Method for modeling an aviation sustainability strategy

Abstract

A method (900) of modeling sustainability strategies (116) for the aviation industry is presented. The method (900) includes receiving (910), at one or more processors (1010), flight traffic information (102) for a plurality of flights for a selected time period. A traffic growth model (110) is applied (930) to the flight traffic information (102) to generate projected flight traffic information for a future time period. Baseline calculations (112) are performed (940) on the projected flight information to generate predicted aviation emissions information for the future time period. User input (104) is received (950), at the one or more processors (1010), the user input (104) indicating one or more parameters for each of one or more of a plurality of sustainability strategies (116). The one or more of the plurality of sustainability strategies (116) are applied (960) to the predicted aviation emissions information to generate adjusted predicted aviation emissions information. Based on the adjusted predicted aviation emissions information, an emissions reduction potential is indicated (970) for the future time period.

Description

Technical Field

[0001] This disclosure generally involves modeling the sustainability and emissions data of one or more aircraft. Background Technology

[0002] Civil aviation has committed to achieving net-zero carbon emissions by 2050, but there is no very detailed plan yet on how to achieve this. With the emergence of new technologies, all aspects of aviation will evolve over time—including the aircraft themselves, operational efficiency, energy forms, and market-based measures. These aspects are deeply interconnected. Summary of the Invention

[0003] A method for modeling sustainability strategies in the aviation industry is proposed. This method includes receiving flight traffic information from multiple flights within a selected time period at one or more processors. A traffic growth model is applied to the flight traffic information to generate projected flight traffic information for future time periods. Baseline calculations are performed on the projected flight traffic information to generate projected aviation emissions information for future time periods. User input, indicating one or more parameters of each of one or more sustainability strategies, is received at one or more processors. One or more of the sustainability strategies are applied to the projected aviation emissions information to generate adjusted projected aviation emissions information. Based on the adjusted projected aviation emissions information, emission reduction potential for future time periods is indicated.

[0004] This disclosure is provided to introduce, in a simplified form, a selection of concepts further described in the detailed description. This disclosure is not intended to identify key or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. The claimed subject matter is not limited to embodiments that address any shortcomings pointed out in any part of this disclosure. Attached Figure Description

[0005] Figure 1 An example modeling pipeline for implementing sustainable aviation strategies is illustrated schematically.

[0006] Figure 2 An example energy lifecycle assessment model is shown, which includes a modeling pipeline for well-to-tank and tank-to-wake emissions.

[0007] Figure 3 Example implementation curves for fleet renewal and fleet growth are shown.

[0008] Figure 4 An example model of aircraft route allocation is schematically depicted.

[0009] Figure 5 This is a graph showing two example fleet insertion curves.

[0010] Figure 6 It is a chart showing methods for calculating the energy efficiency of existing, reference, and user-defined aircraft.

[0011] Figure 7 This is a schematic diagram of hydrogen production and distribution.

[0012] Figure 8 An example visual output of the emission reduction potential from the modeling pipeline is shown.

[0013] Figure 9 A flowchart illustrating an example approach for modeling sustainability strategies in the aviation industry is shown.

[0014] Figure 10 An aspect of the example computing system is illustrated schematically. Detailed Implementation

[0015] Highly detailed simulation tools exist for designing new technologies related to aircraft, operational efficiency solutions, energy systems, and other relevant components of the aviation ecosystem. However, no tool exists for modeling the system-of-systems problem of aviation decarbonization, which necessarily requires consideration of the interaction between new forms of renewable energy and aircraft technologies. Previous solutions either consider emissions associated with the global economy as a whole or focus on the detailed design and simulation of subsystems. These models do not consider the impact of aviation at a detailed level. A concrete approach is needed to model emissions from aviation and provide solutions for reducing these emissions.

[0016] The publicly disclosed modeling pipeline operates on a database containing historically scheduled passenger and cargo flight traffic. In some examples, filters are applied to the dataset if the user wishes to analyze a specific market segment or geographic region. Traffic growth models can then be applied to project flight traffic information for future time periods. Baseline calculations can be run on the projected data to calculate aviation emissions information (such as fuel combustion, energy consumption, and CO2 emissions) for each route. Following this preprocessing, models representing sustainability strategies can be applied to determine their emission reduction potential. Emissions other than CO2 (such as contrails, NOx (nitrogen oxides), SOx (sulfur oxides), etc.) can also be modeled. New approaches to mitigating climate impacts (including novel alternative fuels, carbon removal, and techno-economics) may be integrated into the modeling pipeline.

[0017] The disclosed modeling pipeline may be an integrated system architecture model that represents the interrelationships between the aircraft, operations / operations, energy, and carbon offsetting. This enables users to perform highly interdisciplinary scenario analyses. The modeling pipeline can be implemented in a server-authoritative architecture with APIs that handle client requests and provide data for visualization.

[0018] The disclosed methods and systems can be used as tools for airlines and researchers to assess different roadmaps for reducing aviation emissions and ultimately achieving net-zero emissions, while providing an understanding of how all the different aspects of these strategies are interconnected. Airlines and aircraft manufacturers can use these methods to evaluate strategies for developing technologies and what types of partnerships are needed within the energy industry. The results of modeling using these methods can be presented to policymakers, legislators, and stakeholders such as airlines, energy companies, and banks to influence effective solutions for achieving net-zero emissions. This can help achieve cross-sectoral coordination on feasible technological and policy actions, which will enable civil aviation to achieve its emissions targets.

[0019] Figure 1An example modeling pipeline 100 is schematically illustrated, which can operate on a database containing a flight traffic dataset 102 containing passenger and cargo flights scheduled for a given time period (e.g., one year). In some examples, a global dataset may be provided. However, user input 104 may be received, instructing preprocessing 106 of the flight traffic dataset 102, such as applying filtering 108 to the flight traffic dataset 102 to restrict the dataset to specific market segments, carriers, or geographic regions. A traffic growth model 110 may then be applied to generate projected flight traffic information for future time periods. Baseline calculation 112 may be run on the (filtered) projected dataset to calculate projected aviation emissions information (e.g., fuel combustion, energy consumption, and emissions (e.g., CO2 emissions)) for each route based on values ​​from the flight traffic dataset 102. One or more renewable energy reduction arrays may be applied to the dataset to provide a baseline for renewable energy emission reduction (e.g., based on present-day indicators).

[0020] Following this preprocessing, the model representing sustainability strategy 116 is applied to time-series flight traffic data. Sustainability strategy 116 may include aircraft strategy model 118 (e.g., fleet renewal model component 120, future aircraft model component 122), operational efficiency strategy model 124, energy strategy model 126 (e.g., electricity model component 128, hydrogen model component 130, sustainable aviation fuel (SAF) model component 132), and market-based measures model 134. Each model can function according to user-configured settings. The disclosed method may include a component-based architecture where each model representing a single sustainability strategy can run independently in any desired order, however, a default order may be provided. If all strategies are implemented simultaneously, and emission differences are allowed to be attributed to specific models, running each model sequentially can output an overall reduction in emissions.

