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results_emissions_v3.py
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import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.lines as mlines
import numpy as np
from scipy.optimize import curve_fit
# CONFIGURATION
trajectories_to_analyze = {
"sfo_dfw": True,
"malaga": False,
"bos_fll": True,
"cts_tpe": True,
"dus_tos": True,
"gru_lim": True,
"hel_kef": True,
"lhr_ist": True,
"sin_maa": True
}
seasons_to_analyze = {
"2023-02-06": True,
"2023-05-05": True,
"2023-08-06": True,
"2023-11-06": True
}
diurnal_to_analyze = {
"daytime": True,
"nighttime": True
}
engine_models_to_analyze = {
"GTF1990": True,
"GTF2000": True,
"GTF": True,
"GTF2035": True,
"GTF2035_wi": True
}
saf_levels_to_analyze = [0, 20, 100]
water_injection_levels = ["0", "15"]
base_path = 'main_results_figures/results'
# First pass: Determine common altitude range & waypoints per trajectory
altitude_ranges = {}
trajectory_waypoints = {}
for trajectory, trajectory_enabled in trajectories_to_analyze.items():
if not trajectory_enabled:
continue
trajectory_path = os.path.join(base_path, trajectory)
if not os.path.exists(trajectory_path):
print(f"Trajectory folder not found: {trajectory_path}")
continue
min_altitude_trajectory = float('-inf')
max_altitude_trajectory = float('inf')
all_indices = [] # Collect waypoints across all seasons & diurnal conditions
for folder in os.listdir(trajectory_path):
for season, season_enabled in seasons_to_analyze.items():
if not season_enabled or season not in folder:
continue
for diurnal, diurnal_enabled in diurnal_to_analyze.items():
if not diurnal_enabled or diurnal not in folder:
continue
climate_path = os.path.join(trajectory_path, folder, 'climate/mees/era5model')
if not os.path.exists(climate_path):
print(f"Climate folder not found: {climate_path}")
continue
dfs = {}
for engine, engine_enabled in engine_models_to_analyze.items():
if not engine_enabled:
continue
for saf in ([0] if engine in ["GTF1990", "GTF2000", "GTF"] else saf_levels_to_analyze):
for water_injection in (["15"] if engine == "GTF2035_wi" else ["0"]):
pattern = f"{engine}_SAF_{saf}_A20N_full_WAR_{water_injection}_climate.csv"
file_path = os.path.join(climate_path, pattern)
if not os.path.exists(file_path):
continue
df = pd.read_csv(file_path)
dfs[(engine, saf, water_injection)] = df
if not dfs:
continue
# Find common altitude range
min_altitude = max(df['altitude'].min() for df in dfs.values())
max_altitude = min(df['altitude'].max() for df in dfs.values())
min_altitude_trajectory = max(min_altitude_trajectory, min_altitude)
max_altitude_trajectory = min(max_altitude_trajectory, max_altitude)
# Collect waypoints for common indices calculation (only within the altitude range)
for df in dfs.values():
df_filtered = df[(df['altitude'] >= min_altitude_trajectory) & (df['altitude'] <= max_altitude_trajectory)]
all_indices.append(set(df_filtered['index']))
# Determine final common waypoints for this trajectory
if all_indices:
common_indices = set.intersection(*all_indices)
print(f"{trajectory}: Found {len(common_indices)} common waypoints across all seasons & diurnal conditions.")
else:
print(f"{trajectory}: No common waypoints found across all conditions.")
