boiling_point_dict[smiles]['joback_and_reid'])
vapour_pressure_dict_BP[smiles]['VP_Myrdal_Yalkowsky'][
'BP_Nannoolal'] = vapour_pressures.myrdal_and_yalkowsky(
Pybel_object_dict[smiles], temperature,
boiling_point_dict[smiles]['nannoolal'])
vapour_pressure_dict_BP[smiles]['VP_Myrdal_Yalkowsky'][
'BP_Stein_Brown'] = vapour_pressures.myrdal_and_yalkowsky(
Pybel_object_dict[smiles], temperature,
boiling_point_dict[smiles]['stein_and_brown'])
vapour_pressure_dict_BP[smiles]['VP_Myrdal_Yalkowsky'][
'BP_Joback_Reid'] = vapour_pressures.myrdal_and_yalkowsky(
Pybel_object_dict[smiles], temperature,
boiling_point_dict[smiles]['joback_and_reid'])
vapour_pressure_dict[smiles]['EVAP'] = vapour_pressures.evaporation(
Pybel_object_dict[smiles], temperature)
# 2c) Subooled liquid density [(g/cc) at a specific temperature]
# Some require a pre-calculated critical properties and thus boiling points which are fed into the function directly
density_CP = collections.defaultdict(
lambda: collections.defaultdict(lambda: collections.defaultdict()))
density = collections.defaultdict(lambda: collections.defaultdict())
for smiles in smiles_array:
compound = Pybel_object_dict[
smiles] #Using this simply to try and keep line extensions short as possible
boiling_point = boiling_point_dict[smiles][
'nannoolal'] #Selecting this as an example
def Pure_component1(num_species, species_dict, species_dict2array,
Pybel_object_dict, SMILES_dict, temp, vp_method, bp_method,
critical_method, density_method, ignore_vp, vp_cutoff):
""" This function calculates properties that dictate gas-to-particle partitioning
inputs:
• num_species - number of compounds used in the calculations
• species_dict - dict holding names of all compounds
• species_dict2array - dict that mapes compound name to array index
• Pybel_object_dict - dict holding all Pybel objects of each compound [used in UManSysProp]
• SMILES_dict - dict containing all SMILES strings of each compound
• temp - temperature [K]
• vp_method - choice of vapour pressure method [see calling function and/or UManSysProp]
• bp_method - choice of boiling point method [see calling function and/or UManSysProp]
• critical_method - choice of criticl property method [see calling function and/or UManSysProp]
• density_method - choice of density method [see calling function and/or UManSysProp]
• ignore_vp - flag to ignore compounds with vapour pressure above a defined value given by 'vp_cutoff'
• vp_cutoff - log10 value of vapour pressure above which the odel will ignore partitioning
outputs:
• return_dict['y_density_array']=y_density_array - density of each condensing compound
• return_dict['y_mw']=y_mw - molecular weight of each condensing compound
• return_dict['sat_vp']=sat_vp - saturation vapour pressure of each condensing compound
• return_dict['Delta_H']=Delta_H - enthalpy of vapourisation of each condensing compound
• return_dict['Latent_heat_gas']=Latent_heat_gas - latent hear of condensation of each condensing compound
• return_dict['ignore_index']=ignore_index - array that holds information on which compounds to ignore for partitioning
• return_dict['ignore_index_fortran']=ignore_index_fortran - dense array that holds information on which compounds to ignore for partitioning in Fortran functions
• return_dict['include_index']=include_index - array that holds information on which compounds to include for partitioning
"""
y_density_array = [1000.0] * num_species
y_mw = [200.0] * num_species
o_c = [0.0] * num_species
h_c = [0.0] * num_species
sat_vp = [100.0] * num_species
sat_vp_org = dict(
) #Recorded seperately and will not include the extension for water. This is used for any checks with equilibrium partitioning predictions
y_gas = [0.0] * num_species
species_step = 0
ignore_index = [
