pycontrails.models.ps_model.ps_nominal_grid#

pycontrails.models.ps_model.ps_nominal_grid(aircraft_type, *, level=None, air_temperature=None, q_fuel=43130000.0, mach_number=None, maxiter=10)#

Calculate the nominal performance grid for a given aircraft type.

This function is similar to the PSGrid model, but it doesn’t require meteorological data. Instead, the ambient air temperature can be computed from the ISA model or passed as an argument.

Parameters:

  • aircraft_type (str) – The aircraft type.

  • level (npt.NDArray[np.float_] | None, optional) – The pressure level, [\(hPa\)]. If None, the air_temperature argument must be a xarray.DataArray with a level coordinate.

  • air_temperature (xr.DataArray | npt.NDArray[np.float] | None, optional) – The ambient air temperature, [\(K\)]. If None (default), the ISA temperature is computed from the level argument. If a xarray.DataArray, the level coordinate must be present and the level argument must be None to avoid ambiguity. If a numpy.ndarray is passed, it is assumed to be 1 dimensional with the same shape as the level argument.

  • q_fuel (float, optional) – The fuel heating value, by default JetA.q_fuel

  • mach_number (float | None, optional) – The Mach number. If None (default), the PS design Mach number is used.

  • maxiter (int, optional) – Passed into scipy.optimize.newton().

Returns:

xarray.Dataset – The nominal performance grid. The grid is indexed by altitude and Mach number. Contains the following variables:

  • "fuel_flow" : Fuel flow rate, [\(kg/s\)]

  • "engine_efficiency" : Engine efficiency

  • "aircraft_mass" : Aircraft mass at which the engine efficiency is maximized, [\(kg\)]

Examples

>>> level = np.arange(200, 300, 10, dtype=float)

>>> # Compute nominal aircraft performance assuming ISA conditions
>>> # and the design Mach number
>>> perf = ps_nominal_grid("A320", level=level)
>>> perf.attrs["mach_number"]
0.753

>>> perf.to_dataframe()
       aircraft_mass  engine_efficiency  fuel_flow
level
200.0   58564.031595           0.296651   0.585492
210.0   61772.755626           0.296651   0.614767
220.0   64992.059200           0.296651   0.644041
230.0   68196.058808           0.296651   0.674351
240.0   71364.841887           0.296651   0.706525
250.0   71749.900301           0.296497   0.713566
260.0   71739.903908           0.296022   0.718626
270.0   71726.248416           0.295323   0.725039
280.0   71709.601531           0.294474   0.732570
290.0   71690.471232           0.293529   0.741036

>>> # Now compute it for a higher Mach number
>>> perf = ps_nominal_grid("A320", level=level, mach_number=0.78)
>>> perf.to_dataframe()
       aircraft_mass  engine_efficiency  fuel_flow
level
200.0   57989.240152           0.302153   0.604883
210.0   60819.870039           0.302156   0.631772
220.0   64021.435191           0.302156   0.661856
230.0   67205.781633           0.302156   0.693004
240.0   70351.196513           0.302156   0.726069
250.0   71677.860483           0.302096   0.743054
260.0   71664.587950           0.301756   0.749582
270.0   71647.346486           0.301200   0.757550
280.0   71626.941615           0.300498   0.766680
290.0   71603.964267           0.299700   0.776765