continue # Skip this trajectory
# Store results for second pass
altitude_ranges[trajectory] = (min_altitude_trajectory, max_altitude_trajectory)
trajectory_waypoints[trajectory] = common_indices # Store waypoints per trajectory
# Second pass: Load and apply the common altitude & waypoints
dataframes = []
for trajectory, trajectory_enabled in trajectories_to_analyze.items():
if not trajectory_enabled:
continue
trajectory_path = os.path.join(base_path, trajectory)
if not os.path.exists(trajectory_path):
continue
min_altitude_trajectory, max_altitude_trajectory = altitude_ranges[trajectory]
common_indices = trajectory_waypoints[trajectory]
for folder in os.listdir(trajectory_path):
for season, season_enabled in seasons_to_analyze.items():
if not season_enabled or season not in folder:
continue
for diurnal, diurnal_enabled in diurnal_to_analyze.items():
if not diurnal_enabled or diurnal not in folder:
continue
climate_path = os.path.join(trajectory_path, folder, 'climate/mees/era5model')
if not os.path.exists(climate_path):
continue
dfs = {}
for engine, engine_enabled in engine_models_to_analyze.items():
if not engine_enabled:
continue
for saf in ([0] if engine in ["GTF1990", "GTF2000", "GTF"] else saf_levels_to_analyze):
for water_injection in (["15"] if engine == "GTF2035_wi" else ["0"]):
pattern = f"{engine}_SAF_{saf}_A20N_full_WAR_{water_injection}_climate.csv"
file_path = os.path.join(climate_path, pattern)
if not os.path.exists(file_path):
continue
df = pd.read_csv(file_path)
# Apply trimming based on the precomputed ranges
df = df[(df['altitude'] >= min_altitude_trajectory) & (df['altitude'] <= max_altitude_trajectory) & (df['index'].isin(common_indices))]
df['trajectory'] = trajectory
df['season'] = season
df['diurnal'] = diurnal
df['engine'] = engine
df['saf_level'] = saf
df['water_injection'] = water_injection
# Print final length per engine
print(f"Final Trim {trajectory} - {season} - {diurnal} - Engine: {engine}, SAF: {saf}, WAR: {water_injection} → Length: {len(df)}")
dfs[(engine, saf, water_injection)] = df
if not dfs:
continue
# Verify if all engines for this flight setup have the same length
lengths = [len(df) for df in dfs.values()]
unique_lengths = set(lengths)
if len(unique_lengths) == 1:
print(f"FINAL CHECK: {trajectory} - All flights now have the same length ({unique_lengths.pop()} rows)")
# Append the filtered data
for df in dfs.values():
#Ensure 'cocip_atr20' exists in df, fillwith 0 if missing
if 'cocip_atr20' not in df.columns:
df['cocip_atr20'] = 0
selected_columns = [
'index', 'time', 'fuel_flow', 'ei_nox', 'nvpm_ei_n',
'thrust_setting_meem', 'TT3', 'PT3', 'FAR', 'specific_humidity_gsp',
'flight_phase', 'trajectory', 'season', 'diurnal', 'engine', 'saf_level', 'water_injection',
'accf_sac_aCCF_O3', 'accf_sac_aCCF_CH4', 'accf_sac_aCCF_CO2', 'ei_co2_conservative',
'ei_co2_optimistic', 'cocip_atr20', 'accf_sac_contrails_atr20', 'accf_sac_aCCF_H2O'
]
dataframes.append(df[selected_columns].copy())
else:
print(f"{trajectory}: Length mismatch across flights! Unique lengths: {unique_lengths}")
print("Skipping this dataset to maintain consistency.")
print("\n")
# CONCATENATE ALL FLIGHTS INTO ONE DATAFRAME
final_df = pd.concat(dataframes, ignore_index=True)
dt = 60
final_df['nox'] = final_df['ei_nox']*final_df['fuel_flow']*dt #unit is kg (kg/kg fuel * kg fuel/s * s )
final_df['nvpm_n'] = final_df['nvpm_ei_n']*final_df['fuel_flow']*dt #unit is # (#/kg fuel * kg fuel/s * s )
print(f"Collected {len(final_df)} rows from {len(dataframes)} flight data files.")
