# RCAIDE/Library/Plots/Emissions/plot_contrails_appleman_chart
#
#
# Created: Jul 2024, M. Clarke
# ----------------------------------------------------------------------------------------------------------------------
# IMPORT
# ----------------------------------------------------------------------------------------------------------------------
from RCAIDE.Framework.Core import Units
from RCAIDE.Library.Plots.Common import set_axes, plot_style
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
# ----------------------------------------------------------------------------------------------------------------------
# PLOTS
# ----------------------------------------------------------------------------------------------------------------------
## @ingroup Library-Plots-Performance-Emissions
[docs]
def plot_contrails_appleman_chart(results,
save_figure = False,
show_legend = True,
save_filename = "Appleman_Chart" ,
file_type = ".png",
width = 8, height = 6):
"""
Generate plots showing CO2-equivalent emissions and emission indexes for various fuel species over mission segments.
Parameters
----------
results : Data
Mission results data structure containing:
results.segments[i].conditions.emissions.total with fields:
- CO2 : array
Carbon dioxide emissions [kg]
- NOx : array
Nitrogen oxide emissions [kg]
- H2O : array
Water vapor emissions [kg]
- Contrails : array
Contrail formation impact [kg CO2e]
- Soot : array
Particulate emissions [kg]
- SO2 : array
Sulfur dioxide emissions [kg]
save_figure : bool, optional
Save figure to file if True, default False
show_legend : bool, optional
Display segment legend if True, default True
save_filename : str, optional
Name for saved figure file, default "CO2e_Emissions"
file_type : str, optional
File extension for saved figure, default ".png"
width : float, optional
Figure width in inches, default 11
height : float, optional
Figure height in inches, default 7
Returns
-------
fig : matplotlib.figure.Figure
Figure showing stacked emissions contributions
Notes
-----
Creates a stacked area plot showing:
- Individual contributions from each emission type
- Cumulative total CO2-equivalent impact
- Breakdown by mission segment
- Time history of emissions
Different emission types are distinguished by fill colors
and segments use different shades from the inferno colormap.
**Definitions**
'CO2-equivalent (CO2e)'
Combined climate impact normalized to CO2
'Global Warming Potential (GWP)'
Relative impact factor for different emissions
'Contrail Impact'
Climate forcing from aviation-induced cloudiness
"""
# get plotting style
ps = plot_style()
parameters = {'axes.labelsize': ps.axis_font_size,
'xtick.labelsize': ps.axis_font_size,
'ytick.labelsize': ps.axis_font_size,
'axes.titlesize': ps.title_font_size}
plt.rcParams.update(parameters)
fig = plt.figure(save_filename)
fig.set_size_inches(width,height)
line_colors = cm.inferno(np.linspace(0,0.9,len(results.segments)))
# always contrains left of line
always_contrails_line = np.array([[-59.43367168358419, 101.16672500291688],
[-55.971298564928254, 149.10512192276286],
[-53.23976198809942, 202.20977715552448],
[-51.07478707268698, 254.72173608680438],
[-48.994516392486304, 316.4531559911329],
[-46.60133006650334, 405.2829308132073],
[-44.84984249212462, 479.10862209777156],
[-44.453622681134064, 500.4456889511142]])
maximum_T_for_persistence_line = np.array([[-56.842608797106536, 101.24139540310355],
[-54.4251545910629, 130.12950647532384],
[-52.33321666083305, 163.0428188076071],
[-50.644965581612425, 193.0626531326567],
[-49.120289347800735, 227.11235561778093],
[-47.59607980399021, 262.314782405787],
[-45.83479173958699, 311.93326332983315],
[-44.31361568078405, 354.628398086571],
[-43.59584645898963, 381.73842025434607],
[-41.52140940380353, 457.87889394469727],
[-40.9651149224128, 483.83152490957883],
[-40.64753237661884, 499.40263679850665]])
# no contrails right of line
no_contrails_line = np.array([[-50.76957181192394, 100.84004200210016],
[-48.83514175708787, 122.79780655699457],
[-45.85649282464124, 165.53494341383742],
[-43.44603896861511, 211.71391902928485],
[-41.35970131839927, 258.4599229961499],
[-40.39878660599698, 285.0005833625015],
[-38.638431921596094, 336.9245128923113],
[-36.80200676700503, 400.95438105238594],
[-35.366468323416186, 455.17442538793614],
[-34.2513125656283, 500.73970365184925]])
# An Appleman chart can be used to identify more and less favorable conditions. Using the vehicle's mission, estimate the
# time the vehicle will spend at a lower temperature and/or higher pressure than the 0% relative humidity on the Appleman chart.
