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aeromaps.models.air_transport.aircraft_fleet_and_operations.non_co2.non_co2

non_co2

Module for computing non-CO2 climate effects related to aircraft operations, including contrail-related adjustments and fuel-effect corrections used in ERF calculations.

OperationsContrailsSimple 

OperationsContrailsSimple(name='operations_contrails_simple', *args, **kwargs)

Bases: AeroMAPSModel

Simple operational model computing contrails gains for ERF calculation and overconsumption associated with contrail avoidance.

Parameters:

Name Type Description Default
name

Name of the model instance ('operations_contrails_simple' by default).

 'operations_contrails_simple'
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def __init__(self, name="operations_contrails_simple", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(operations_contrails_final_gain, operations_contrails_final_overconsumption, operations_contrails_start_year, operations_contrails_duration)

Execute computation for contrails gains for ERF calculation and overconsumption associated with contrail avoidance.

Parameters:

Name Type Description Default
operations_contrails_final_gain float

Final impact of contrail operational improvements in terms of percentage reduction in contrails climate impacts [%].

 required
operations_contrails_final_overconsumption float

Final impact of contrail operational improvements in terms of percentage increase in fuel consumption [%].

 required
operations_contrails_start_year Number

Start year for implementing contrail operational improvements to reduce contrail climate impacts [yr].

 required
operations_contrails_duration float

Duration for implementing 98% of contrail operational improvements to reduce contrail climate impacts [yr].

 required

Returns:

Type Description
operations_contrails_gain

Impact of contrail operational improvements in terms of percentage reduction in contrails climate impacts [%].
operations_contrails_overconsumption

Impact of contrail operational improvements in terms of percentage increase in fuel consumption [%].
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def compute(
    self,
    operations_contrails_final_gain: float,
    operations_contrails_final_overconsumption: float,
    operations_contrails_start_year: Number,
    operations_contrails_duration: float,
) -> Tuple[pd.Series, pd.Series]:
    """Execute computation for contrails gains for ERF calculation and overconsumption associated with contrail avoidance.

    Parameters
    ----------
    operations_contrails_final_gain
        Final impact of contrail operational improvements in terms of percentage reduction in contrails climate impacts [%].
    operations_contrails_final_overconsumption
        Final impact of contrail operational improvements in terms of percentage increase in fuel consumption [%].
    operations_contrails_start_year
        Start year for implementing contrail operational improvements to reduce contrail climate impacts [yr].
    operations_contrails_duration
        Duration for implementing 98% of contrail operational improvements to reduce contrail climate impacts [yr].

    Returns
    -------
    operations_contrails_gain
        Impact of contrail operational improvements in terms of percentage reduction in contrails climate impacts [%].
    operations_contrails_overconsumption
        Impact of contrail operational improvements in terms of percentage increase in fuel consumption [%].
    """

    transition_year = operations_contrails_start_year + operations_contrails_duration / 2
    operations_contrails_limit = 0.02 * operations_contrails_final_gain
    operations_parameter = np.log(100 / 2 - 1) / (operations_contrails_duration / 2)
    for k in range(self.historic_start_year, self.prospection_start_year):
        self.df.loc[k, "operations_contrails_gain"] = 0
        self.df.loc[k, "operations_contrails_overconsumption"] = 0
    for k in range(self.prospection_start_year, self.end_year + 1):
        if (
            operations_contrails_final_gain
            / (1 + np.exp(-operations_parameter * (k - transition_year)))
            < operations_contrails_limit
        ):
            self.df.loc[k, "operations_contrails_gain"] = 0
            self.df.loc[k, "operations_contrails_overconsumption"] = 0
        else:
            self.df.loc[k, "operations_contrails_gain"] = operations_contrails_final_gain / (
                1 + np.exp(-operations_parameter * (k - transition_year))
            )
            self.df.loc[k, "operations_contrails_overconsumption"] = (
                operations_contrails_final_overconsumption
                / (1 + np.exp(-operations_parameter * (k - transition_year)))
            )

    operations_contrails_gain = self.df["operations_contrails_gain"]
    operations_contrails_overconsumption = self.df["operations_contrails_overconsumption"]

    return operations_contrails_gain, operations_contrails_overconsumption

FuelEffectCorrectionContrails 

FuelEffectCorrectionContrails(name='fuel_effect_correction_contrails', *args, **kwargs)

Bases: AeroMAPSModel

Compute fuel-effect on contrails for ERF calculation.

