fastga.models.propulsion.fuel_propulsion.basicIC_engine.basicIC_engine module

Parametric propeller IC engine.

class fastga.models.propulsion.fuel_propulsion.basicIC_engine.basicIC_engine.BasicICEngine(max_power: float, cruise_altitude_propeller: float, fuel_type: float, strokes_nb: float, prop_layout: float, k_factor_sfc: float, speed_SL, thrust_SL, thrust_limit_SL, efficiency_SL, speed_CL, thrust_CL, thrust_limit_CL, efficiency_CL, effective_J, effective_efficiency_ls, effective_efficiency_cruise)[source]

Bases: fastga.models.propulsion.fuel_propulsion.base.AbstractFuelPropulsion

Parametric Internal Combustion engine.

It computes engine characteristics using fuel type, motor architecture and constant propeller efficiency using analytical model from following sources:

Parameters

max_power – maximum delivered mechanical power of engine (units=W)
cruise_altitude_propeller – design altitude for cruise (units=m)
fuel_type – 1.0 for gasoline and 2.0 for diesel engine and 3.0 for Jet Fuel
strokes_nb – can be either 2-strokes (=2.0) or 4-strokes (=4.0)
prop_layout – propulsion position in nose (=3.0) or wing (=1.0)
speed_SL – array with the speed at which the sea level performance of the propeller

were computed :param thrust_SL: array with the required thrust at which the sea level performance of the propeller were computed :param thrust_limit_SL: array with the limit thrust available at the speed in speed_SL :param efficiency_SL: array containing the sea level efficiency computed at speed_SL and thrust_SL :param speed_CL: array with the speed at which the cruise level performance of the propeller were computed :param thrust_CL: array with the required thrust at which the cruise level performance of the propeller were computed :param thrust_limit_CL: array with the limit thrust available at the speed in speed_CL :param efficiency_CL: array containing the cruise level efficiency computed at speed_CL and thrust_CL

property propeller_efficiency_interpolator_sl

property propeller_efficiency_interpolator_cl

property sfc_interpolator

static read_map(map_file_path)[source]

compute_flight_points(flight_points: fastoad.model_base.flight_point.FlightPoint)[source]

Computes Specific Fuel Consumption according to provided conditions.

See FlightPoint for available fields that may be used for computation. If a DataFrame instance is provided, it is expected that its columns match field names of FlightPoint (actually, the DataFrame instance should be generated from a list of FlightPoint instances).

Note

About thrust_is_regulated, thrust_rate and thrust

thrust_is_regulated tells if a flight point should be computed using thrust_rate (when False) or thrust (when True) as input. This way, the method can be used in a vectorized mode, where each point can be set to respect a thrust order or a thrust rate order.

if thrust_is_regulated is not defined, the considered input will be the defined one between thrust_rate and thrust (if both are provided, thrust_rate will be used)
if thrust_is_regulated is True or False (i.e., not a sequence), the considered input will be taken accordingly, and should of course be defined.
if there are several flight points, thrust_is_regulated is a sequence or array, thrust_rate and thrust should be provided and have the same shape as thrust_is_regulated:code:. The method will consider for each element which input will be used according to thrust_is_regulated.

Parameters: flight_points – FlightPoint or DataFram instance
Returns: None (inputs are updated in-place)

propeller_efficiency(thrust: Union[float, Sequence[float]], atmosphere: stdatm.atmosphere.Atmosphere) → Union[float, Sequence][source]

Compute the propeller efficiency.

Parameters

thrust – Thrust (in N)
atmosphere – Atmosphere instance at intended altitude

Returns

efficiency

compute_max_power(flight_points: fastoad.model_base.flight_point.FlightPoint) → Union[float, Sequence][source]

Compute the ICE maximum power @ given flight-point.

Parameters: flight_points – current flight point(s)
Returns: maximum power in kW

sfc(thrust: Union[float, Sequence[float]], engine_setting: Union[float, Sequence[float]], atmosphere: stdatm.atmosphere.Atmosphere) → Tuple[numpy.ndarray, numpy.ndarray][source]

Computation of the SFC.

Parameters

thrust – Thrust (in N)
engine_setting – Engine settings (climb, cruise,… )
atmosphere – Atmosphere instance at intended altitude

Returns

SFC (in g/kw) and Power (in W)

max_thrust(engine_setting: Union[float, Sequence[float]], atmosphere: stdatm.atmosphere.Atmosphere) → numpy.ndarray[source]

Computation of maximum thrust either due to propeller thrust limit or ICE max power.