# Add calculations per waypoint to final_df
final_df['nox_impact'] = final_df['fuel_flow'] * dt * (final_df['accf_sac_aCCF_O3'] + final_df['accf_sac_aCCF_CH4'] * 1.29) * final_df['ei_nox']
final_df['co2_impact_cons'] = final_df['fuel_flow'] * dt * final_df['accf_sac_aCCF_CO2'] * final_df['ei_co2_conservative']
final_df['co2_impact_opti'] = final_df['fuel_flow'] * dt * final_df['accf_sac_aCCF_CO2'] * final_df['ei_co2_optimistic']
final_df['h2o_impact'] = final_df['fuel_flow'] * dt * final_df['accf_sac_aCCF_H2O'] * final_df['ei_co2_conservative']
final_df['contrail_atr20_cocip'] = final_df['cocip_atr20'].fillna(0) if 'cocip_atr20' in final_df.columns else 0
final_df['contrail_atr20_accf'] = final_df['accf_sac_contrails_atr20']
# Calculate climate impact per waypoint
final_df['climate_non_co2'] = final_df['nox_impact'] + final_df['h2o_impact'] + final_df['contrail_atr20_cocip']
final_df['climate_total_cons'] = final_df['climate_non_co2'] + final_df['co2_impact_cons']
final_df['climate_total_opti'] = final_df['climate_non_co2'] + final_df['co2_impact_opti']
# print(final_df['fuel_flow'])
# Map engine names for display in the plot
engine_display_names = {
'GTF1990': 'CFM1990/2000', # Combined label
'GTF2000': 'CFM1990/2000', # Combined label
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
# Use Matplotlib colors for consistency
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:blue'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]} # Next matplotlib color
}
# X-axis variables and their units for labeling (not titles)
x_vars = {
'TT3': ('TT3', 'TT3 (K)'),
'PT3': ('PT3', 'PT3 (bar)'),
'FAR': ('FAR', 'FAR (-)'),
'specific_humidity_gsp': ('Specific Humidity', 'Specific Humidity [kg/kg]')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
axs = axs.flatten()
legend_handles = {}
seen_cfm = False
for i, (x_var, (title_label, x_label)) in enumerate(x_vars.items()):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[final_df['engine'] == engine]
if not subset.empty:
ax.scatter(subset[x_var], subset['ei_nox'] * 1000,
label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
# Collect unique legend handles - combine CFM1990/2000 into one
if engine in ['GTF1990', 'GTF2000']:
if not seen_cfm:
legend_handles['CFM1990/2000'] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None', markersize=8, label='CFM1990/2000'
)
seen_cfm = True
else:
if engine_display_names[engine] not in legend_handles:
legend_handles[engine_display_names[engine]] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None', markersize=8, label=engine_display_names[engine]
)
ax.set_xlabel(x_label)
ax.set_ylabel(f'$EI_{{\\mathrm{{NOx}}}}$ (g/ kg Fuel)')
ax.set_title(f'$EI_{{\\mathrm{{NOx}}}}$ vs {title_label}')
# Place legend **in the top-left corner** of the **first plot (TT3 vs EI NOx)**
axs[0].legend(handles=legend_handles.values(), loc='upper left', title="Engine")
plt.tight_layout()
plt.savefig('results_report/emissions/nox_emissions_tt3_pt3_far_scatter.png', format='png')
# #plt.show()
# Engine display names and colors
engine_display_names = {
'GTF1990': 'CFM1990',
'GTF2000': 'CFM2000',
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:orange'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]}
}
x_vars = {
'TT3': ('TT3', 'TT3 (K)'),
'PT3': ('PT3', 'PT3 (bar)'),
'FAR': ('FAR', 'FAR (-)'),
'thrust_setting_meem': ('Thrust Setting', 'Thrust Setting (-)')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
axs = axs.flatten()
legend_handles = {}
for i, (x_var, (title_label, x_label)) in enumerate(x_vars.items()):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine)]
# Exclude SAF != 0
if engine in ['GTF2035', 'GTF2035_wi']:
subset = subset[subset['saf_level'] == 0]
if not subset.empty:
ax.scatter(subset[x_var], subset['nvpm_ei_n'],
label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
if engine_display_names[engine] not in legend_handles:
legend_handles[engine_display_names[engine]] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None',
markersize=8, label=engine_display_names[engine]
)
ax.set_xlabel(x_label)
ax.set_ylabel(f'$EI_{{\\mathrm{{nvPM,number}}}}$ (# / kg Fuel)')