This custom model aggregates pathway-specific particle emission indices and distance shares to compute a correction applied to contrail forcing to reflect fuel/pathway effects.

Parameters:

Name Type Description Default
name

Name of the model instance ('fuel_effect_correction_contrails' by default).

 'fuel_effect_correction_contrails'

Attributes:

Name Type Description
pathways_manager

EnergyCarrierManager instance managing the energy carriers pathways considered in the scenario.
input_names

Dictionary defining the expected input names for the model.
output_names

Dictionary defining the output names for the model.
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def __init__(self, name="fuel_effect_correction_contrails", *args, **kwargs):
    super().__init__(name=name, model_type="custom", *args, **kwargs)
    self.pathways_manager = None

custom_setup 

custom_setup()

Setup input and output names for the model grammar, based on .yaml configuration of energy carriers.

Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def custom_setup(self):
    """Setup input and output names for the model grammar, based on .yaml configuration of energy carriers."""
    self.input_names = {}
    self.output_names = {}

    for aircraft_type in self.pathways_manager.get_all_types("aircraft_type"):
        if aircraft_type == "dropin_fuel":
            for pathway in self.pathways_manager.get(
                aircraft_type=aircraft_type,
            ):
                self.input_names.update(
                    {
                        f"{pathway.name}_massic_share_{aircraft_type}": pd.Series([0.0]),
                        f"{pathway.name}_emission_index_particles_number": 0.0,
                    }
                )
            self.input_names.update(
                {
                    "total_aircraft_distance_dropin_fuel": pd.Series([0.0]),
                }
            )
        elif aircraft_type == "hydrogen":
            self.input_names.update(
                {
                    "contrails_relative_effect_hydrogen_wrt_kerosene": 0.0,
                    "total_aircraft_distance_hydrogen": pd.Series([0.0]),
                }
            )
        elif aircraft_type == "electric":
            self.input_names.update(
                {
                    "total_aircraft_distance_electric": pd.Series([0.0]),
                }
            )
        else:
            warnings.warn(f"Aircraft type '{aircraft_type}' not supported.", UserWarning)

    self.input_names.update(
        {
            "total_aircraft_distance": pd.Series([0.0]),
        }
    )

    self.output_names.update(
        {
            "fuel_effect_correction_contrails": pd.Series([0.0]),
        }
    )

compute 

compute(input_data)

Execute the computation of fuel-effect correction for contrails ERF computation.

Parameters:

Name Type Description Default
input_data

Mapping of input series and scalars required by the model. Expected keys include pathway-specific massic shares of each aircraft consumption; particle emission indices, total aircraft distances travelled by aircraft type and overall total aircraft distance, and contrails_relative_effect_hydrogen_wrt_kerosene when hydrogen pathways are present.

 required

Returns:

Type Description
output_data

Dictionary with key 'fuel_effect_correction_contrails' containing a pd.Series multiplicative correction (dimensionless) for contrail forcing over the scenario.
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def compute(self, input_data) -> dict:
    """Execute the computation of fuel-effect correction for contrails ERF computation.

    Parameters
    ----------
    input_data
        Mapping of input series and scalars required by the model. Expected
        keys include pathway-specific massic shares of each aircraft consumption; particle emission
        indices, total aircraft distances travelled by aircraft type and overall total
        aircraft distance, and contrails_relative_effect_hydrogen_wrt_kerosene
        when hydrogen pathways are present.

