pyna

The pyna class contains the methods to run the following noise analyses:

• Computing noise contours from a list of microhpone locations

• Computing the EPNL table for the individual and overall noise sources

• Computing noise directional and spectral distributions

• Computing noise for a predefined trajectory time series

• Computing a take-off trajectory

• Computing a take-off trajectory and its noise

The class also contains methods to plot the results of these analyses.

class pyNA.pyna.pyna(settings: pyNA.src.settings.Settings)

Bases: object

The pyna module contains the methods to assess the noise footprint of an aircraft (i.e airframe and engine) flying along a predefined trajectory and calculates the sensitivities of the noise footprint with respect to engine variables.

__init__(settings: pyNA.src.settings.Settings) → None

Initialize pyna class.

Parameters

settings (Settings) – pyna settings

Returns

None

compute_noise_contours(x_lst: numpy.ndarray, y_lst: numpy.ndarray) → None

Compute noise contours for a predefined trajectory.

Parameters

• x_lst (list) – List of x-location of the microphones.

• y_lst (list) – List of y-location of the microphones.

Returns

None

compute_noise_epnl_table() → pandas.core.frame.DataFrame

Compute table of epnl for individual noise sources and observers.

Returns

None

compute_noise_source_distribution(time_step: numpy.int64) → None

Compute noise source spectral and directional distribution.

Parameters

time_step (np.int64) – Time step in predefined trajectory at which to compute the noise source distribution.

Returns

None

compute_noise_time_series() → None

Compute noise for a predefined trajectory.

Returns

None

compute_trajectory(control_optimization=False, objective=None) → bool

Compute aircraft take-off trajectory.

Returns

converged

Return type

bool

compute_trajectory_noise(init_traj_name=None) → None

Compute aircraft take-off trajectory and noise signature.

Parameters

init_traj_name (str) – Name of initialization trajectory (in output folder of case).

Returns

None

load_results(file_name, case_name='final')

Load model .sql results file.

Parameters

• file_name (str) – Name of the .sql results file to load.

• case_name (str) – Name of the case to load in the results-file.

Returns

results

static load_settings(case_name: str) → pyNA.src.settings.Settings

Load default pyna settings.

Parameters

case_name (str) – name of the case to load

Returns

pyna settings

Return type

Settings

optimize_trajectory_takeoff_noise(n_sideline=1, init_traj_name=None) → None

Compute aircraft take-off trajectory and noise signature.

Parameters

• n_sideline (int) – Number of sideline microphones to use in the control optimization

• init_traj_name (str) – Name of initialization trajectory (in output folder of case).

Returns

None

plot_noise_contours() → None

Plot noise contours around take-off trajectory.

Returns

None

plot_noise_source_distribution(time_step: numpy.int64, metric: str, components: list) → None

plot_noise_time_series(metric: str) → None

Plot the noise metric along the trajectory.

Parameters

metric (str) – noise metric to plot. Specify ‘pnlt’ or ‘oaspl’/

Returns

None

static plot_optimizer_convergence_data(file_name: str)

Plot the convergence data of the optimization across iterates.

Parameters

file_name (str) – name of the IPOPT output file

Returns

None

plot_trajectory(problem, *problem_verify)

static save_time_series(problem: openmdao.core.problem.Problem, settings: pyNA.src.settings.Settings, ac: Dict[str, Any], path_save_name: str, engine_save_name: str) → None

Save a time_series solution of the trajectory calculations.

Parameters

• problem (om.Problem) – Openmdao probolem to save.

• settings (Settings) – pyna settings

• ac (Aircraft) – aircraft parameters

• path_save_name (str) – name of the time_series path save file

• engine_save_name (str) – name of the time_series engine save file

Returns

None