ax.set_title(f'$EI_{{\\mathrm{{nvPM,number}}}}$ vs {title_label}')
ax.set_yscale('log')
plt.tight_layout()
axs[1].legend(handles=legend_handles.values(), loc='lower right', title="Engine")
plt.subplots_adjust(hspace=0.3, wspace=0.25)
plt.savefig('results_report/emissions/nvpm_emissions_no_saf_scatter.png', format='png')
# #plt.show()
# #plt.show()
# Consistent engine display names and colors as in previous plots
engine_display_names = ['CFM1990', 'CFM2000', 'GTF', 'GTF2035', 'GTF2035WI']
engine_colors = {
'CFM1990': 'tab:blue',
'CFM2000': 'tab:orange',
'GTF': 'tab:green',
'GTF2035': 'tab:red',
'GTF2035WI': 'purple' # Matplotlib default color cycle index 4 is purple
}
# ICAO data
thrust_setting_icao = [0.07, 0.3, 0.85, 1]
icao_data = {
'GTF': [5.78e15, 3.85e14, 1.60e15, 1.45e15],
'GTF2035': [5.78e15, 3.85e14, 1.60e15, 1.45e15],
'GTF2035WI': [5.78e15, 3.85e14, 1.60e15, 1.45e15],
'CFM1990': [4.43e15, 9.03e15, 2.53e15, 1.62e15],
'CFM2000': [7.98e14, 4.85e14, 1.39e15, 1.02e15],
}
# Plot
plt.figure(figsize=(8, 6))
for engine in engine_display_names:
plt.plot(thrust_setting_icao, icao_data[engine], marker='o', label=engine, color=engine_colors[engine])
plt.xlabel('Thrust Setting (-)')
plt.ylabel(f'$EI_{{\\mathrm{{nvPM,number}}}}$ (# / kg Fuel)')
plt.title('ICAO $EI_{{\\mathrm{{nvPM,number}}}}$ vs Thrust Setting (SLS)')
plt.yscale('log')
plt.legend(title='Engine')
plt.grid(True, which='both', linestyle='--', linewidth=1.0)
plt.savefig('results_report/emissions/nvpm_emissions_icao_lto.png', format='png')
# #plt.show()
# Use Matplotlib default color cycle for consistency
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_display_names = {
'GTF2035_0': 'GTF2035 SAF0',
'GTF2035_20': 'GTF2035 SAF20',
'GTF2035_100': 'GTF2035 SAF100',
'GTF2035_wi_0': 'GTF2035WI SAF0',
'GTF2035_wi_20': 'GTF2035WI SAF20',
'GTF2035_wi_100': 'GTF2035WI SAF100'
}
# Colors based on Plot 1 (use same color for GTF2035WI across SAF levels)
engine_groups = {
'GTF2035_0': {'marker': 's', 'color': 'tab:red'},
'GTF2035_20': {'marker': 's', 'color': 'tab:pink'},
'GTF2035_100': {'marker': 's', 'color': 'tab:grey'},
'GTF2035_wi_0': {'marker': 'D', 'color': default_colors[4]}, # Same as in Plot 1
'GTF2035_wi_20': {'marker': 'D', 'color': 'tab:olive'},
'GTF2035_wi_100': {'marker': 'D', 'color': 'tab:cyan'}
}
x_vars = {
'TT3': ('TT3', 'TT3 (K)'),
'PT3': ('PT3', 'PT3 (bar)'),
'FAR': ('FAR', 'FAR (-)'),
'thrust_setting_meem': ('Thrust Setting', 'Thrust Setting (-)')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
axs = axs.flatten()
legend_handles = {}
for i, (x_var, (title_label, x_label)) in enumerate(x_vars.items()):
ax = axs[i]
for engine, style in engine_groups.items():
base_engine = 'GTF2035' if 'wi' not in engine else 'GTF2035_wi'
saf_level = int(engine.split('_')[-1])
subset = final_df[(final_df['engine'] == base_engine) & (final_df['saf_level'] == saf_level)]
if not subset.empty:
ax.scatter(subset[x_var], subset['nvpm_ei_n'],
label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
if engine_display_names[engine] not in legend_handles:
legend_handles[engine_display_names[engine]] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None',
markersize=8, label=engine_display_names[engine]
)
ax.set_xlabel(x_label)
ax.set_ylabel(f'$EI_{{\\mathrm{{nvPM,number}}}}$ (# / kg Fuel)')
ax.set_title(f'$EI_{{\\mathrm{{nvPM,number}}}}$ vs {title_label}')
ax.set_yscale('log')
plt.tight_layout()
axs[1].legend(handles=legend_handles.values(), loc='lower right', title="Engine")
plt.subplots_adjust(hspace=0.3, wspace=0.25)
plt.savefig('results_report/emissions/nvpm_emissions_saf_scatter.png', format='png')
# #plt.show()
# Engine display names and colors
engine_display_names = {
'GTF1990': 'CFM1990',
'GTF2000': 'CFM2000',
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:orange'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]}
}
x_vars = {
'TT3': ('TT3', 'TT3 (K)'),
'PT3': ('PT3', 'PT3 (bar)'),
'FAR': ('FAR', 'FAR (-)'),
'thrust_setting_meem': ('Thrust Setting', 'Thrust Setting (-)')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
axs = axs.flatten()
legend_handles = {}
for i, (x_var, (title_label, x_label)) in enumerate(x_vars.items()):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine)]