[0021] Model components can utilize supplementary datasets as needed, such as aircraft characteristics or lifecycle carbon intensity of processes. Optionally, the non-CO2 emission model 136 can also be applied to predicted aviation emissions information to indicate aggregate climate impact over future time periods. At each stage, aggregate indicators such as the overall reduction in CO2 emissions can be calculated and saved as output indicators 138. This information can be used for post-processing to generate visualizations 140 that show the changes in indicators for different strategies. After all strategies have been applied, additional indicators (such as the insertion rate of new aircraft into the fleet or the total electricity required by aviation annually) can be calculated based on the final indicators.

[0022] The baseline flight traffic dataset 102 can be derived from real-world flight data over a time period (e.g., one year), which can be processed, cleaned, validated, and aggregated. In some examples, the flight traffic dataset 102 may include aviation emissions information for multiple flights within a selected time period. The flight traffic dataset 102 may include multiple historical flight data sets, including flight metadata such as origin / destination pairs, operators, aircraft type, energy use, fuel combustion, emissions, passenger load factor, cargo load factor, weather conditions, etc. The flight dataset may include flight data for a single year or multiple years. In one example, the flight dataset includes flight data since 2019, the year on which civil aviation's 2050 emissions reduction targets are based. Newly acquired data can be added to the flight dataset and used to adjust projected models. The model may have a model start year and a model end year, which can be default values ​​(e.g., 2019 and 2050) or selected by the user.

[0023] Overall, the published method predicts how sustainability strategies (such as traffic growth, aircraft, operations, and energy) affect each route, aircraft type in the fleet, and fuel consumption per year. The processed flight traffic datasets can be aggregated based on unique combinations of aircraft type, route, operator, year, and / or other parameters. Routes flown by each operator using each aircraft type and per year can be aggregated by flight volume.

[0024] Each route can be defined by its origin-destination pair or departure and arrival airports. Airports are defined by their unique International Air Transport Association (IATA) identification codes. The latitude and longitude of each airport are used to determine the great circle distance (gcd) of a given route. The gcd of each route can be used for fuel combustion, energy, and emissions calculations via the route's corrected flight distance or congestion time.

[0025] Flights can also be designated as domestic or international. For example, airport IATA codes can be mapped to countries, and each route can be labeled international if the origin and destination airports are associated with different countries. Following this definition, flights between a country and its associated territories can be labeled as domestic flights. For example, from an emissions accounting perspective, a flight from the adjacent continental United States to Puerto Rico might be considered domestic. This distinction relates to emissions calculations, as emissions from international flights fall under the purview of the International Civil Aviation Organization (ICAO) and are subject to the International Aviation Carbon Offset and Reduction Scheme (CORSIA), while emissions from domestic flights are part of each country's individual emissions inventory.

[0026] Finally, whether a flight is designated as a passenger flight or a cargo flight can be determined separately from the aircraft type designation, and is therefore a separate attribute. Each flight in the baseline flight traffic dataset can include load factors, whether it is passenger load and belly cargo on a passenger flight or cargo load on a cargo flight.

[0027] User input 104 can provide input variables and parameters for various models, model components, and modules in modeling pipeline 100. Default values ​​can be used in the absence of specific user input. Users can input parameters and assumptions (such as model load factors). Users can request or specify energy production systems and parameters, associated lifecycle emissions for each energy carrier, etc. International flights may account for energy production in the destination country.

[0028] Users can include staff from airline sustainability organizations whose task is to define sustainability goals and how to achieve them. Researchers in the aviation and energy industries can use these methods to further understand how their areas of expertise relate to other areas of sustainability.

[0029] Preprocessing 106 can be applied to refine the flight dataset 102. For example, flights can be excluded, summarized, averaged, etc. Raw data and metadata can be formatted for use in subsequent models and modules. Flight data can be organized on a per-route basis based on origin and destination airports.

[0030] As an example, user input 104 could include parameters for data filtering 108. For instance, a user could restrict the flight dataset to passenger flights and / or flights within North America only. Different countries might have different subsidies for different renewable energy sources, different energy production resources, etc. For instance, a user could restrict modeling to direct emissions or life-cycle emissions.

[0031] The traffic growth model 110 can be applied to preprocessed air traffic datasets (e.g., from one year) to calculate traffic volume on each route for each future year, generating a set of projected flight traffic information. Users can set custom annual growth rates (CAGR) for all flights or use a default growth forecast that predicts the growth rate by region. The traffic growth model is applied to several flights on each route, from which other indicators (such as fuel combustion, energy consumption, and emissions) can be projected for the next few years.

[0032] Input variables for a traffic growth model may include the number of flights on routes in a given year, the compound annual growth rate of all air traffic, the compound annual growth rate of intra-regional air traffic, the start and end years of the model, the estimated annual tonnage on routes (from the air traffic dataset), the estimated annual fuel combustion on routes (from the air traffic dataset), the estimated annual energy consumption on routes (from the air traffic dataset), and the estimated annual lifecycle emissions on routes (e.g., kg of CO2 emissions) (from the air traffic dataset).

[0033] The traffic growth model selected by the user determines how CAGR is defined. If the user chooses a custom CAGR, a single CAGR value can be applied to all routes in the dataset for all years in the forecast. If the user chooses the default traffic growth forecast, the CAGR value can be set for each route by geographic region. This means that CAGR does not necessarily have the same value for all routes; instead, it depends on the countries where the origin and destination airports of the route are located. Once the CAGR for a specific route has been specified, the CAGR value can be used to calculate flight traffic for the next few years, with the growth rate being a function of the region. Traffic growth modeling can be performed separately for passenger aircraft and cargo aircraft, and passenger aircraft and cargo aircraft can also have different CAGR values.

[0034] Baseline calculations (112) are used to calculate associated emissions, fuel combustion, etc., for a given flight path, aircraft type, carrier, etc. Baseline calculations can be applied to projected flight traffic information to generate future information for each flight path (e.g., on a yearly basis). In examples where flight traffic datasets include aviation emissions, baseline calculations can be applied to projected flight traffic information to generate predicted aviation emissions information. Calculations may also be affected by aircraft load, weather conditions, etc.

[0035] The input variables for baseline calculation 112 may include aircraft type (e.g., as defined using ICAO names), carbon intensity of the energy carrier used for a particular aircraft type, number of seats associated with each aircraft type (for passenger flights), average weight per passenger (for passenger flights), average cargo capacity for each aircraft type, belly cargo load factor, passenger load factor, aircraft load factor for dedicated cargo aircraft, and low calorific value of jet fuel (e.g., gravitational energy density of the fuel).

[0036] The output variables of the baseline calculation 112 may include energy consumption per route, greenhouse gas emissions per route, payload capacity per aircraft type, average payload per aircraft type, and a series of variables (including gross ton-kilometers, revenue ton-kilometers, revenue passenger capacity, revenue freight capacity, available capacity, available passenger capacity, and available freight capacity) expressed as a function of each aircraft type, route, and year.