Parameters

engine_setting – Engine settings (climb, cruise,… )
atmosphere – Atmosphere instance at intended altitude (should be <=20km)

Returns

maximum thrust (in N)

compute_weight() → float[source]: Computes weight of installed propulsion (engine, nacelle and propeller) depending on maximum power. Uses model described in : Gudmundsson, Snorri. General aviation aircraft design: Applied Methods and Procedures. Butterworth-Heinemann, 2013. Equation (6-44)

compute_dimensions() -> (<class 'float'>, <class 'float'>, <class 'float'>, <class 'float'>)[source]: Computes propulsion dimensions (engine/nacelle) from maximum power. Model from :…

compute_drag(mach, unit_reynolds, wing_mac)[source]: Compute nacelle drag coefficient cd0.

class fastga.models.propulsion.fuel_propulsion.basicIC_engine.basicIC_engine.Engine(*args, **kwargs)[source]

Bases: fastga.models.propulsion.dict.DynamicAttributeDict

Class for storing data for engine.

An instance is a simple dict, but for convenience, each item can be accessed as an attribute (inspired by pandas DataFrames). Hence, one can write:

>>> engine = Engine(power_SL=10000.)
>>> engine["power_SL"]
10000.0
>>> engine["mass"] = 70000.
>>> engine.mass
70000.0
>>> engine.mass = 50000.
>>> engine["mass"]
50000.0

Note: constructor will forbid usage of unknown keys as keyword argument, but other methods will allow them, while not making the matching between dict keys and attributes, hence:

>>> engine["foo"] = 42  # Ok
>>> bar = engine.foo  # raises exception !!!!
>>> engine.foo = 50  # allowed by Python
>>> # But inner dict is not affected:
>>> engine.foo
50
>>> engine["foo"]
42

This class is especially useful for generating pandas DataFrame: a pandas DataFrame can be generated from a list of dict… or a list of oad.FlightPoint instances.

The set of dictionary keys that are mapped to instance attributes is given by the get_attribute_keys().

A dictionary class where keys can also be used as attributes.

The keys that can be used as attributes are defined using decorators AddKeyAttribute or SetKeyAttributes.

They can also be used as keyword arguments when instantiating this class.

Note

Using this class as a dict is useful when instantiating another dict or a pandas DataFrame, or instantiating from them. Direct interaction with DynamicAttributeDict instance should be done through attributes.

Example:

>>> @AddKeyAttributes({"foo": 0.0, "bar": None, "baz": 42.0})
... class MyDict(DynamicAttributeDict):
...     pass
...

>>> d = MyDict(foo=5, bar="aa")
>>> d.foo
5
>>> d.bar
'aa'
>>> d.baz  # returns the default value
42.0
>>> d["foo"] = 10.0  # can still be used as a dict
>>> d.foo  # but change are propagated to/from the matching attribute
10.0
>>> d.foo = np.nan  # setting None or numpy.nan returns to default value
>>> d["foo"]
0.0
>>> d.foo # But the attribute will now return the default value
0.0
>>> d.bar = None  # If default value is None, setting None or numpy.nan deletes the key.
>>> # d["bar"]  #would trigger a key error
>>> d.bar # But the attribute will return None

Parameters

args – a dict-like object where all keys are contained in attribute_keys
kwargs – argument keywords must be names contained in attribute_keys

classmethod get_attribute_keys()

Returns: list of attributes paired to dict key.

property height: Height in meters.

property length: Length in meters.

property mass: Mass in kilograms.

property power_SL: Power at sea level in watts.

property width: Width in meters.

class fastga.models.propulsion.fuel_propulsion.basicIC_engine.basicIC_engine.Nacelle(*args, **kwargs)[source]

Bases: fastga.models.propulsion.dict.DynamicAttributeDict

Class for storing data for nacelle.

Similar to Engine.

A dictionary class where keys can also be used as attributes.

The keys that can be used as attributes are defined using decorators AddKeyAttribute or SetKeyAttributes.

They can also be used as keyword arguments when instantiating this class.

Note

Using this class as a dict is useful when instantiating another dict or a pandas DataFrame, or instantiating from them. Direct interaction with DynamicAttributeDict instance should be done through attributes.

Example:

>>> @AddKeyAttributes({"foo": 0.0, "bar": None, "baz": 42.0})
... class MyDict(DynamicAttributeDict):
...     pass
...

>>> d = MyDict(foo=5, bar="aa")
>>> d.foo
5
>>> d.bar
'aa'
>>> d.baz  # returns the default value
42.0
>>> d["foo"] = 10.0  # can still be used as a dict
>>> d.foo  # but change are propagated to/from the matching attribute
10.0
>>> d.foo = np.nan  # setting None or numpy.nan returns to default value
>>> d["foo"]
0.0
>>> d.foo # But the attribute will now return the default value
0.0
>>> d.bar = None  # If default value is None, setting None or numpy.nan deletes the key.
>>> # d["bar"]  #would trigger a key error
>>> d.bar # But the attribute will return None

Parameters

args – a dict-like object where all keys are contained in attribute_keys
kwargs – argument keywords must be names contained in attribute_keys

classmethod get_attribute_keys()

Returns: list of attributes paired to dict key.

property height: Height in meters.

property length: Length in meters.

property wet_area: Wet area in meters².

property width: Width in meters.