# RCAIDE/Library/Methods/Performance/compute_noise_certification_data.py
#
#
# Created: Jul 2023, M. Clarke
# ----------------------------------------------------------------------------------------------------------------------
# IMPORT
# ----------------------------------------------------------------------------------------------------------------------
# RCAIDE imports
import RCAIDE
from RCAIDE.Framework.Core import Units , Data
import matplotlib.colors
import matplotlib.colors as colors
from RCAIDE.Library.Plots import *
# Pacakge imports
import numpy as np
from matplotlib import pyplot as plt
import plotly.graph_objects as go
# ----------------------------------------------------------------------
# Compute Aircraft Noise Certification Data
# ----------------------------------------------------------------------
[docs]
def compute_noise_certification_data(approach_mission = None,
takeoff_mission = None,
noise_level = None,
min_noise_level = 45,
max_noise_level = 105,
noise_scale_label = "Max. SPL [dbA]",
save_figure = False,
show_figure = True,
save_filename = "Certification_Noise",
colormap = 'jet',
file_type = ".png",
width = 12,
height = 6):
"""Calculates the noise at certification points as well as the noise contours of approach and takeoff.
A combined approach-takeoff noisec contour is also created
"""
if approach_mission == None:
raise AssertionError('Approach mission not specifed!')
if takeoff_mission == None:
raise AssertionError('Takeoff mission not specifed!')
microphone_x_resolution = 401
microphone_y_resolution = 9
noise_times_steps = 51
number_of_microphone_in_stencil = 1800
# update weights analysis
for segment in approach_mission.segments:
if segment.analyses.noise == None:
raise AssertionError('Noise analysis not specifed!')
noise_analysis = segment.analyses.noise
noise_analysis.settings.microphone_x_resolution = microphone_x_resolution
noise_analysis.settings.microphone_y_resolution = microphone_y_resolution
noise_analysis.settings.noise_times_steps = noise_times_steps
noise_analysis.settings.number_of_microphone_in_stencil = number_of_microphone_in_stencil
noise_analysis.settings.microphone_min_y = 1E-6
noise_analysis.settings.microphone_max_y = 1800
noise_analysis.settings.microphone_min_x = 1E-6
noise_analysis.settings.microphone_max_x = 8000
# update weights analysis
for segment in takeoff_mission.segments:
if segment.analyses.noise == None:
raise AssertionError('Noise analysis not specifed!')
noise_analysis = segment.analyses.noise
noise_analysis.settings.microphone_x_resolution = microphone_x_resolution
noise_analysis.settings.microphone_y_resolution = microphone_y_resolution
noise_analysis.settings.noise_times_steps = noise_times_steps
noise_analysis.settings.number_of_microphone_in_stencil = number_of_microphone_in_stencil
noise_analysis.settings.microphone_min_y = 1E-6
noise_analysis.settings.microphone_max_y = 1800
noise_analysis.settings.microphone_min_x = -2000 + 1E-6
noise_analysis.settings.microphone_max_x = 6000
# evaluate both missions
approach_results = approach_mission.evaluate()
takeoff_results = takeoff_mission.evaluate()
# post process results
noise_data = post_process_certification_noise_data(approach_results,takeoff_results)
# plot diagram
if show_figure:
fig = plt.figure(save_filename)
fig.set_size_inches(width,height)
noise_levels = np.linspace(min_noise_level,max_noise_level,7)
noise_cmap = plt.get_cmap('turbo')
noise_new_cmap = truncate_colormap(noise_cmap,0.0, 1.0)
noise_level = noise_data.certification_SPL_dBA_max
X = noise_data.certification_microphone_locations[:,:,0]
Y = noise_data.certification_microphone_locations[:,:,1]
ap_noise_level = noise_data.approach_SPL_dBA_max
ap_X = noise_data.approach_microphone_locations[:,:,0]
ap_Y = noise_data.approach_microphone_locations[:,:,1]
ap_POS = noise_data.approach_trajectory
to_noise_level = noise_data.takeoff_SPL_dBA_max
to_X = noise_data.takeoff_microphone_locations[:,:,0]
to_Y = noise_data.takeoff_microphone_locations[:,:,1]
to_POS = noise_data.takeoff_trajectory
axis_0 = fig.add_subplot(2,2,1)
axis_0.set_xlabel('x [m]')
axis_0.set_ylabel('altitude [m]')
axis_1 = fig.add_subplot(2,2,3)
axis_1.set_xlabel('x [m]')
axis_1.set_ylabel('y [m]')
axis_2 = fig.add_subplot(2,2,2)
axis_2.set_xlabel('x [m]')
axis_2.set_ylabel('y [m]')
axis_3 = fig.add_subplot(2,2,4)
axis_3.set_xlabel('x [m]')
axis_3.set_ylabel('y [m]')