[0037] Payload capacities can take into account both passenger and cargo transport. For passenger aircraft, payload capacity can be calculated based on standard passenger weight, available seating capacity, and belly cargo capacity. For cargo aircraft, payload capacity may simply be a characteristic of the aircraft type.

[0038] In some examples, gross ton-kilometers can be defined as the available traffic capacity by aircraft type and route. Available capacity can be divided into contributions from passengers and cargo, and is given by total available passenger ton-kilometers and total available cargo ton-kilometers. Revenue payload index can describe the actual payload (e.g., passengers and cargo) carried on board, and can be calculated from the payload capacity index using load factors.

[0039] In some examples, capacity can be measured by seats and passengers rather than weight. These metrics (available seat kilometers and revenue passenger kilometers) can be calculated by simply dividing by the weight per passenger based on total available passenger ton-kilometers and revenue passenger ton-kilometers. These metrics can also be calculated directly from flight distance, payload capacity, and carried payload. Annual revenue ton-kilometers can be the sum across all aircraft types and routes. Payload capacity can be converted from revenue payload using a load factor.

[0040] The number of flights for each combination of route and aircraft type can be calculated directly based on the assumed total revenue ton-kilometers. Then, in calculations dealing with traffic growth and aircraft replacement, route distance and payload weight can be considered as factors. The total number of flights can be calculated based on the cumulative total number of flights for each route and each aircraft type.

[0041] For in-service aircraft models, fuel combustion can be used as a function of flight distance and aircraft type to calculate the energy use of aircraft in flight within the baseline flight traffic dataset. Flight distance can be calculated using any suitable method (e.g., by applying a 51-nautical-mile correction based on the uncorrected great circle distance between the origin and destination airports). In some examples, polynomial curve fitting can be used to estimate fuel consumption per aircraft type as a function of two inputs: congestion time and the payload weight carried. Separate functions exist to estimate the congestion time of aircraft flight based on the great circle distance of the flight path in nautical miles.

[0042] The low calorific value (LCV) of jet fuel can be used to convert the fuel combustion of each flight into an equivalent energy use per flight; the LCV is a measure of fuel energy density. Total energy use is then given by multiplying the energy use per flight by the number of flights across all aircraft types, routes, and years. Energy use from other energy types can be considered and summed to determine energy use across all energy carriers on a given route. Emissions of a specific aircraft type on a given route in a given year can be based on the carbon intensity of the energy carriers used by that aircraft. Emissions on each route can be based on the sum of contributions from each aircraft (as a function of the carbon intensity of each associated energy carrier). These values ​​can be summed to derive total lifecycle emissions for all energy carriers across all routes.

[0043] Fuel efficiency is a measure of energy consumption per unit load and distance traveled, where total energy is measured in megajoules (MJ), and the load distance metric in ton-kilometers includes both passenger and freight transport. Emission intensity can be defined similarly to efficiency, but is defined in terms of emissions generated per unit load and distance traveled.

[0044] Fleet size estimation models can be used to estimate the number of physical aircraft required to service traffic (passenger or freight) on an air route. This allows for the determination of metrics (such as the insertion rate of new aircraft) needed to meet a chosen fleet scenario. Input variables may include aircraft type. Applied constants may include the average traffic volume carried by a particular-size aircraft in a year, and correction factors for the average traffic carried by aircraft with different energy carriers (derived from increased turnaround time leading to reduced utilization). Output variables may include the fractional number of aircraft required to service all air traffic on the route, and the annual insertion rate of new aircraft into the fleet.

[0045] To convert the number of flights into the fractional number of aircraft required to serve traffic on routes, the average traffic volume carried by an aircraft per year can be calculated. For passenger flights, this can be done on a permissible seat-kilometer (ASK) basis, while for cargo flights it can be done on a permissible ton-kilometer (ATK) basis. Based on real-world flight traffic volumes for a given year, the average annual traffic volume carried by a specific aircraft type can be calculated across multiple capacity ranges (based on seat count or cargo capacity). Taking into account aircraft that only fly for part of the year (e.g., mid-year deliveries or aircraft grounded), the traffic volume carried by each aircraft can be corrected for based on the number of months in which it is active to arrive at a projected traffic volume for its full-year flight.

[0046] The number of aircraft fractions required to serve traffic on each route can be obtained by multiplying the traffic volume on that route by the average traffic volume carried by an aircraft within its specific capacity class. The average traffic volume carried by an aircraft might assume flight using only conventional jet fuel; aircraft using newer energy carriers such as electricity or hydrogen can carry less traffic per year (due to increased turnaround time). Therefore, a correction factor can be applied to the average traffic volume carried based on its energy type. For example, a correction factor of 0.75 could be applied to hydrogen aircraft, while a correction factor of 0.5 could be applied to battery electric aircraft. The insertion rate of new aircraft on a route is simply the time derivative of the fractional aircraft count.

[0047] Figure 2 An example energy life cycle assessment model 200 for modeling pipeline 100 is shown. The energy life cycle assessment model 200 can be used to calculate total emissions (including well-to-tank emissions 205 and tank-to-wake emissions 210). Upstream of the energy conversion to motion, well-to-tank emissions 205 consider the environmental impacts of extracting, producing, and distributing the energy carrier for aircraft 215. Tank-to-wake or direct emissions 210 include those emissions directly from the processes that convert the energy carrier into motion.

[0048] For example, Figure 2The specification indicates multiple energy sources 220, including fossil fuel extraction 222, biogenic feedstocks 224, carbon capture and waste feedstocks 226, and renewable electricity sources 228. Each energy source can be associated with an environmental impact, such as emissions for generating an equivalent unit of energy. Energy sources 220 can be converted into energy carriers 230. Energy carriers 230 can include fossil jet fuel 232, SAF 234, H2 236, and batteries 238. Extracted fossil fuels 222 can be used to generate fossil jet fuel 232, as well as to generate electricity for the production of H2 236 and batteries 238. Biogenic feedstocks 224 and carbon capture and waste feedstocks 226 can be used to produce SAF. Renewable electricity sources 228 (e.g., wind, thermal, solar energy) can be used to produce SAF 234, H2 236, and batteries 238.

[0049] The energy carrier 230 can be subject to conversion method 240. For example, fossil jet fuel 232, SAF 234, and H2 236 can be used in the internal combustion engine 242. H2 236 can be used to generate fuel cells 244. The battery 238 and fuel cell 244 can be used to drive the electric motor 246. Therefore, the internal combustion engine 242 and the electric motor 246 can be used to drive the aircraft 215.

[0050] The hydrogen, electricity, and SAF (Self-Alkali Fuel) models are interconnected. Electrolyzers can be used to produce hydrogen from water. Therefore, the electricity used for hydrogen production can come from the grid or a separate generating source. SAF can be produced via a power-to-liquid (PtL) method. Therefore, the carbon intensity of SAF and hydrogen production is dependent on electricity. The PtL method can model how the carbon intensity of electricity, hydrogen, and SAF is generated as fuel via the PtL pathway. Feedback loops can be modeled between these energy components in the PtL pathway.