    Returns
    -------
    output_data
        Dictionary with key 'fuel_effect_correction_contrails' containing a
        pd.Series multiplicative correction (dimensionless) for contrail
        forcing over the scenario.
    """

    fuel_effect_correction_contrails = get_default_series(
        self.historic_start_year, self.end_year
    )

    for aircraft_type in self.pathways_manager.get_all_types("aircraft_type"):
        if aircraft_type == "dropin_fuel":
            relative_particles_number = get_default_series(
                self.historic_start_year, self.end_year
            )
            distance_share_dropin_fuel = (
                input_data["total_aircraft_distance_dropin_fuel"]
                / input_data["total_aircraft_distance"]
            )

            default_pathway = self.pathways_manager.get(
                aircraft_type=aircraft_type, default=True
            )[0]
            default_emission_index_number_particles = input_data[
                f"{default_pathway.name}_emission_index_particles_number"
            ]

            for pathway in self.pathways_manager.get(
                aircraft_type=aircraft_type,
            ):
                relative_particles_number += (
                    input_data[f"{pathway.name}_massic_share_{aircraft_type}"]
                    / 100
                    * np.sqrt(
                        input_data[f"{pathway.name}_emission_index_particles_number"]
                        / default_emission_index_number_particles
                    )
                ).fillna(0)

            fuel_effect_correction_contrails += (
                distance_share_dropin_fuel * relative_particles_number
            )
        elif aircraft_type == "hydrogen":
            fuel_effect_correction_contrails += (
                input_data["total_aircraft_distance_hydrogen"]
                / input_data["total_aircraft_distance"]
                * input_data["contrails_relative_effect_hydrogen_wrt_kerosene"]
            )
        elif aircraft_type == "electric":
            pass
        else:
            warnings.warn(f"Aircraft type '{aircraft_type}' not supported.", UserWarning)

    output_data = {
        "fuel_effect_correction_contrails": fuel_effect_correction_contrails,
    }

    self._store_outputs(output_data)
    return output_data

WithoutFuelEffectCorrectionContrails 

WithoutFuelEffectCorrectionContrails(name='without_fuel_effect_correction_contrails', *args, **kwargs)

Bases: AeroMAPSModel

Model returning a unitary correction (no fuel effect) for contrails.

Parameters:

Name Type Description Default
name

Name of the model instance ('without_fuel_effect_correction_contrails' by default).

 'without_fuel_effect_correction_contrails'
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def __init__(self, name="without_fuel_effect_correction_contrails", *args, **kwargs):
    super().__init__(name=name, *args, **kwargs)

compute 

compute(total_aircraft_distance)

Return a multiplicative correction equal to 1 for all years.

Parameters:

Name Type Description Default
total_aircraft_distance Series

Total distance travelled by aircraft (based on RPK traffic) [km]. FIXME: this input is not used in the computation.

 required

Returns:

Type Description
fuel_effect_correction_contrails

Multiplicative multiplicative correction (dimensionless) for contrail forcing over the scenario, equal to 1 for all years.
Source code in aeromaps/models/air_transport/aircraft_fleet_and_operations/non_co2/non_co2.py 
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def compute(
    self,
    total_aircraft_distance: pd.Series,
) -> pd.Series:
    """Return a multiplicative correction equal to 1 for all years.

    Parameters
    ----------
    total_aircraft_distance
        Total distance travelled by aircraft (based on RPK traffic) [km].
        FIXME: this input is not used in the computation.

    Returns
    -------
    fuel_effect_correction_contrails
        Multiplicative multiplicative correction (dimensionless) for contrail
        forcing over the scenario, equal to 1 for all years.
    """

    for k in range(self.historic_start_year, self.end_year + 1):
        self.df.loc[k, "fuel_effect_correction_contrails"] = 1

    fuel_effect_correction_contrails = self.df["fuel_effect_correction_contrails"]
    return fuel_effect_correction_contrails