the atmosphere diff --git a/examples/ModernEarth/settings_Atmosphere.yaml b/examples/ModernEarth/settings_Atmosphere.yaml index c222eab..8da0cc7 100644 --- a/examples/ModernEarth/settings_Atmosphere.yaml +++ b/examples/ModernEarth/settings_Atmosphere.yaml @@ -37,6 +37,9 @@ planet: # If true, then water will condense. water-condensation: false +particles: +- {name: H2Oaer, RH-condensation: 0.5} + # Specifies boundary conditions. If a species is not specified, then # the model assumes zero-flux boundary conditions at the top and # bottom of the atmosphere diff --git a/examples/using_VULCAN_reactions.ipynb b/examples/using_VULCAN_reactions.ipynb deleted file mode 100644 index 8a52a17..0000000 --- a/examples/using_VULCAN_reactions.ipynb +++ /dev/null @@ -1,390 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0.3.18\n" - ] - } - ], - "source": [ - "from photochem import __version__\n", - "print(__version__)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using VULCAN reaction networks\n", - "\n", - "This notebook shows how to use reaction networks from [VULCAN](https://github.com/exoclime/VULCAN), which is a different photochemical model.\n", - "\n", - "First, we download VULCAN from Github:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "CompletedProcess(args=['rm', '-rf', './VULCAN/.git'], returncode=0)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import subprocess\n", - "\n", - "subprocess.run(\"rm -rf ./VULCAN\".split())\n", - "subprocess.run(\"git clone --depth=1 https://github.com/exoclime/VULCAN.git\".split())\n", - "subprocess.run(\"rm -rf ./VULCAN/.git\".split())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we use the `vulcan2yaml` function to convert the `SNCHO_full_photo_network` reaction network into the Photochem yaml format.\n", - "\n", - "`vulcan2yaml` is not perfect. The main issue is that VULCAN and Photochem have different databases of photolysis cross sections. So `vulcan2yaml` only includes photolysis reactions with data." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from photochem.utils import vulcan2yaml\n", - "\n", - "vulcan2yaml(\"VULCAN/thermo/SNCHO_full_photo_network.txt\",\\\n", - " \"VULCAN/thermo/all_compose.txt\", \\\n", - " outfile = \"SNCHO_full_photo_network.yaml\",\\\n", - " vulcan_nasa9_data_folder=\"VULCAN/thermo/NASA9\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we need to put together a settings file which will work with the new network. Here I use the boundary conditions in `VULCAN/atm/BC_bot_Earth.txt`." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "settings_file = \\\n", - "\"\"\"\n", - "atmosphere-grid:\n", - " bottom: 0.0\n", - " top: 1.0e7\n", - " number-of-layers: 200\n", - "\n", - "photolysis-grid:\n", - " regular-grid: true\n", - " lower-wavelength: 92.5\n", - " upper-wavelength: 855.0\n", - " number-of-bins: 200\n", - "\n", - "planet:\n", - " use-background-gas: true\n", - " background-gas: N2\n", - " surface-pressure: 1.013\n", - " planet-mass: 5.972e27\n", - " planet-radius: 6.371e8\n", - " surface-albedo: 0.25\n", - " solar-zenith-angle: 60.0\n", - " hydrogen-escape:\n", - " type: diffusion limited\n", - " water:\n", - " fix-water-in-troposphere: true\n", - " relative-humidity: manabe\n", - " gas-rainout: true\n", - " rainfall-rate: 1 # relative to modern earth's rainfall rate\n", - " tropopause-altitude: 1.1e6 # cm. required if gas-rainout or fix-water-in-troposphere\n", - " water-condensation: true\n", - " condensation-rate: {A: 1.0e-5, rhc: 0.01, rh0: 0.015}\n", - "\n", - "boundary-conditions:\n", - "- name: CO2\n", - " lower-boundary: {type: mix, mix: 4e-4}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: O2\n", - " lower-boundary: {type: mix, mix: 0.2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: Ar\n", - " lower-boundary: {type: mix, mix: 9.34e-3}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: CO\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.01, flux: 3.7e11, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: CH4\n", - " lower-boundary: {type: flux, flux: 1.2e11}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: NH3\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.01, flux: 5.0e+10, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: N2O\n", - " lower-boundary: {type: flux, flux: 1.0e+9}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: \"NO\"\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.016, flux: 1e9, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: NO2\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.1, flux: 1e9, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: HCN\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.1, flux: 4.4e+8, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: H2S\n", - " lower-boundary: {type: vdep + dist flux, vdep: 0.015, flux: 2e8, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: SO2\n", - " lower-boundary: {type: vdep + dist flux, vdep: 1, flux: 9e9, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: COS\n", - " lower-boundary: {type: vdep + dist flux, vdep: 1, flux: 9e9, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: H2SO4\n", - " lower-boundary: {type: vdep + dist flux, vdep: 1, flux: 7e8, height: 2}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "- name: H2\n", - " lower-boundary: {type: flux, flux: 1e6}\n", - " upper-boundary: {type: veff, veff: 0.0}\n", - "\"\"\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "# save the settings file\n", - "from photochem.utils._format import yaml, MyDumper, Loader, FormatSettings_main, FormatReactions_main\n", - "\n", - "data = yaml.load(settings_file,Loader=Loader)\n", - "data = FormatSettings_main(data)\n", - "fil = open('settings_vulcan.yaml','w')\n", - "yaml.dump(data,fil,Dumper=MyDumper,sort_keys=False,width=70)\n", - "fil.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we have everything we need to do a calculation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from matplotlib import pyplot as plt\n", - "from IPython.display import clear_output\n", - "from photochem import Atmosphere, zahnle_earth" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "ename": "PhotoException", - "evalue": "IOError: This reaction is a duplicate: Ar => Ar", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mPhotoException\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[7], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m pc \u001b[38;5;241m=\u001b[39m \u001b[43mAtmosphere\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSNCHO_full_photo_network.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msettings_vulcan.yaml\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../templates/ModernEarth/Sun_now.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m\\\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m../templates/ModernEarth/atmosphere_ModernEarth.txt\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32mAtmosphere.pyx:43\u001b[0m, in \u001b[0;36m_photochem.Atmosphere.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mPhotoException\u001b[0m: IOError: This reaction is a duplicate: Ar => Ar" - ] - } - ], - "source": [ - "pc = Atmosphere('SNCHO_full_photo_network.yaml',\\\n", - " \"settings_vulcan.yaml\",\\\n", - " \"../templates/ModernEarth/Sun_now.txt\",\\\n", - " \"../templates/ModernEarth/atmosphere_ModernEarth.txt\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Photochem found a duplicate reaction. Its not an important one because its just Ar makeing Ar. But we need to get rid of it. So this code snippet fixes this." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "fil = open(\"SNCHO_full_photo_network.yaml\",'r')\n", - "lines = fil.readlines()\n", - "fil.close()\n", - "\n", - "fil = open(\"SNCHO_full_photo_network.yaml\",'w')\n", - "for line in lines:\n", - " line = line.replace(\"Ar <=> Ar\",\"Ar => Ar\")\n", - " fil.write(line)\n", - "fil.close() " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "pc = Atmosphere('SNCHO_full_photo_network.yaml',\\\n", - " \"settings_vulcan.yaml\",\\\n", - " \"../templates/ModernEarth/Sun_now.txt\",\\\n", - " \"../templates/ModernEarth/atmosphere_ModernEarth.txt\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "# intitialize with nothing in atmosphere\n", - "init_cond = np.ones(pc.wrk.usol.shape,order='F')*1e-40" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [], - "source": [ - "pc.var.atol = 1.0e-23\n", - "pc.var.verbose = 0\n", - "pc.initialize_stepper(init_cond)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.rcParams.update({'font.size': 15})\n", - "\n", - "tn = 0\n", - "while tn < 1e17:\n", - " clear_output(wait=True)\n", - " fig,ax = plt.subplots(1,1,figsize=[6,5])\n", - " sol = pc.mole_fraction_dict()\n", - " species = ['H2O','O3','NO','NO2','N2O','HNO3']\n", - " for i,sp in enumerate(species):\n", - " ax.plot(sol[sp],sol['alt'],label=sp)\n", - " ax.set_xscale('log')\n", - " ax.set_ylabel('Altitude (km)')\n", - " ax.set_xlabel('Mixing ratio')\n", - " ax.set_xlim(1e-15,1)\n", - " ax.set_ylim(0,100)\n", - " ax.grid()\n", - " ax.text(0.02, 1.04, 't = '+'%.2e'%tn, \\\n", - " size = 15,ha='left', va='bottom',transform=ax.transAxes)\n", - " ax.legend(ncol=1,bbox_to_anchor=(1,1.0),loc='upper left')\n", - " plt.show()\n", - " for i in range(10):\n", - " tn = pc.step()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "pc.destroy_stepper()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These results are similar to Figure 9 in [the 2021 paper abount the VULCAN](https://arxiv.org/abs/2108.01790) photochemical model. Differences are probably mostly caused by different photolysis cross sections." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/src/input/photochem_input_read.f90 b/src/input/photochem_input_read.f90 index 0352bb7..a63d621 100644 --- a/src/input/photochem_input_read.f90 +++ b/src/input/photochem_input_read.f90 @@ -768,6 +768,17 @@ subroutine unpack_settings(infile, s, dat, var, err) !!!!!!!!!!!!!!!!! ! Condensation rate parameters. size is zero when there are no particles allocate(var%cond_params(dat%np)) + ! Replace default values with values from settings file, if needed + if (allocated(s%particles) .and. dat%there_are_particles) then + do i = 1,size(s%particles) + ind = findloc(dat%species_names(1:dat%np),s%particles(i)%name) + if (ind(1) == 0) then + err = 'Particle '//s%particles(i)%name//' in the settings file is not a particle in the reaction file.' + return + endif + var%cond_params(ind(1)) = s%particles(i)%params + enddo + endif !!!!!!!!!!!!!!!!!!!!!!!!!!! !!! boundary-conditions !!! diff --git a/src/photochem_types.f90 b/src/photochem_types.f90 index b804ea5..35b9336 100644 --- a/src/photochem_types.f90 +++ b/src/photochem_types.f90 @@ -30,7 +30,21 @@ module photochem_types ! make a giant IO object real(dp) :: den real(dp) :: press end type + + !> Condensation parameters + type :: CondensationParameters + real(dp) :: k_cond = 100.0_dp !! rate coefficient for condensation + real(dp) :: k_evap = 10.0_dp !! rate coefficient for evaporation + real(dp) :: RHc = 1.0_dp !! RH where condensation occurs + real(dp) :: smooth_factor = 0.2_dp !! A factor that smooths condensation/evaporation + !! rate to prevents stiffness + end type + type :: SettingsParticle + character(:), allocatable :: name + type(CondensationParameters) :: params + endtype + type :: PhotoSettings character(:), allocatable :: filename @@ -69,6 +83,9 @@ module photochem_types ! make a giant IO object real(dp) :: rainfall_rate character(s_str_len), allocatable :: rainout_species(:) real(dp) :: trop_alt + + ! particles + type(SettingsParticle), allocatable :: particles(:) ! boundary-conditions type(SettingsBC), allocatable :: ubcs(:) @@ -367,15 +384,6 @@ subroutine time_dependent_rate_fcn(tn, nz, rate) integer :: LH !! H index end type - - !