[0051] Back Figure 1 The Renewable Energy Emission Reduction Array 114 can define electricity, hydrogen, and SAF production over time to indicate the corresponding carbon intensity of each energy carrier in a time series. This can be represented relative to a fossil fuel baseline. This provides a baseline that can be adjusted when the user defines additional model parameters.

[0052] One or more sustainability strategies 116 can be applied to preprocessed data and predicted aviation emissions information to model changes in emissions over time based on the applied sustainability strategy. This document describes in detail four main strategy models (Aircraft Strategy Model 118, Operational Efficiency Strategy Model 124, Energy Strategy Model 126, and Market-Based Measures Model 134), but other strategy models may be applied additionally or alternatively. Sustainability strategies 116 can be applied in the order described or in the order provided by the user. Some sustainability strategies inform the same output variables, although not all output variables are necessarily affected by all sustainability strategies. Therefore, feedback loops or iterative processes of different sustainability strategies, not described herein but which should be apparent to those skilled in the art, may exist. For example, an energy model favorable to hydrogen production could inform an aircraft model of increasing the number of hydrogen-fueled aircraft joining the fleet.

[0053] Aircraft strategy model 118 can model the impact of an aircraft fleet that grows and changes over time (e.g., for operators). It will be assumed that the aircraft fleet increases in size over time to meet traffic growth models. Fleet update model component 120 and future aircraft model component 122 can be considered individually, in series, and / or iteratively to model how the composition of the aircraft fleet changes and grows over time.

[0054] Fleet Renewal Model Component 120 can model the removal of older aircraft from the fleet and their replacement with their latest generation equivalents. Users can provide input to control how quickly older aircraft are replaced in the fleet and then view the impact on fuel combustion and emissions across the entire fleet. The model can also provide users with instructions on how many new aircraft need to be added to the fleet each year to renew the entire fleet by a target year.

[0055] Input variables for the fleet renewal model component 120 may include the phase-out duration of older aircraft in the fleet, which can be defined as the length of time between the end of production for an aircraft type and the last instance of that type leaving the fleet. Input variables may additionally or alternatively include ramp shape parameters, such as dimensionless numbers controlling how steep the ramp ascent / descent phases are. Input variables may additionally or alternatively include the cargo payload capacity for each aircraft type, the great circle distance on each route, the number of flights by aircraft type, route, and year, the seat capacity for each passenger aircraft type, the total cargo ton-kilometers on the routes, the start year of the model, the start year of the fleet renewal process (which may default to the same year as the start year of the model run), and the end of production for each existing aircraft type. Output variables may include the number of flights by aircraft type and route for a given year, and the phase-out curve for the previous generation of aircraft.

[0056] By defining a phase-out curve, older aircraft can be gradually phased out of the fleet. This curve describes the portion of flight that remains serviced by older aircraft types each year relative to the model's start year. The phase-out curve can be an S-curve derived from the curve of the cumulative beta distribution function. If an aircraft's production ended before the model's start year, the phase-out period for that aircraft type may be shortened, allowing the last aircraft to leave the fleet a given number of years after its production run ended.

[0057] The fleet adds new aircraft to serve traffic growth and replace older aircraft that have been phased out. It can be assumed that all new aircraft added to the fleet are latest-generation aircraft (defined as those actively produced in the year the model began, or those undergoing commercial certification in the near future). The specific aircraft type chosen to replace each previous-generation aircraft type can be selected to maintain size similarity and, where possible, the same original equipment manufacturer (OEM). For example, flights served by previous-generation aircraft may be replaced one-to-one with their latest-generation replacements. Flights of the new aircraft type may account for fleet renewals and service growth on routes. In the event of a temporary reduction in total air traffic (e.g., during a pandemic), the total number of flights can be adjusted. An ideal traffic growth curve can be applied during this period to allow for the continued delivery of new aircraft for future growth while existing aircraft are temporarily grounded. This curve can be an exponential curve whose CAGR is chosen to match the actual traffic growth curve in a future selected reference year.

[0058] Therefore, the total number of flights served by the latest generation of aircraft on routes in a given year is based on fleet renewal and fleet growth, while the number of flights still served by the previous generation of aircraft can be obtained by calculating the difference between total traffic volume and the number of flights served by new aircraft.

[0059] As an example, Figure 3Example implementation curve 300 for fleet renewal and growth is shown. As illustrated, the total number of flights increases based on the traffic growth curve from 2020 (model start date) to 2050 (model end date). The fleet includes previous generation aircraft 302 and the latest generation aircraft 304. As the model progresses, the latest generation aircraft are added based on fleet growth 306 and fleet replacement 308, while the previous generation aircraft are gradually phased out. In this example, the previous generation aircraft 302 initially constitutes the entire fleet (in 2020) and is gradually phased out on the replacement curve by 2035. The latest generation aircraft 304 begins to be added in 2020 and constitutes the entire fleet by 2035. Additional growth between 2035 and 2050 is entirely facilitated by the introduction of the latest generation aircraft 304.

[0060] The payload capacity (seats and / or cargo) of a new aircraft may differ from that of the aircraft it replaces. To maintain constant traffic volume on all routes before and after the application of fleet updates, the number of flights served by the new aircraft can be adjusted. For passenger aircraft, a seat correction factor can be applied to the number of flights by the new aircraft to keep ASK (Air Seat Size) on the routes constant.

[0061] A similar correction can be applied to freighter aircraft based on the cargo capacity of old and new aircraft on each route. However, an additional capacity factor can be included to account for changes in belly cargo capacity resulting from passenger aircraft replacement. Since passenger and freighter aircraft do not fly on exactly the same routes, this factor is calculated for each regional pair (e.g., North America to China).

[0062] The additional capacity factor can be calculated as the ratio of total additional / missing TCTK (the change in belly cargo from dedicated freighters in each region pair and the total TCTK). This term can be derived by applying cargo tonnage conservation before and after fleet updates and determining the increment (delta) of belly cargo tonnage carried after passenger aircraft updates. After these adjustments to capacity, the final number of flights of the latest generation aircraft added from fleet updates can be combined with any latest generation aircraft already serving the route, and the number of new flights served by previous generation aircraft can be determined accordingly.

[0063] Since the latest generation of aircraft added to the fleet are existing aircraft types, standard methods for calculating fuel combustion and emissions across the entire fleet can be used. Using coefficients specific to the new aircraft, fuel combustion can be easily recalculated for each route.

[0064] The future aircraft model component 122 can introduce future aircraft types that do not yet exist into the fleet. This can include both conventional fuel aircraft that incorporate new technologies to reduce fuel consumption and aircraft that are fueled by entirely new energy carriers such as hydrogen and electricity. Users can create a fleet of future aircraft, define which routes they will be placed on, the year of entry into service, and the final market share. The model then calculates the number of flights on these routes served by the future aircraft types, as well as the resulting changes in energy consumption and emissions.