# Exclude SAF != 0
if engine in ['GTF2035', 'GTF2035_wi']:
subset = subset[subset['saf_level'] == 0]
if not subset.empty:
ax.scatter(subset[x_var], subset['fuel_flow'],
label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
if engine_display_names[engine] not in legend_handles:
legend_handles[engine_display_names[engine]] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None',
markersize=8, label=engine_display_names[engine]
)
ax.set_xlabel(x_label)
ax.set_ylabel('Fuel Flow (kg/s)')
ax.set_title(f'Fuel Flow vs {title_label}')
plt.tight_layout()
axs[1].legend(handles=legend_handles.values(), loc='lower right', title="Engine")
plt.subplots_adjust(hspace=0.3, wspace=0.25)
plt.savefig('results_report/emissions/fuel_flow_no_saf_scatter.png', format='png')
# #plt.show()
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_display_names = {
'GTF2035_0': 'GTF2035 SAF0',
'GTF2035_20': 'GTF2035 SAF20',
'GTF2035_100': 'GTF2035 SAF100',
'GTF2035_wi_0': 'GTF2035WI SAF0',
'GTF2035_wi_20': 'GTF2035WI SAF20',
'GTF2035_wi_100': 'GTF2035WI SAF100'
}
engine_groups = {
'GTF2035_0': {'marker': 's', 'color': 'tab:red'},
'GTF2035_20': {'marker': 's', 'color': 'tab:pink'},
'GTF2035_100': {'marker': 's', 'color': 'tab:grey'},
'GTF2035_wi_0': {'marker': 'D', 'color': default_colors[4]},
'GTF2035_wi_20': {'marker': 'D', 'color': 'tab:olive'},
'GTF2035_wi_100': {'marker': 'D', 'color': 'tab:cyan'}
}
x_vars = {
'TT3': ('TT3', 'TT3 (K)'),
'PT3': ('PT3', 'PT3 (bar)'),
'FAR': ('FAR', 'FAR (-)'),
'thrust_setting_meem': ('Thrust Setting', 'Thrust Setting (-)')
}
fig, axs = plt.subplots(2, 2, figsize=(12, 10))
axs = axs.flatten()
legend_handles = {}
for i, (x_var, (title_label, x_label)) in enumerate(x_vars.items()):
ax = axs[i]
for engine, style in engine_groups.items():
base_engine = 'GTF2035' if 'wi' not in engine else 'GTF2035_wi'
saf_level = int(engine.split('_')[-1])
subset = final_df[(final_df['engine'] == base_engine) & (final_df['saf_level'] == saf_level)]
if not subset.empty:
ax.scatter(subset[x_var], subset['fuel_flow'],
label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
if engine_display_names[engine] not in legend_handles:
legend_handles[engine_display_names[engine]] = mlines.Line2D(
[], [], color=style['color'], marker=style['marker'], linestyle='None',
markersize=8, label=engine_display_names[engine]
)
ax.set_xlabel(x_label)
ax.set_ylabel('Fuel Flow (kg/s)')
ax.set_title(f'Fuel Flow vs {title_label}')
plt.tight_layout()
axs[1].legend(handles=legend_handles.values(), loc='lower right', title="Engine")
plt.subplots_adjust(hspace=0.3, wspace=0.25)
plt.savefig('results_report/emissions/fuel_flow_saf_scatter.png', format='png')
plt.show()
# Define flight phases
"""ei nox vs waypoint """
flight_phases = ['climb', 'cruise', 'descent']
phase_titles = {'climb': 'Climb Phase', 'cruise': 'Cruise Phase', 'descent': 'Descent Phase'}
# Default engine display names and colors
engine_display_names = {
'GTF1990': 'CFM1990',
'GTF2000': 'CFM2000',
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:orange'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]}
}
saf_colors = {
('GTF2035', 0): 'tab:red',
('GTF2035', 20): 'tab:pink',
('GTF2035', 100): 'tab:grey',
('GTF2035_wi', 0): default_colors[4],
('GTF2035_wi', 20): 'tab:olive',
('GTF2035_wi', 100): 'tab:cyan'
}
# Scatter Plots - Without SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == 0)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
ax.scatter(subset['index'], subset['ei_nox'] * 1000, label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
if phase == 'climb':
ax.set_xlim(0, subset['index'].max() + 5)
else:
ax.set_xlim(subset['index'].min() - 5, subset['index'].max() + 5)
axs[0].set_ylabel(f'$EI_{{\\mathrm{{NOx}}}}$')
fig.suptitle(f"$EI_{{\\mathrm{{NOx}}}}$ vs Time in Minutes (Without SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[2].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
# Extract color from scatter plot (PathCollection)
rgba_color = handle.get_facecolor()[0] # Extract first color in RGBA format
solid_color = (rgba_color[0], rgba_color[1], rgba_color[2], 1.0) # Force alpha to 1.0
