# Copyright 2025 ISAE-SUPAERO, https://www.isae-supaero.fr/en/ # Copyright 2025 IRT Saint Exupéry, https://www.irt-saintexupery.com # # This program is free software; you can redistribute it and/or # modify it under the terms of the GNU General Public License # version 3 as published by the Free Software Foundation. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU # General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software Foundation, # Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. """ Prospective aircraft design accounting for technology maturing ============================================================== """ from jax import vmap from matplotlib.lines import Line2D from matplotlib.patches import Patch from matplotlib.pyplot import subplots from numpy import array from numpy import linspace from noads.application.base_objects import categories_mission from noads.application.base_objects import category_conso from noads.application.base_objects import propulsion_architectures from noads.application.base_objects import propulsion_mission from noads.application.base_objects import tech_params_lower_mid_upper_2020_2040_2060 from noads.core.models.fleet.aircraft_design import AircraftDesign from noads.core.models.fleet.aircraft_operation import AircraftOperation from noads.core.models.fleet.aircraft_tech_parameter import AircraftTechParameter # %% # Aircraft Technology Evolution # ----------------------------- # Let's start by initializing the time evolution of some key aircraft technology # components, according to several literature sources. data_sources = { "NASA": { "battery_specific_energy": array([[2035, 2040], [500, 750]]), "emotor_specific_power": array([[2022, 2035, 2035], [2, 13, 16]]), "lh2tank_gravimetric_index": array([[], []]), "fuelcell_specific_power": array([[], []]), "fuelcell_efficiency": array([[2035], [55.2]]), "electronics_specific_power": array([[2035, 2040, 2050], [13, 19, 26]]), "struct_weight_factor": array([[], []]), }, "IATA": { "battery_specific_energy": array([[2020, 2030, 2035], [200, 225, 400]]), "emotor_specific_power": array([[], []]), "lh2tank_gravimetric_index": array([ [2025, 2035, 2035, 2050], [20, 35, 50, 70], ]), "fuelcell_specific_power": array([[2025, 2040, 2050], [4, 8, 10]]), "fuelcell_efficiency": array([[], []]), "electronics_specific_power": array([[], []]), "struct_weight_factor": array([ [2030, 2030, 2030, 2030, 2030, 2050, 2050, 2050, 2050, 2050, 2050, 2050], [86, 80, 88, 83, 90, 80, 72, 68, 70, 83, 73, 61], ]), }, "ATI": { "battery_specific_energy": array([[2025], [220]]), "emotor_specific_power": array([[2026, 2035, 2050], [13, 23, 25]]), "lh2tank_gravimetric_index": array([ [2035, 2035, 2040, 2040, 2040, 2050, 2050, 2050], [47, 58, 61, 66, 72, 64, 69, 75], ]), "fuelcell_specific_power": array([ [2025, 2030, 2035, 2050], [1.5, 2.0, 2.5, 5.0], ]), "fuelcell_efficiency": array([[2025, 2030, 2035, 2050], [60, 65, 70, 75]]), "electronics_specific_power": array([[2026, 2030, 2050], [8.92, 20, 30]]), "struct_weight_factor": array([[2025, 2030, 2035, 2050], [80, 75, 70, 60]]), }, "ICCT": { "battery_specific_energy": array([[2025, 2030, 2050], [250, 300, 500]]), "emotor_specific_power": array([[], []]), "lh2tank_gravimetric_index": array([[2035, 2035], [20, 35]]), "fuelcell_specific_power": array([[2035, 2035], [1, 2]]), "fuelcell_efficiency": array([[2035, 2035], [45, 65]]), "electronics_specific_power": array([[2025], [8]]), "struct_weight_factor": array([[], []]), }, "EASA": { "battery_specific_energy": array([[2022], [210]]), "emotor_specific_power": array([[2022, 2022], [57.6 / 22.7, 73.5 / 56.6]]), "lh2tank_gravimetric_index": array([[], []]), "fuelcell_specific_power": array([[], []]), "fuelcell_efficiency": array([[], []]), "electronics_specific_power": array([[], []]), "struct_weight_factor": array([[], []]), }, "Adler