# Also use the vehicle's mission, estimate the time the vehicle will spend at temperatures and pressures
# between the 0% relative humidity and maximum temperature for persistence on the Appleman chart.
# plot bounds of Appleman chart
axis = fig.add_subplot(1, 1, 1)
axis.plot(always_contrails_line[:,0], always_contrails_line[:,1], label = "Always Contrails", linewidth = ps.line_width, linestyle = '-', color = 'black')
axis.plot(maximum_T_for_persistence_line[:,0], maximum_T_for_persistence_line[:,1], label = "Maximum T for Persistence", linewidth = ps.line_width, color = 'black', linestyle = '--')
axis.plot(no_contrails_line[:,0], no_contrails_line[:,1], label = "No Contrails", linewidth = ps.line_width, color = 'black', linestyle = ':')
axis.set_xlabel('Temperature (°C)')
axis.set_ylabel('Pressure (hPa)')
axis.set_ylim(100,500)
axis.set_xlim(-60,-30)
axis.invert_yaxis()
# start with 0 time in each region
always_contrails_time_sec =0
persistence_time_sec = 0
# get line colors for plots
line_colors = cm.inferno(np.linspace(0,0.9,len(results.segments)))
# loop through mission segments and plot conditions on Appleman chart
for i in range(len(results.segments)):
always_contrails_time_stamps = []
persistence_time_stamps = []
no_contrails_time_stamps = []
T = results.segments[i].conditions.freestream.temperature[:,0] - 273.15
P = results.segments[i].conditions.freestream.pressure[:,0]/100
t = results.segments[i].conditions.frames.inertial.time[:,0]
# rediscretize temperature pressure and time to ensure points are captured within appleman chart
time = np.linspace(t[0], t[-1], 200)
temperature = np.interp(time, t, T)
pressure = np.interp(time, t, P)
segment_tag = results.segments[i].tag
segment_name = segment_tag.replace('_', ' ')
# if any of the pressure values are above 500 hPa, print a warning that the conditions are outside the bounds of the Appleman chart
if np.any(pressure < 500):
axis.scatter(temperature, pressure, label =segment_name, color = line_colors[i], s = 50)
# compute time spent in each region based in temperature and pressure conditions
for j in range(len(temperature)):
if temperature[j] < np.interp(pressure[j], always_contrails_line[:,1], always_contrails_line[:,0]):
always_contrails_time_stamps.append(time[j])
if temperature[j] > np.interp(pressure[j], no_contrails_line[:,1], no_contrails_line[:,0]):
no_contrails_time_stamps.append(time[j])
# compute time spent between 0% relative humidity (always contrails) and maximum temperature for persistence on the Appleman chart
if temperature[j] > np.interp(pressure[j], always_contrails_line[:,1], always_contrails_line[:,0]) and temperature[j] < np.interp(pressure[j], maximum_T_for_persistence_line[:,1], maximum_T_for_persistence_line[:,0]):
persistence_time_stamps.append(time[j])
# convert time stamps into numpy arrays and compute total time spent in each region
always_contrails_time_stamps = np.array(always_contrails_time_stamps)
persistence_time_stamps = np.array(persistence_time_stamps)
no_contrails_time_stamps = np.array(no_contrails_time_stamps)
# use differences between time stamps to compute total time spent in each region
always_contrails_time_sec += np.sum(np.diff(always_contrails_time_stamps))
persistence_time_sec += np.sum(np.diff(persistence_time_stamps))
else:
print(segment_name + "Segment has pressure values outside the bounds of the Appleman chart. Time spent in each region may be inaccurate.")
# add annotations for each region of the Appleman chart
axis.text(-58, 400, 'Always Contrails', fontsize = 12
, color = 'black')
axis.text(-50, 220, 'Maybe Contrails', fontsize = 12
, color = 'black')
axis.text(-38, 150, 'No Contrails', fontsize = 12
, color = 'black')
t_fin = results.segments[-1].conditions.frames.inertial.time[-1,0]
no_contrails_time_sec = t_fin - always_contrails_time_sec - persistence_time_sec
# percent of flight in each regine
always_contrails_time_percent = (always_contrails_time_sec/t_fin)*100
persistence_time_percent = (persistence_time_sec/t_fin)*100
no_contrails_time_percent = (no_contrails_time_sec/t_fin)*100
print(f"Percent of flight in Always Contrails region: {always_contrails_time_percent:.2f}%")
print(f"Percent of flight in Persistence region: {persistence_time_percent:.2f}%")
print(f"Percent of flight in No Contrails region: {no_contrails_time_percent:.2f}%")
always_contrails_time_hr = always_contrails_time_sec/3600
persistence_time_hr = persistence_time_sec/3600
no_contrails_time_hr = no_contrails_time_sec/3600
# time spend in each region
print(f"Time spent in Always Contrails region: {always_contrails_time_hr:.2f} hours")
print(f"Time spent in Persistence region: {persistence_time_hr:.2f} hours")
print(f"Time spent in No Contrails region: {no_contrails_time_hr:.2f} hours")
if show_legend:
leg = fig.legend(bbox_to_anchor=(0.5, 0.95), loc='upper center', ncol = 4)
leg.set_title('Flight Segment', prop={'size': ps.legend_font_size, 'weight': 'heavy'})
# Adjusting the sub-plots for legend
# set title of plot
title_text = 'Appleman Chart'
fig.tight_layout()
fig.suptitle(title_text)
fig.subplots_adjust(top=0.8)
if save_figure:
plt.savefig(save_filename + file_type)
return fig