marker = handle.get_paths()[0] # Keep marker style consistent
legend_handles.append(mlines.Line2D([], [], color=solid_color, marker=marker,
linestyle='None', markersize=8, label=label))
# Apply the updated handles to the legend
axs[2].legend(handles=legend_handles, loc='upper left', title='Engine', frameon=True, markerscale=1.0)
plt.savefig('results_report/emissions/ei_nox_no_saf_scatter_waypoints.png', format='png')
# plt.show()
# Scatter Plots - With SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine in ['GTF2035', 'GTF2035_wi']:
for saf in [0, 20, 100]:
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == saf)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
label = f"{engine_display_names[engine]} SAF{saf}"
ax.scatter(subset['index'], subset['ei_nox'] * 1000, label=label, marker=engine_groups[engine]['marker'],
color=saf_colors[(engine, saf)], alpha=0.3, s=10)
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
if phase == 'climb':
ax.set_xlim(0, subset['index'].max() + 5)
else:
ax.set_xlim(subset['index'].min() - 5, subset['index'].max() + 5)
axs[0].set_ylabel(f'$EI_{{\\mathrm{{NOx}}}}$ (g / kg Fuel)')
fig.suptitle(f"$EI_{{\\mathrm{{NOx}}}}$ vs Time in Minutes (With SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[2].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
# Extract color from scatter plot (PathCollection)
rgba_color = handle.get_facecolor()[0] # Extract first color in RGBA format
solid_color = (rgba_color[0], rgba_color[1], rgba_color[2], 1.0) # Force alpha to 1.0
marker = handle.get_paths()[0] # Keep marker style consistent
legend_handles.append(mlines.Line2D([], [], color=solid_color, marker=marker,
linestyle='None', markersize=8, label=label))
# Apply the updated handles to the legend
axs[2].legend(handles=legend_handles, loc='upper left', title='Engine & SAF Level', frameon=True, markerscale=1.0)
plt.savefig('results_report/emissions/ei_nox_saf_scatter_waypoints.png', format='png')
# plt.show()
# Function to introduce NaN gaps in the dataset
def introduce_gaps(df):
df = df.sort_values(by='index')
index_diff = df['index'].diff()
gap_threshold = 5 # Define a threshold to introduce NaNs when gaps appear
df.loc[index_diff > gap_threshold, 'ei_nox'] = None # Assign NaN where gaps exist
return df
# Function to ensure gaps persist in average computation
def compute_avg_with_gaps(df):
df = introduce_gaps(df)
avg_subset = df.groupby('index')['ei_nox'].mean() * 1000
return avg_subset.reindex(range(df['index'].min(), df['index'].max() + 1))
# Line Plots - Without SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == 0)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)] # Apply climb phase filter
if not subset.empty:
avg_subset = compute_avg_with_gaps(subset)
ax.plot(avg_subset.index, avg_subset.values, linestyle='-', color=style['color'], alpha=1.0, label=engine_display_names[engine])
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
# ax.legend(title="Engine")
axs[0].set_ylabel(f'$EI_{{\\mathrm{{NOx}}}}$ (g/kg Fuel)')
fig.suptitle(f"$EI_{{\\mathrm{{NOx}}}}$ Average vs Time in Minutes (Without SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[2].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
legend_handles.append(mlines.Line2D([], [], color=handle.get_color(), linestyle='-', linewidth=2, label=label))
# Apply the updated handles to the legend
axs[2].legend(handles=legend_handles, loc='upper left', title='Engine', frameon=True)
plt.savefig('results_report/emissions/ei_nox_no_saf_average_waypoints.png', format='png')
# plt.show()
# Line Plots - With SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine in ['GTF2035', 'GTF2035_wi']:
for saf in [0, 20, 100]:
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == saf)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)] # Apply climb phase filter
if not subset.empty:
avg_subset = compute_avg_with_gaps(subset)
ax.plot(avg_subset.index, avg_subset.values, linestyle='-', color=saf_colors[(engine, saf)], alpha=1.0,
label=f"{engine_display_names[engine]} SAF{saf}")
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