[0065] Input variables for future aircraft models may include the percentage of traffic served by a future aircraft type on addressable routes (e.g., those meeting market and range constraints) in a given future year, the year each future aircraft type enters service, the fleet insertion time for a given aircraft type, and a dimensionless coefficient describing the shape of the fleet insertion curve. Input variables may additionally or alternatively include the reference aircraft's tank-to-wake energy consumption, the default difference in energy consumption per seat-kilometer relative to the average of best-in-class existing aircraft of the same size class, and a custom difference in energy consumption per seat-kilometer relative to the average of best-in-class existing aircraft of the same size class. Input variables may also include the average lifetime carbon intensity for each energy type, tank-to-wake energy consumption per route per year, the total number of flights per route per year, the total available capacity per route per year, the average passenger load factor, and the average weight of passengers and their baggage. For battery-electric aircraft, input variables may additionally or alternatively include the battery-electric aircraft's system charging efficiency. This may include losses directly from battery charging as well as those from power distribution to airports. Input variables for battery-electric aircraft may additionally or alternatively include the lifespan of the aircraft battery pack in charge / discharge cycles, and the energy required to produce replacement aircraft battery packs.

[0066] The output variables of the future aircraft model may include the energy consumption from the fuel tank to the wake aircraft per year per route, the life cycle emissions from the oil well to the wake per year per route, the number of flights performed per year per route, and the available capacity per year per route.

[0067] The allocation of future aircraft types on flight routes is determined by range and market segmentation constraints. To calculate this, a set of aircraft types that can operate on specific routes is derived based on their distance and existing aircraft size categories. This can be addressed by plotting the range and market segmentation constraints for each future aircraft type as regions on a 2D plane, such as... Figure 4 As shown. Figure 4An example model 400 for aircraft route allocation is schematically depicted. Routes are divided into routes A 402 (e.g., shorter regional flights), routes B 404 (e.g., shorter routes served by single-aisle aircraft), and routes C 406 (e.g., longer routes served by single-aisle aircraft). Future aircraft types 410 can then be added to the routes. The orientation of each route on model 400 determines the set of future aircraft to be added to the routes. For example, aircraft A 412 is only added to routes A 402, routes served by regional aircraft with sufficient range. For example, a new aircraft (such as a battery-electric aircraft) may have a limited range and is therefore limited to certain routes. While aircraft B 414 has the same range constraint as aircraft A 412, its target is the short routes currently served by single-aisle aircraft (route B 404). Aircraft C 416 has a greater range than aircraft A 412 or aircraft B414, and it is only assigned to route C406, which is currently served by single-aisle aircraft.

[0068] Route assignment can be iterative and can limit how many specific types of aircraft can be added to the fleet over time. In some examples, current aircraft can be assigned to other routes (e.g., battery-powered flights take over regional flights on route A 402, while conventional aircraft on these routes can be assigned to route B 404).

[0069] The number of flights each future aircraft type will serve on each route can be calculated using a fleet insertion curve, which describes how the share of flights served by each aircraft type on a route changes over time. S-curves can be used to construct these fleet insertion curves, the shape of which can be determined by the year of entry into service, the assumed market share in future years, and user-set curve shape parameters. The insertion duration can be calculated such that the traffic share curve value for a future date matches the user-set market share for that future date. For example, Figure 5 Chart 500 shows two example fleet insertion curves (502, 504). Fleet insertion curve 502 is defined by a 30% market share, a time of entry in 2030, and a curve shape of α=1.5. Fleet insertion curve 504 is defined by a 50% market share, a time of entry in 2035, and a curve shape of α=2.

[0070] On some routes, the sum of the market share of all future aircraft types in the coming year may be greater than 1. To correct for this, a market share normalization coefficient can be calculated for each set of future aircraft. This coefficient normalizes the insertion curve of each aircraft so that the sum of its market share in the coming year equals 1, thus deriving the corrected fleet insertion curve.

[0071] By multiplying the interpolation curve by the total number of flights eligible to serve each future aircraft type per year, the uncorrected number of flights replacing each existing aircraft type on each route can be obtained. To ensure the conservation of total passenger traffic, a capacity correction factor can be applied to the number of flights replacing each existing aircraft type on each route, based on the ratio of the capacity of old to new aircraft on the route. If the future aircraft type has a different belly cargo capacity than the existing aircraft type it replaces, the total tonnage carried can vary. This is because, unlike fleet renewal models, there is no dedicated cargo future aircraft type to divert cargo traffic to maintain the total tonnage conservation of the route network.

[0072] The number of flights served by existing aircraft on each route can be obtained by multiplying the total number of flights on the route by 1 and then subtracting the sum of the interpolation curves from all future and existing aircraft types on each route. In other words, the number of flights served by each aircraft in the fleet is simply the market share of those aircraft multiplied by the number of flights on routes eligible to be served by those aircraft. The resulting number of flights is a combination of future and existing aircraft, which can include both the latest generation and previous generation aircraft types.

[0073] Energy consumption for each future aircraft type along each flight path can be calculated based on the aircraft performance model of the reference aircraft model upon which it is based. These reference performance models can be stored as payload-range-energy tables, which give the energy consumption of each reference future aircraft as a function of payload and flight distance. The reference aircraft can be designed based on general requirements representing each aircraft category and energy carrier. Linear interpolation algorithms can be used to calculate energy consumption at any distance and payload based on discrete points in the table.

[0074] Consistent with the assumptions used in models for existing aircraft, a constant payload value can be used for all flight paths. This reduces the 2D linear interpolation function to a 1D linear interpolation function for flight distance only.

[0075] Each reference future aircraft model has a default energy efficiency increment relative to the best existing aircraft in its size category. If the user selects an energy efficiency increment different from the default value of the baseline model, a scalar multiplier can be applied to all energy values ​​in the fuel consumption table to match the aircraft's actual energy efficiency to the user's input.

[0076] Figure 6 Chart 600 illustrates a method for calculating the energy efficiency of existing, reference, and user-defined aircraft. Each line in Chart 600 represents the energy consumption (E) per seat, as a function of distance traveled, or how much energy the aircraft consumes per passenger per kilometer of travel. Existing aircraft are represented by line 602. The best-in-class average of existing aircraft is represented by line 604. A new or future aircraft is described in the context of a reference aircraft (represented by line 606). The improvement in energy consumption (Δe) of the new reference aircraft can then be determined. ref For example, the reference aircraft might be a new hydrogen-powered single-aisle aircraft. Existing single-aisle aircraft, represented by line 602, can be used to generate a best-in-class average based on the current fuel efficiency of existing aircraft in this category. The reference aircraft can have an estimated fuel efficiency that can be retrieved from a database. The user can then define the fuel efficiency characteristics (ƒΔe) of a user-defined aircraft based on the reference aircraft. When the user-defined aircraft is assigned to a flight path, the best-in-class average (Δe) can be used as a reference. user To determine emissions savings.

