opendrift.models.openoil.adios.computation.gnome_oil
Code for making a “GnomeOil” from an Oil Object
See the PyGNOME code for more about GNOME’s requirements
NOTE: This make s JSON compatible Python structure from which to build a GnomeOil
Module Contents
Functions
This provides an empty dictionary with everything that is needed |
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Make a dict that a GnomeOil can be built from |
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estimate pour point from kinematic viscosity |
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estimate flash point from api or boiling point |
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component temps from boiling point |
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set component SARA types |
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estimate component densities from boiling point |
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estimate component molecular weight from boiling point |
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resins and asphaltenes from database or estimated if None |
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Here we describe the form of a linear function for the purpose of |
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We make use of a generalized logistic function or Richard's curve |
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estimate cut temperatures |
estimate pseudocomponent mass fractions |
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get SARA from database |
Attributes
- opendrift.models.openoil.adios.computation.gnome_oil.logger
- opendrift.models.openoil.adios.computation.gnome_oil.get_empty_dict()[source]
This provides an empty dictionary with everything that is needed to generate a GNOME Oil
- opendrift.models.openoil.adios.computation.gnome_oil.make_gnome_oil(oil)[source]
Make a dict that a GnomeOil can be built from
A GnomeOil needs:
“name, “# Physical properties “api, “pour_point, “solubility, # kg/m^3 “# emulsification properties “bullwinkle_fraction, “bullwinkle_time, “emulsion_water_fraction_max, “densities, “density_ref_temps, “density_weathering, “kvis, “kvis_ref_temps, “kvis_weathering, “# PCs: “mass_fraction, “boiling_point, “molecular_weight, “component_density, “sara_type, “flash_point=None, “adios_oil_id=None,
- opendrift.models.openoil.adios.computation.gnome_oil.estimate_pour_point(oil)[source]
estimate pour point from kinematic viscosity
- opendrift.models.openoil.adios.computation.gnome_oil.estimate_flash_point(oil)[source]
estimate flash point from api or boiling point
- opendrift.models.openoil.adios.computation.gnome_oil.component_temps(cut_temps, N=10)[source]
component temps from boiling point
- opendrift.models.openoil.adios.computation.gnome_oil.component_types(cut_temps, N=10)[source]
set component SARA types
- opendrift.models.openoil.adios.computation.gnome_oil.component_densities(boiling_points)[source]
estimate component densities from boiling point
- opendrift.models.openoil.adios.computation.gnome_oil.component_mol_wt(boiling_points)[source]
estimate component molecular weight from boiling point
- opendrift.models.openoil.adios.computation.gnome_oil.inert_fractions(oil, density=None, viscosity=None)[source]
resins and asphaltenes from database or estimated if None
- opendrift.models.openoil.adios.computation.gnome_oil._linear_curve(x, a, b)[source]
Here we describe the form of a linear function for the purpose of curve-fitting measured data points.
- opendrift.models.openoil.adios.computation.gnome_oil.clamp(x, M, zeta=0.03)[source]
We make use of a generalized logistic function or Richard’s curve to generate a linear function that is clamped at x == M. We make use of a zeta value to tune the parameters nu, resulting in a smooth transition as we cross the M boundary.
- opendrift.models.openoil.adios.computation.gnome_oil._inverse_linear_curve(y, a, b, M, zeta=0.12)[source]
- opendrift.models.openoil.adios.computation.gnome_oil.normalized_cut_values_james(oil, N=10)[source]
estimate cut temperatures