# ax.legend(title="Engine & SAF Level")
axs[0].set_ylabel(f'$EI_{{\\mathrm{{NOx}}}}$ (g/kg Fuel)')
fig.suptitle(f"$EI_{{\\mathrm{{NOx}}}}$ Average vs Time in Minutes (With SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[2].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
legend_handles.append(mlines.Line2D([], [], color=handle.get_color(), linestyle='-', linewidth=2, label=label))
# Apply the updated handles to the legend
axs[2].legend(handles=legend_handles, loc='upper left', title='Engine & SAF Level', frameon=True)
plt.savefig('results_report/emissions/ei_nox_saf_average_waypoints.png', format='png')
# plt.show()
"""ei nvPM"""
# Define flight phases
flight_phases = ['climb', 'cruise', 'descent']
phase_titles = {'climb': 'Climb Phase', 'cruise': 'Cruise Phase', 'descent': 'Descent Phase'}
# Default engine display names and colors
engine_display_names = {
'GTF1990': 'CFM1990',
'GTF2000': 'CFM2000',
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:orange'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]}
}
saf_colors = {
('GTF2035', 0): 'tab:red',
('GTF2035', 20): 'tab:pink',
('GTF2035', 100): 'tab:grey',
('GTF2035_wi', 0): default_colors[4],
('GTF2035_wi', 20): 'tab:olive',
('GTF2035_wi', 100): 'tab:cyan'
}
# Function to introduce NaN gaps in the dataset
def introduce_gaps(df):
df = df.sort_values(by='index')
index_diff = df['index'].diff()
gap_threshold = 5 # Define a threshold to introduce NaNs when gaps appear
df.loc[index_diff > gap_threshold, 'nvpm_ei_n'] = None # Assign NaN where gaps exist
return df
# Function to ensure gaps persist in average computation
def compute_avg_with_gaps(df):
df = introduce_gaps(df)
avg_subset = df.groupby('index')['nvpm_ei_n'].mean()
return avg_subset.reindex(range(df['index'].min(), df['index'].max() + 1))
# Scatter Plots - Without SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == 0)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
ax.scatter(subset['index'], subset['nvpm_ei_n'], label=engine_display_names[engine], marker=style['marker'],
color=style['color'], alpha=0.3, s=10)
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
ax.set_yscale('log')
# ax.legend(title="Engine")
axs[0].set_ylabel('$EI_{{\\mathrm{{nvPM,number}}}}$ (#/kg fuel)')
fig.suptitle("$EI_{{\\mathrm{{nvPM,number}}}}$ vs Time in Minutes (Without SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[0].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
# Extract color from scatter plot (PathCollection)
rgba_color = handle.get_facecolor()[0] # Extract first color in RGBA format
solid_color = (rgba_color[0], rgba_color[1], rgba_color[2], 1.0) # Force alpha to 1.0
marker = handle.get_paths()[0] # Keep marker style consistent
legend_handles.append(mlines.Line2D([], [], color=solid_color, marker=marker,
linestyle='None', markersize=8, label=label))
# Apply the updated handles to the legend
axs[0].legend(handles=legend_handles, loc='lower left', title='Engine', frameon=True, markerscale=1.0)
plt.savefig('results_report/emissions/ei_nvpm_no_saf_scatter_waypoints.png', format='png')
# plt.show()
# Scatter Plots - With SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine in ['GTF2035', 'GTF2035_wi']:
for saf in [0, 20, 100]:
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == saf)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
label = f"{engine_display_names[engine]} SAF{saf}"
ax.scatter(subset['index'], subset['nvpm_ei_n'], label=label, marker=engine_groups[engine]['marker'],
color=saf_colors[(engine, saf)], alpha=0.3, s=10)
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
ax.set_yscale('log')
# ax.legend(title="Engine & SAF Level")
axs[0].set_ylabel('$EI_{{\\mathrm{{nvPM,number}}}}$ (#/kg fuel)')
fig.suptitle("$EI_{{\\mathrm{{nvPM,number}}}}$ vs Time in Minutes (With SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[0].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
# Extract color from scatter plot (PathCollection)
rgba_color = handle.get_facecolor()[0] # Extract first color in RGBA format
solid_color = (rgba_color[0], rgba_color[1], rgba_color[2], 1.0) # Force alpha to 1.0
marker = handle.get_paths()[0] # Keep marker style consistent