[0077] The fleet’s total energy use is the sum of contributions from old routes (using existing aircraft) and new routes (using future aircraft). The total carbon emissions for each type of future aircraft on each route may simply be the product of the total energy consumed in flight and the life-cycle carbon emissions of each energy carrier.

[0078] For novel energy carriers, these lifecycle carbon intensities can be extracted from the electricity and hydrogen models within the energy model. For aircraft fueled by conventional jet fuel, the carbon intensity of conventional jet fuel is used. The reduced carbon intensity of jet fuel due to the introduction of SAF can be applied later in the modeling pipeline.

[0079] Please note that the lifecycle emissions model for battery-electric flight includes two additional terms: one for energy lost during power distribution and battery charging, and another for energy consumed during the production of replacement batteries. It can be assumed that losses from other energy carriers are incorporated into the lifecycle carbon intensity of the energy carrier, therefore these additional terms may not be necessary.

[0080] Back Figure 1Operational efficiency model 124 can allow operational improvements to be applied to any or all parts of an existing air transport system, including the aircraft itself, aircraft operations at airports, air traffic management, flight routing, and passenger load factor.

[0081] For example, an improvement in passenger load factor can reduce the number of flights required to meet future travel demand. It can be assumed that the efficiency improvement for each part of the system is independent of the others, and then summed to determine the overall system-wide improvement as a function of time. For example, the efficiency improvement for each future year can be calculated, where the magnitude of the improvement increases annually (e.g., linearly).

[0082] Input variables may include aircraft energy consumption per year per route (e.g., tank-to-wake), lifecycle emissions per year per route (e.g., well-to-wake), number of flights per year per route, and available capacity per year per route (e.g., passengers, cargo). Input variables may additionally or alternatively include changes in energy consumption over the baseline energy consumption due to aircraft retrofitting and maintenance improvements, fleet and airport operations improvements, and / or air traffic management improvements over the next few years. Baseline passenger load factors and future passenger load factors may be applied before applying operational improvements.

[0083] Output variables may include aircraft energy consumption per route per year after the operational changes, lifecycle emissions per route per year after the operational changes, number of flights per route per year after the operational changes, and available capacity per route per year after the operational changes. Changes in energy consumption may be due to aircraft retrofitting and maintenance improvements, fleet and airport operational improvements, and / or improvements in air traffic management. Projected passenger load factors for the next few years may also be output.

[0084] Improvements resulting from changes in passenger load factor can be based on the ratio of the passenger load factor before model application to the projected passenger load factor. Energy and emissions for each route can be determined by applying the operating efficiency factor to the input aircraft energy consumption and lifecycle emissions for each route per year. The total energy and emissions for the entire fleet can then be determined by summing across all aircraft types and routes.

[0085] Changes in energy consumption resulting from aircraft retrofitting and maintenance improvements, fleet and airport operational improvements, and / or air traffic management improvements do not affect the number of flights or payload-distance metrics. However, if we assume that the available capacity in the air transport system remains constant annually with the application of various emission reduction strategies, changes in the passenger load factor may affect these metrics. Therefore, the passenger load factor can affect the number of flights and revenue passenger kilometers associated with passenger aircraft, but not the number of cargo flights.

[0086] Energy strategy model 126 includes components associated with the production of different forms of energy, such as electricity model component 128, hydrogen model component 130, and sustainable aviation fuel (SAF) model component 132. These individual model components can be used to calculate the carbon intensity over time for each type of energy based on various technological and market assumptions for each part of the energy system. The resulting carbon intensity for each fuel can then be combined with the energy consumption of each aircraft type from previous models (e.g., future aircraft) to determine the resulting life-cycle emissions associated with the operation of these aircraft. International flights may account for energy production in the destination country. For example, different countries may have different subsidies for different renewable energy sources, different energy production resources, etc. The energy model can be fed back to the updated aircraft model based on the interconnectivity between the aircraft and the energy system.

[0087] The power model component 128 can consist of multiple energy generation sources, representing the most prominent power source. For example, energy generation sources may include wind power, hydropower, photovoltaic solar power, bioenergy, coal, natural gas, oil, nuclear power, and other renewable energy sources (such as tidal power, geothermal power, concentrated solar power, and novel technologies). In any given year, the composition of energy generation sources must add up to 1. The power composition and average carbon intensity of each production method determine the total emissions associated with charging an all-electric aircraft, producing hydrogen via electrolysis, and powering direct air capture.

[0088] The input variables for the power model component 128 may include the user input share of electricity produced by each power generation type in the year the model ends, the average carbon intensity of each power generation method, the shape parameter of the market absorption transition rate of the power generation method, and the measured share of electricity produced by each power generation type in the year the model begins.

[0089] In all energy models, the market share of each component of the energy system (in this case, power generation) up to the end of the model year is modeled using a half-S-curve. This shape is chosen because the market adoption rates of various alternative energy technologies are not expected to plateau before the end of the model year. Assuming all electricity types have a non-zero global market share at the start of the model, it can be assumed that the market entry year and the simulation start year are the same. However, newer forms of electricity may have later simulation start years, which will adjust the half-S-curve. A single average value can be used for the carbon intensity of each power generation method. This value can be assumed to be constant over time. The aggregate carbon intensity of power generation can be calculated using a weighted average of the carbon intensity of each power source.

[0090] The hydrogen model component 130 can calculate carbon intensity and other metrics associated with hydrogen production from various sources. The model can calculate emissions from both liquid hydrogen used directly in aircraft and gaseous hydrogen used as feedstock for SAF production. Hydrogen production options include electrolysis (such as green hydrogen) and steam methane reforming (SMR) from natural gas (such as grey hydrogen).

[0091] Input variables for the hydrogen model component 130 may include the hydrogen electrolysis market share and dimensionless hydrogen electrolysis market entry shape parameters in the model end year, the hydrogen electrolysis insertion year, and the hydrogen liquefaction per unit energy. Liquefaction of hydrogen per unit energy, electrolysis of hydrogen per unit energy, and SMR of hydrogen per unit energy may include electricity requirements. Input variables may additionally or alternatively include hydrogen liquefaction evaporation recovery fraction, hydrogen vapor loss fraction, constant carbon intensity of electrolysis, and constant carbon density of SMR. Input variables may additionally or alternatively include the equivalent emission potential of hydrogen emitted directly into the atmosphere, electrical carbon intensity, and certain liquefaction parameters. Input variables may additionally or alternatively include overall production emissions of gaseous hydrogen by production method, overall production emissions per unit of liquid hydrogen produced by production method, and overall production emissions per unit of liquid hydrogen transported by production method. Input variables may additionally or alternatively include emissions from a unit of hydrogen liquefaction, the ratio of hydrogen transported to hydrogen produced (including losses), and the ratio of liquefied hydrogen to hydrogen produced (including hydrogen reliquefied in the evaporation recovery unit).

[0092] The output variables of the hydrogen model component may include the average carbon intensity of liquid hydrogen production from well to aircraft refueling (well to fuel tank), the average carbon intensity of gaseous hydrogen production for SAF production, and the annual share of hydrogen produced by different production pathways.