] #Append to this to identify any compounds that do not have automated calculation of properties. OR are species that will be ignored in partitioning
include_index = []
include_dict = dict()
ignore_index_fortran = numpy.zeros((num_species), )
print("Calculating component properties using UManSysProp")
# Which boiling point method has been chosen
boiling_point = {
'joback_and_reid': boiling_points.joback_and_reid,
'stein_and_brown': boiling_points.stein_and_brown,
'nannoolal': boiling_points.nannoolal,
}[bp_method]
vapour_pressure = {
'nannoolal': vapour_pressures.nannoolal,
'myrdal_and_yalkowsky': vapour_pressures.myrdal_and_yalkowsky,
# Evaporation doesn't use boiling point
'evaporation': lambda c, t, b: vapour_pressures.evaporation(c, t),
}[vp_method]
# Which density method has been chosen
critical_property = {
'nannoolal': critical_properties.nannoolal,
'joback_and_reid': critical_properties.joback_and_reid,
}[critical_method]
liquid_density = {
'girolami': lambda c, t, p: liquid_densities.girolami(c),
'schroeder': liquid_densities.schroeder,
'le_bas': liquid_densities.le_bas,
'tyn_and_calus': liquid_densities.tyn_and_calus,
}[density_method]
for compound in species_dict.values():
if compound in SMILES_dict.keys():
# Calculate a boiling point with Nanoolal for density methods
#pdb.set_trace()
b1 = boiling_points.nannoolal(
Pybel_object_dict[SMILES_dict[compound]])
y_density_array[species_dict2array[compound]] = liquid_density(
Pybel_object_dict[SMILES_dict[compound]], temp,
critical_property(Pybel_object_dict[SMILES_dict[compound]],
b1)) * 1.0E3
#y_density_array[species_dict2array[compound]]=(liquid_densities.girolami(Pybel_object_dict[SMILES_dict[compound]])*1.0E3) #Convert from g/cc to kg/m3
#y_density_array.append(1400.0)
y_mw[species_dict2array[compound]] = (
Pybel_object_dict[SMILES_dict[compound]].molwt)
#In the following you will need to select which vapour pressure method you like.
groups_dict = groups.composition(
Pybel_object_dict[SMILES_dict[compound]])
o_c[species_dict2array[
compound]] = groups_dict['O'] / groups_dict['C']
h_c[species_dict2array[
compound]] = groups_dict['H'] / groups_dict['C']
# Calculate boiling point
b = boiling_point(Pybel_object_dict[SMILES_dict[compound]])
sat_vp[species_dict2array[compound]] = vapour_pressure(
Pybel_object_dict[SMILES_dict[compound]], temp, b)
if ignore_vp is True:
if sat_vp[species_dict2array[compound]] > vp_cutoff:
ignore_index.append(species_dict2array[compound])
ignore_index_fortran[species_dict2array[compound]] = 1.0
else:
include_index.append(species_dict2array[compound])
else:
include_index.append(species_dict2array[compound])
#sat_vp[species_dict2array[compound]]=(vapour_pressures.nannoolal(Pybel_object_dict[SMILES_dict[compound]], temp, boiling_points.nannoolal(Pybel_object_dict[SMILES_dict[compound]])))
#sat_vp_org[Pybel_object_dict[SMILES_dict[compound]]]=vapour_pressures.nannoolal(Pybel_object_dict[SMILES_dict[compound]], temp, boiling_points.nannoolal(Pybel_object_dict[SMILES_dict[compound]]))
#y_gas.append(concentration_array[species_step])
else:
ignore_index.append(species_dict2array[compound])
ignore_index_fortran[species_dict2array[compound]] = 1.0
species_step += 1
Delta_H = [
0.0
] * num_species #Dont prescribe an enthalpy of vaporisation. For non-VBS model simulations with varying temperature, we recalculate using GCM
Latent_heat_gas = [
0.0
] * num_species # - Still need to account for any latent heat release [Future work]
return_dict = dict()
return_dict['y_density_array'] = y_density_array
return_dict['y_mw'] = y_mw
return_dict['o_c'] = o_c
return_dict['h_c'] = h_c
return_dict['sat_vp'] = sat_vp
return_dict['Delta_H'] = Delta_H
return_dict['Latent_heat_gas'] = Latent_heat_gas
return_dict['ignore_index'] = ignore_index
return_dict['ignore_index_fortran'] = ignore_index_fortran
return_dict['include_index'] = include_index
return return_dict