legend_handles.append(mlines.Line2D([], [], color=solid_color, marker=marker,
linestyle='None', markersize=8, label=label))
# Apply the updated handles to the legend
axs[0].legend(handles=legend_handles, loc='lower left', title='Engine & SAF Level', frameon=True, markerscale=1.0)
plt.savefig('results_report/emissions/ei_nvpm_saf_scatter_waypoints.png', format='png')
# plt.show()
# Line Plots - Without SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine, style in engine_groups.items():
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == 0)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
avg_subset = compute_avg_with_gaps(subset)
ax.plot(avg_subset.index, avg_subset.values, linestyle='-', color=style['color'], alpha=1.0, label=engine_display_names[engine])
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
ax.set_yscale('log')
# ax.legend(title="Engine")
axs[0].set_ylabel(f'$EI_{{\\mathrm{{nvPM,number}}}}$ (#/kg fuel)')
fig.suptitle(f"$EI_{{\\mathrm{{nvPM,number}}}}$ Average vs Time in Minutes (Without SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[0].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
legend_handles.append(mlines.Line2D([], [], color=handle.get_color(), linestyle='-', linewidth=2, label=label))
# Apply the updated handles to the legend
axs[0].legend(handles=legend_handles, loc='lower left', title='Engine', frameon=True)
plt.savefig('results_report/emissions/ei_nvpm_no_saf_average_waypoints.png', format='png')
# plt.show()
# Line Plots - With SAF
fig, axs = plt.subplots(1, 3, figsize=(18, 6), sharey=True)
for i, phase in enumerate(flight_phases):
ax = axs[i]
for engine in ['GTF2035', 'GTF2035_wi']:
for saf in [0, 20, 100]:
subset = final_df[(final_df['engine'] == engine) & (final_df['flight_phase'] == phase) & (final_df['saf_level'] == saf)]
subset = subset[(subset['flight_phase'] != 'climb') | (subset['index'] <= 39)]
if not subset.empty:
avg_subset = compute_avg_with_gaps(subset)
ax.plot(avg_subset.index, avg_subset.values, linestyle='-', color=saf_colors[(engine, saf)], alpha=1.0,
label=f"{engine_display_names[engine]} SAF{saf}")
ax.set_xlabel('Time in minutes')
ax.set_title(phase_titles[phase])
ax.set_yscale('log')
# ax.legend(title="Engine & SAF Level")
axs[0].set_ylabel(f'$EI_{{\\mathrm{{nvPM,number}}}}$ (#/kg fuel)')
fig.suptitle(f"$EI_{{\\mathrm{{nvPM,number}}}}$ Average vs Time in Minutes (With SAF)", fontsize=14)
plt.tight_layout()
handles, labels = axs[0].get_legend_handles_labels()
legend_handles = []
for handle, label in zip(handles, labels):
legend_handles.append(mlines.Line2D([], [], color=handle.get_color(), linestyle='-', linewidth=2, label=label))
# Apply the updated handles to the legend
axs[0].legend(handles=legend_handles, loc='lower left', title='Engine & SAF Level', frameon=True)
plt.savefig('results_report/emissions/ei_nvpm_saf_average_waypoints.png', format='png')
# plt.show()
"""FUEL FLOW"""
# Define flight phases
flight_phases = ['climb', 'cruise', 'descent']
phase_titles = {'climb': 'Climb Phase', 'cruise': 'Cruise Phase', 'descent': 'Descent Phase'}
# Default engine display names and colors
engine_display_names = {
'GTF1990': 'CFM1990',
'GTF2000': 'CFM2000',
'GTF': 'GTF',
'GTF2035': 'GTF2035',
'GTF2035_wi': 'GTF2035WI'
}
default_colors = plt.rcParams['axes.prop_cycle'].by_key()['color']
engine_groups = {
'GTF1990': {'marker': '^', 'color': 'tab:blue'},
'GTF2000': {'marker': '^', 'color': 'tab:orange'},
'GTF': {'marker': 'o', 'color': 'tab:green'},
'GTF2035': {'marker': 's', 'color': 'tab:red'},
'GTF2035_wi': {'marker': 'D', 'color': default_colors[4]}
}
saf_colors = {
('GTF2035', 0): 'tab:red',
('GTF2035', 20): 'tab:pink',
('GTF2035', 100): 'tab:grey',
('GTF2035_wi', 0): default_colors[4],
('GTF2035_wi', 20): 'tab:olive',
('GTF2035_wi', 100): 'tab:cyan'
}
# Function to introduce NaN gaps in the dataset
def introduce_gaps(df):
df = df.sort_values(by='index')
index_diff = df['index'].diff()
gap_threshold = 5 # Define a threshold to introduce NaNs when gaps appear
df.loc[index_diff > gap_threshold, 'fuel_flow'] = None # Assign NaN where gaps exist
return df
# Function to ensure gaps persist in average computation