[0093] Figure 7A schematic diagram 700 illustrating hydrogen production and distribution is shown. The hydrogen model component 130 may include two hydrogen production pathways 702 (steam methane reforming (SMR) and electrolysis driven by power generation 704). In the model's initial year, it can be assumed that all hydrogen is produced via SMR. Introducing electrolysis into the hydrogen production market can be modeled using a half-S curve. The shape of this curve can be based on user-inputted electrolysis market share in the model's final year, the insertion year, and market entry shape parameters.

[0094] The CO2 equivalent emissions associated with hydrogen production and distribution can be divided into three components: 1) lifecycle emissions from gaseous hydrogen production 706 for SAF production (via SMR or electrolysis); 2) lifecycle emissions from hydrogen liquefaction 708; and 3) equivalent emissions from hydrogen vapor loss to the atmosphere 710.

[0095] Emissions associated with hydrogen production from electrolysis comprise two items: direct emissions related to the electricity required to power the electrolyzer, and indirect items (which account for emissions due to stack degradation and replacement, as well as hydrogen distribution). The carbon intensity of electricity generation can be obtained directly from the power model component (representing the use of electricity from the grid for hydrogen production) or can be set to a custom value (representing the use of dedicated or “off-grid” power supplies). For SMR, emissions associated with production take a similar form, but both the energy intensity associated with SMR and the carbon intensity of SMR production may have constant assumed values.

[0096] The carbon intensity of liquefaction 708, which produces liquid H2 for spacecraft 712, can be expressed as the product of the electrical energy intensity of liquefaction and the carbon intensity of the power source. An additional multiplier can be added for the number of times hydrogen must be liquefied in its supply chain (e.g., whether hydrogen needs to be reliquefied after long-distance transport before being loaded onto the spacecraft). This value can be the same for SMR or electrolysis.

[0097] In the production of liquid hydrogen, hydrogen is potentially lost to some extent via evaporation / boiloff throughout the production and distribution process. Some of this hydrogen can potentially be recaptured (at the expense of additional liquefaction energy 708) by including an evaporation recovery system 714. To account for both hydrogen loss and recovery, the hydrogen model components may include partial loss (release / emission to the atmosphere 716) and partial evaporation recovery 714. This results in a difference between the volume of hydrogen produced and the amount of hydrogen transported. Similarly, the additional amount of hydrogen captured and reliquefied using an evaporation recovery system can be considered.

[0098] Since hydrogen itself is a greenhouse gas, warming emissions associated with hydrogen leaked directly into the atmosphere can be included. These emissions are likely a function of the effective warming potential per unit of hydrogen directly released into the atmosphere and the mass of non-recoverable hydrogen, which is related to the overall mass of hydrogen produced. A significant portion of losses in hydrogen production systems can be attributed to the challenges of handling hydrogen at cryogenic temperatures. It can be assumed that hydrogen leaks in gaseous systems can be managed with sufficient passive controls (e.g., leak detection, sealing) and may not be included in the model components.

[0099] The general form of carbon intensity for liquid hydrogen production methods is the sum of emissions contributions from gaseous hydrogen production, hydrogen liquefaction, and direct losses of hydrogen to the atmosphere. To convert this to carbon intensity per unit of hydrogen transported, an item must be added to account for energy use and emissions associated with the production and distribution of any non-recoverable hydrogen. This results in the average carbon intensity of hydrogen per unit transported. The general form of lifecycle carbon intensity for gaseous hydrogen production methods does not include items concerning liquefaction energy, direct emissions from hydrogen losses, and evaporation recovery; it may include items concerning hydrogen production. Once the model has been run over all hydrogen production methods, the combined carbon intensity of liquid and gaseous hydrogen production can be calculated as a weighted average of the annual hydrogen emission intensity from each production method based on its market share.

[0100] Back Figure 1 When SAF is introduced to the market, Sustainable Aviation Fuel Model Component 132 can calculate the life-cycle carbon intensity of jet fuel. The model component can consider SAF derived from different categories of biological feedstocks (such as fats, oils and greases; sugars and starches; new energy crops; waste and residues), as well as SAF produced using electricity (referred to as electrofuel or electro-liquid (PtL) fuel).

[0101] Input variables for SAF model components may include the share of jet fuel produced from a given feedstock source in the model's end year, the insertion year of the fuel type from the given feedstock source, and the dimensionless market entry shape parameter of the fuel type from the given feedstock source. Input variables may additionally or alternatively include lifecycle carbon intensity for SAF produced from a given biofeedstock type, lifecycle carbon intensity for power generation, and lifecycle carbon intensity for gaseous hydrogen production. Input variables may additionally or alternatively include the energy intensity of PtL production (including direct air capture (DAC) for CO2 extraction and production, the proportion of PtL SAFs available as jet fuel, and the hydrogen demand for PtL production with or without product recycling. Input variables may additionally or alternatively include the lifecycle emissions of a production unit of PtL SAF (excluding emissions attributable to other proportions of outputs used for thermal production). The electricity and thermal demands of PtL production can be entered with or without product recycling. The electricity, thermal, and hydrogen demands of PtL production may be entered at specific output ratios. Additional input variables include the ratio of PtL mass flow, which includes light hydrocarbons used for thermal production and for PtL-only products in the product output.

[0102] The output variables of SAF model component 132 may include the average carbon intensity of jet fuel (including SAF) per year, and the share of jet fuel produced per year by different production pathways.

[0103] The composition of all jet fuel sources (including SAF jet fuel) must add up to 100%. The share of conventional jet fuel from fossil sources in the model's initial year can be set to 100%. For each year of the model component, the remaining share of jet fuel after the introduction of SAF can be assumed to be conventional jet fuel. The market insertion curve for each type of SAF is similar to those described for hydrogen and can be controlled by parameters such as market share in the final model year, the initial insertion year, and the transition rate.

[0104] The aggregate carbon intensity of jet fuels including SAFs is likely a weighted average of the lifecycle carbon intensity of jet fuels from all feedstock sources (weighted by their annual usage share). The expression of lifecycle CO2 intensity for SAF types differs for fuels produced from biomass and fuels produced from electricity. Each category of bio-based SAFs may have a different lifecycle carbon intensity, which includes emissions from feedstock cultivation, collection and transportation, as well as fuel production and final distribution to the point of use. Within feedstock categories, this value can vary considerably depending on the specific feedstock used and the production pathway. For simplicity, a single average for each category can be used. This can be assumed to be a constant value per year.

[0105] A constant value can also be used for the life-cycle carbon intensity of conventional jet fuels. The reduction in carbon intensity of fossil jet fuels due to improvements in petroleum extraction and refining processes (to create low-carbon aviation fuels or LCAFs) can be included in this model.

[0106] Unlike SAFs derived from waste and biomass, the carbon intensity of PtL fuel is not constant. Instead, it is a function of the carbon intensity of the electricity and hydrogen used to produce the fuel. Inputs to this part of the model include the total energy demand for PtL production (including the electricity used for carbon extraction using DAC) and a fraction of the product slate as the useful chain length for producing jet fuel.