et al. (2025)": { "battery_specific_energy": array([[2023, 2030], [300, 600]]), "emotor_specific_power": array([[2025], [8]]), "lh2tank_gravimetric_index": array([[2035], [50]]), "fuelcell_specific_power": array([[2035, 2050], [2, 6]]), "fuelcell_efficiency": array([[2035, 2050], [60, 75]]), "electronics_specific_power": array([[], []]), "struct_weight_factor": array([[], []]), }, } markers_sources = { "NASA": "o", "IATA": "s", "ATI": "d", "ICCT": "X", "EASA": "*", "Adler et al. (2025)": "P", } units = { "battery_specific_energy": "Wh/kg", "emotor_specific_power": "kW/kg", "electronics_specific_power": "kW/kg", "fuelcell_specific_power": "kW/kg", "lh2tank_gravimetric_index": "%", "fuelcell_efficiency": "%", "struct_weight_factor": "%", } name_to_fullname = { "battery_specific_energy": "Battery Specific Energy", "emotor_specific_power": "E-motor Specific Power", "electronics_specific_power": "Power electronics Specific Power", "fuelcell_specific_power": "Fuel cell Specific Power", "lh2tank_gravimetric_index": "LH2 tank Gravimetric Index", "fuelcell_efficiency": "Fuel cell Efficiency", "struct_weight_factor": "Structural weight reduction", } propulsion_colors = { "JetA-GasTurbine": "maroon", "Battery-Electric": "limegreen", "lH2-FuelCell": "royalblue", "lH2-GasTurbine": "orangered", } # %% # Now let's visualize how the Technology Evolution scenarios compare with these sources. years = linspace(2020, 2060, 41) fig1, axes1 = subplots( 2, 4, figsize=(12, 7), layout="constrained", ) tech_params = {} for i, (name, values) in enumerate(tech_params_lower_mid_upper_2020_2040_2060.items()): lower, mid, upper = values lower_param = AircraftTechParameter(name, (lower[0], lower[1], lower[2])) mid_param = AircraftTechParameter(name, (mid[0], mid[1], mid[2])) upper_param = AircraftTechParameter(name, (upper[0], upper[1], upper[2])) tech_params[name] = (lower_param, mid_param, upper_param) lower_values = lower_param.value_at_entry_into_service(years) mid_values = mid_param.value_at_entry_into_service(years) upper_values = upper_param.value_at_entry_into_service(years) ax = axes1.flat[i] ax.fill_between( years, lower_values, upper_values, alpha=0.2, color="k", label="Upper-to-Lower", ) ax.plot(years, lower_values, "k-", linewidth=2, label="Lower") ax.plot(years, mid_values, "k:", linewidth=2, label="Mid") ax.set_title(name_to_fullname[name], fontsize="medium") ax.set_ylabel(units[name]) ax.set_xlabel("Year") for source, source_data in data_sources.items(): x_data = source_data[name][0] y_data = source_data[name][1] ax.plot( x_data, y_data, markers_sources[source], label=source, markersize=8, ) # axes1[0, 0].legend(loc="upper left", framealpha=0.4) axes1[-1, -1].clear() axes1[-1, -1].set_axis_off() legend_handles = [ Line2D([0], [0], color="k", ls="-", lw=2, label="Lower"), Line2D([0], [0], color="k", ls=":", lw=2, label="Mid"), Patch(alpha=0.2, facecolor="k", label="Upper-to-Lower"), ] legend_handles.extend([ Line2D([0], [0], ls="None", marker=marker, markersize=8, label=name, color=f"C{j}") for j, (name, marker) in enumerate(markers_sources.items()) ]) axes1[-1, -1].legend(handles=legend_handles, loc="center") fig1.suptitle( "Aircraft Technology Parameters evolution", fontsize="large", ) fig1.savefig("./aircraft_technology.pdf") print( "Aircraft technology parameters evolution figure saved as 'aircraft_technology.pdf'" ) # show() # %% # Prospective Aircraft Design # --------------------------- # Now for each year of Entry-Into-Service we'll design an airplane using GAM and compare # their overall empty weight and mission energy consumption. # # Unfeasible designs may yield infinite mass and energy, therefore the (mass/energy) # metric is calculated on a per (seat-km) basis and then inverted. They are therefore a # measure of traffic per (mass/energy). The highest the metric the lower the # (mass/energy). fig2, axes2 = subplots(2, 3, layout="constrained", figsize=(12, 8)) fig2.suptitle("Aircraft Energy Efficiency\n[seat km / MJ]", fontsize="x-large") fig3, axes3 = subplots(2, 3, layout="constrained", figsize=(12, 8)) fig3.suptitle("Aircraft Empty-Mass Efficiency\n[seat km / kg]", fontsize="x-large") categories = categories_mission.keys() ymax = 4.0 for cat, category in enumerate(categories): max_range = 1e-3 * categories_mission[category]["range"] seat = categories_mission[category]["npax"] ax_e = axes2.flat[cat] ax_m = axes3.flat[cat] current_energy_per_ask = category_conso[category] reference_aircraft = AircraftOperation( name=f"{category}_RECENT", propulsion=None, energy_per_ask=current_energy_per_ask[1], recent=True, ) ax_e.hlines( y=1.0 / current_energy_per_ask[0], xmin=years[0], xmax=years[-1], colors="dimgray", linestyles="-", linewidth=1.5, ) ax_e.hlines( y=1.0 / current_energy_per_ask[1], xmin=years[0], xmax=years[-1], colors="dimgray", linestyles=":", linewidth=1.5, ) ax_e.fill_between( [years[0], years[-1]], [1.0 / current_energy_per_ask[0], 1.0 / current_energy_per_ask[0]], [1.0 / current_energy_per_ask[2], 1.0 / current_energy_per_ask[2]], alpha=0.4, color="dimgray", ) for _prop, propulsion in enumerate(propulsion_architectures.keys()): ask_per_energy_low_to_up = [] ask_per_owe_low_to_up = [] for tech_idx in range(3): aircraft = AircraftDesign( name=f"{category}_{propulsion}", propulsion=None, mission={ **categories_mission[category], **propulsion_mission[propulsion], "category": category, }, power_system=propulsion_architectures[propulsion], aircraft_tech_params=[ tech_param[tech_idx] for tech_name, tech_param in tech_params.items() ], reference_aircraft=reference_aircraft, ) model = aircraft.design_model() model.discipline.jax_out_func = vmap(model.discipline.jax_out_func) outputs = model.discipline.execute({ f"{category}_{propulsion}.entry_into_service": years }) ask_per_energy_low_to_up.append( 1.0 / outputs[f"{category}_{propulsion}.energy_per_ask"] ) ask_per_owe_low_to_up.append( seat * max_range / outputs[f"{category}_{propulsion}.owe"] ) ax_e.fill_between( years, ask_per_energy_low_to_up[0], ask_per_energy_low_to_up[-1], alpha=0.2, color=propulsion_colors[propulsion], ) ax_e.plot( years, ask_per_energy_low_to_up[0], color=propulsion_colors[propulsion], linestyle="-", linewidth=3, ) ax_e.plot( years, ask_per_energy_low_to_up[1], color=propulsion_colors[propulsion], linestyle=":", linewidth=3, ) ax_m.fill_between( years, ask_per_owe_low_to_up[0], ask_per_owe_low_to_up[-1], alpha=0.2, color=propulsion_colors[propulsion], ) ax_m.plot( years, ask_per_owe_low_to_up[0], color=propulsion_colors[propulsion], linestyle="-", linewidth=3, ) ax_m.plot( years, ask_per_owe_low_to_up[1], color=propulsion_colors[propulsion], linestyle=":", linewidth=3, ) ax_e.set_ylim(ymin=0.0, ymax=ymax) ax_e.set_title( f"{category.replace('_', ' ')}\n({seat} seat, {max_range} km)", fontsize="large", ) ax_m.set_ylim(ymin=0.0) ax_m.set_title( f"{category.replace('_', ' ')}\n({seat} seat, {max_range} km)", fontsize="large", ) axes2[-1, -1].clear() axes2[-1, -1].set_axis_off() axes3[-1, -1].clear() axes3[-1, -1].set_axis_off() # Manually add legend handles = [ Patch(color=color, label=prop_name) for prop_name, color in propulsion_colors.items() ] handles.append(Patch(color="dimgray", label="Current Technology")) axes2[-1, -1].legend( handles=handles, loc="center", ) axes2[-1, 0].set_xlabel("Entry-Into-Service") axes3[-1, -1].legend( handles=[ Patch(color=color, label=prop_name) for prop_name, color in propulsion_colors.items() ], loc="center", ) axes3[-1, 0].set_xlabel("Entry-Into-Service") fig2.savefig("./aircraft_prospective_energy.pdf") fig3.savefig("./aircraft_prospective_mass.pdf") print( "Figures saved as aircraft_prospective_energy.pdf and aircraft_prospective_mass.pdf" ) # show()