> Condensation parameters - type :: CondensationParameters - real(dp) :: k_cond = 100.0_dp !! rate coefficient for condensation - real(dp) :: k_evap = 10.0_dp !! rate coefficient for evaporation - real(dp) :: RHc = 1.0_dp !! RH where condensation occurs - real(dp) :: smooth_factor = 0.2_dp !! A factor that smooths condensation/evaporation - !! rate to prevents stiffness - end type type :: PhotochemVars ! PhotochemVars contains information that can change between diff --git a/src/photochem_types_create.f90 b/src/photochem_types_create.f90 index b41e4e6..301a802 100644 --- a/src/photochem_types_create.f90 +++ b/src/photochem_types_create.f90 @@ -47,6 +47,7 @@ function unpack_PhotoSettings(root, filename, err) result(s) character(:), allocatable :: temp_char logical :: success integer :: i, j + real(dp) :: tmp_real ! filename s%filename = filename @@ -233,6 +234,7 @@ function unpack_PhotoSettings(root, filename, err) result(s) if (s%gas_rainout) then nullify(list) list => tmp2%get_list('rainout-species', required = .false., error = io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif if (associated(list)) then call unpack_string_list(list, s%rainout_species, err) if (allocated(err)) return @@ -257,6 +259,61 @@ function unpack_PhotoSettings(root, filename, err) result(s) endif endif + + ! Particles + nullify(list) + list => root%get_list('particles',.false.,error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + if (associated(list)) then + allocate(s%particles(list%size())) + i = 1 + item => list%first + do while (associated(item)) + + select type (e => item%node) + class is (type_dictionary) + s%particles(i)%name = e%get_string('name', error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + + tmp_real = s%particles(i)%params%k_cond + s%particles(i)%params%k_cond = e%get_real('condensation-rate', default=tmp_real, error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + + tmp_real = s%particles(i)%params%k_evap + s%particles(i)%params%k_evap = e%get_real('evaporation-rate', default=tmp_real, error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + + tmp_real = s%particles(i)%params%RHc + s%particles(i)%params%RHc = e%get_real('RH-condensation', default=tmp_real, error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + + tmp_real = s%particles(i)%params%smooth_factor + s%particles(i)%params%smooth_factor = e%get_real('smooth-factor', default=tmp_real, error=io_err) + if (allocated(io_err)) then; err = trim(filename)//trim(io_err%message); return; endif + + class default + err = "IOError: Particles must be a list of dictionaries" + return + end select + + i = i + 1 + item => item%next + enddo + + block + character(s_str_len), allocatable :: names(:) + allocate(names(size(s%particles))) + do i = 1,size(names) + names(i) = s%particles(i)%name + enddo + i = check_for_duplicates(names) + if (i /= 0) then + err = '"'//trim(names(i))//'" is a duplicate particle in '//trim(filename) + return + endif + endblock + + endif !!!!!!!!!!!!!!!!!!!!!!!!!!! !!! boundary-conditions !!! diff --git a/tests/testevo.f90 b/tests/testevo.f90 index 182edb7..229d55d 100644 --- a/tests/testevo.f90 +++ b/tests/testevo.f90 @@ -25,11 +25,7 @@ subroutine main_() print*,trim(err) stop 1 endif - - ! Change RH to 0.5. - ind = findloc(pc%dat%species_names, 'H2Oaer', 1) - pc%var%cond_params(ind)%RHc = 0.5_dp - + ! Initialize stepper call pc%initialize_stepper(pc%var%usol_init, err) if (allocated(err)) then diff --git a/tests/testrun.f90 b/tests/testrun.f90 index b8038b7..a06a934 100644 --- a/tests/testrun.f90 +++ b/tests/testrun.f90 @@ -26,10 +26,6 @@ subroutine main_() stop 1 endif - ! Change RH to 0.5. - ind = findloc(pc%dat%species_names, 'H2Oaer', 1) - pc%var%cond_params(ind)%RHc = 0.5_dp - ! Initialize stepper call pc%initialize_stepper(pc%var%usol_init, err) if (allocated(err)) then