# plot aircraft position
axis_0.plot(ap_POS[:,0],-ap_POS[:,2], color = 'black', linestyle = '-' , marker = 'o', linewidth = 2, label= "Approach")
axis_0.plot(to_POS[:,0],-to_POS[:,2], color = 'blue', linestyle = '-' , marker = 's', linewidth = 2, label= "Takeoff")
axis_0.legend(loc='upper center')
# plot aircraft noise levels
CS_11 = axis_1.contourf(X,Y,noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
CS_12 = axis_1.contourf(X,-Y,noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
CS_21 = axis_2.contourf(ap_X,ap_Y,ap_noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
CS_22 = axis_2.contourf(ap_X,-ap_Y,ap_noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
CS_31 = axis_3.contourf(to_X,to_Y,to_noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
CS_32 = axis_3.contourf(to_X,-to_Y,to_noise_level ,noise_levels,cmap = noise_new_cmap,extend='both')
cbar = fig.colorbar(CS_11, ax=axis_1)
cbar.ax.set_ylabel(noise_scale_label, rotation = 90)
cbar = fig.colorbar(CS_11, ax=axis_2)
cbar.ax.set_ylabel(noise_scale_label, rotation = 90)
cbar = fig.colorbar(CS_11, ax=axis_3)
cbar.ax.set_ylabel(noise_scale_label, rotation = 90)
run_way_x_pts = np.linspace(0, 3000, 20)
run_way_y_pts = run_way_x_pts * 0
axis_1.plot(run_way_x_pts,run_way_y_pts, color = 'grey', linestyle = '-' , linewidth = 5 , alpha=0.5)
axis_2.plot(run_way_x_pts,run_way_y_pts, color = 'grey', linestyle = '-' , linewidth = 5 , alpha=0.5)
axis_3.plot(run_way_x_pts,run_way_y_pts, color = 'grey', linestyle = '-' , linewidth = 5 , alpha=0.5)
set_axes(axis_1)
set_axes(axis_2)
set_axes(axis_3)
axis_0.set_title('Flight Trajectory')
axis_1.set_title('Approach and Takeoff Noise')
axis_2.set_title('Approach Noise')
axis_3.set_title('Takeoff Noise')
fig.tight_layout()
if save_figure:
figure_title = save_filename
plt.savefig(figure_title + file_type )
return noise_data
[docs]
def post_process_certification_noise_data(approach_results,takeoff_results):
approach_noise_data = post_process_noise_data(approach_results)
takeoff_noise_data = post_process_noise_data(takeoff_results)
# append approach noise
approach_pos = approach_noise_data.aircraft_position
approach_pos[:,0] -= 2000
# append takeoff noise
cert_SPL_dBA_max = np.max(np.concatenate((approach_noise_data.SPL_dBA,takeoff_noise_data.SPL_dBA), axis = 0) ,axis = 0)
cert_pos = np.concatenate((approach_pos, takeoff_noise_data.aircraft_position), axis = 0)
cert_mic_locs = takeoff_noise_data.microphone_locations
res = Data(
certification_SPL_dBA_max = cert_SPL_dBA_max,
certification_trajectory = cert_pos,
certification_microphone_locations= cert_mic_locs,
approach_SPL_dBA_max = np.max(approach_noise_data.SPL_dBA,axis = 0),
approach_SPL_dBA = approach_noise_data.SPL_dBA,
approach_time = approach_noise_data.time,
approach_trajectory = approach_pos,
approach_microphone_locations = takeoff_noise_data.microphone_locations,
takeoff_SPL_dBA_max = np.max(takeoff_noise_data.SPL_dBA,axis = 0),
takeoff_SPL_dBA = takeoff_noise_data.SPL_dBA,
takeoff_time = takeoff_noise_data.time,
takeoff_trajectory = takeoff_noise_data.aircraft_position,
takeoff_microphone_locations = takeoff_noise_data.microphone_locations
)
print('Certification Noise')
print('-----------------------------------------------')
print('2000 m Approach Noise :', round(cert_SPL_dBA_max[0, 0], 2))
print('6000 m Flyover Noise :', round(cert_SPL_dBA_max[-1, 0], 2))
print('450 m Sideline Noise :', round(max(cert_SPL_dBA_max[:, 2]), 2))
area_85_dbA = len(np.where(cert_SPL_dBA_max.flatten()>85)[0]) *100 / len(cert_SPL_dBA_max.flatten())
area_65_dbA = len(np.where(cert_SPL_dBA_max.flatten()>65)[0]) *100 / len(cert_SPL_dBA_max.flatten())
print('% Area > 85 dBA Threshold:', round(area_85_dbA, 2))
print('% Area > 65 dBA Threshold:', round(area_65_dbA, 2))
res.approach_noise_2000m = cert_SPL_dBA_max[0, 0]
res.flyover_noise_6000m = cert_SPL_dBA_max[-1, 0]
res.sideline_noise_450m = max(cert_SPL_dBA_max[:, 2])
res.area_65_dbA = area_65_dbA
res.area_85_dbA = area_85_dbA
return res
# ------------------------------------------------------------------
# Truncate colormaps
# ------------------------------------------------------------------
[docs]
def truncate_colormap(cmap, minval=0.0, maxval=1.0, n=100):
new_cmap = colors.LinearSegmentedColormap.from_list(
'trunc({n},{a:.2f},{b:.2f})'.format(n=cmap.name, a=minval, b=maxval),
cmap(np.linspace(minval, maxval, n)))
return new_cmap