[0107] The quadratic curve generated from the output of the Fischer-Tropsch (FT) synthesis model can be used to calculate the electricity and heat requirements for producing one unit of PtL SAF from the input total energy demand. This value can be calculated in two cases: one where the reactants pass through the reactor only once (no recovery), and another where light reactants are recovered through the reactor to maximize jet fuel production (full recovery).

[0108] The electricity and heat requirements for PtL production can be input with or without product recovery. These energy intensities can be used to calculate the actual electricity and heat requirements for the selected product output of PtL fuel. The hydrogen requirement for PtL is independent of the energy requirement. However, it may be based on the product output ratio due to variations in the amount of unreacted hydrogen in the product stream.

[0109] The carbon intensity of PtL fuel can be described as the sum of the carbon intensity of electricity consumption and hydrogen consumption. The carbon intensity of power generation can be input from the grid model or provided as a constant (representing the dedicated power supply for PtL production). The carbon intensity of gaseous hydrogen can be obtained from the hydrogen production model components. Thermal energy can be assumed to originate from the combustion of light hydrocarbons in the product output, and therefore its production emissions can be attributed to PtL fuel emissions.

[0110] SAFs produced from various feedstocks can be grouped into four described feedstock categories. Fats, oils, and fats may include tallow, used edible oils, palm fatty acid distillates, corn oil, soybean oil, rapeseed oil, linseed oil, palm oil, jatropha oil, etc. Sugars and starches may include sugarcane, corn kernels, molasses, etc. New energy crops may include Miscanthus, switchgrass, poplar, willow, eucalyptus, etc. Waste and residues may include corn stalks, forestry residues, exhaust gases, wheat straw, municipal solid waste, and agricultural residues. Appropriate conversion processes can be used for each feedstock, such as Fischer-Tropsch synthesis, hydrogenated ester and fatty acid pathways, and alcohol-to-gas conversion. Some feedstocks can be processed using multiple pathways.

[0111] For each feedstock category, a default carbon intensity can be set to the median value for that category, while upper and lower limits can be set by the SAF with the highest and lowest lifetime carbon intensity. Optionally, only waste feedstocks can be included. This can include waste fats, oils, and greases, such as tallow, used edible oils, and palm fatty acid distillates.

[0112] The PtL production model can take into account the amount of syngas (e.g., a mixture of hydrogen and carbon monoxide) reactants required to produce one unit of hydrocarbon fuel. Since carbon dioxide is assumed to be the primary feedstock in this production process, the PtL production model may also include a reverse water-gas shift (RWGS) reactor for producing carbon monoxide from carbon dioxide for use in syngas production.

[0113] The hydrogen and carbon requirements for PtL production can be set by running this PtL production model in two configurations: with and without product recovery (achieved by recursively cycling the output back into the reactor until it converges to a higher percentage of product output for the desired chain length). The energy requirements for PtL production can be bounded by assumptions about the energy intensity of the direct air capture (DAC) and the conditions within the FT reactor. The heat and electricity requirements in the reactor are calculated based on reactor conditions, including the specific heat coefficient of the reactor mixture and the density within the reactor (e.g., air density). The total electricity requirement can be the sum of the electricity used for the DAC and the electricity used for the FT reactor. The heat requirement for the DAC can be assumed to come from waste heat produced by the FT reaction (which is exothermic). The differences in the values ​​of these terms with and without product recovery are a result of the different syngas requirements.

[0114] Once future aircraft types using new energy carriers are added to the fleet, the consumption of jet fuel (including SAF), hydrogen, and electricity for the entire fleet can be calculated. However, the total production of hydrogen and SAF differs because these energy carriers can also be used as feedstocks for producing other fuels in addition to direct use. Based on the total production of different energy carriers, it is possible to calculate indicators of the resources required to produce sufficient energy for aviation (including primary energy requirements, biomass feedstock requirements, and land use for energy crops and infrastructure).

[0115] Input variables for the energy demand model components may include the electrical energy consumption of the entire fleet of battery-electric aircraft, the hydrogen energy consumption of the entire fleet of hydrogen-powered aircraft, and the jet fuel energy consumption of the entire fleet of conventional fuel-powered aircraft. Input variables may also include the electricity share generated by each power generation method, the hydrogen share produced by each hydrogen production method, and the jet fuel share produced from each feedstock category. Input variables may additionally or alternatively include the uncorrected hydrogen requirement for producing one unit of PtL, the uncorrected electricity requirement for producing one unit of PtL, the heat requirement for producing one unit of PtL, the electricity requirement for producing one unit of gaseous hydrogen through a specific production process, and the electricity requirement for liquefying one unit of hydrogen. Input variables may additionally or alternatively include a portion of the hydrogen lost to atmospheric release during the production process, a portion of the hydrogen captured by the evaporation recovery system, and multiple liquefactions during the hydrogen production process. Input variables may additionally or alternatively include the conversion efficiency of feedstock to SAF by energy content, the proportion of product output from the SAF production process that can be used as jet fuel, the average energy intensity of biomass in each feedstock category, and the land required to generate one unit of electricity or SAF energy (e.g., from infrastructure or crops). Input variables may additionally or alternatively include the total amount of liquid hydrogen produced for aviation, the total amount of gaseous hydrogen produced for aviation, the total electrical energy used directly in battery-powered aircraft, the total electrical energy used in hydrogen production for aviation, and the total electrical energy used in PtL SAF production for aviation. Input variables may additionally or alternatively include the ratio of hydrogen produced to hydrogen transported (considering losses from atmospheric release) and the ratio of hydrocarbons produced to hydrocarbons transported by the FT reactor (considering additional light hydrocarbons used for heat production). Input variables may additionally or alternatively include specific hydrogen requirements for PtL production (considering additional hydrogen for the production of light hydrocarbons from thermal energy) and specific electricity requirements for PtL production (considering additional electricity for the production of light hydrocarbons from thermal energy).

[0116] The output variables of the energy demand model component may include total electrical energy produced for aviation by power generation method, total hydrogen energy produced for aviation by power generation method, total SAF produced by feedstock category, total primary energy for aviation from fossil sources, renewable electricity and biomass, total amount of a certain type of biomass feedstock used to meet SAF demand, and total land use used for energy production.

[0117] The SAF demand from each feedstock category can be expressed as the product of total jet fuel demand and the share of fuel produced from that feedstock. The total demand for all SAF types can be expressed as the difference between fossil jet fuel demand and total jet fuel demand. Total hydrogen production can be expressed as the sum of liquid hydrogen demand directly used in hydrogen aircraft and gaseous hydrogen demand used as feedstock for PtL SAF production. Total liquid hydrogen production can be set equal to the consumption of the entire hydrogen fuel fleet, with an additional term representing the amount of hydrogen lost through atmospheric release during production. The amount of hydrogen lost to release can be expressed as a function of the loss fraction and the evaporation recovery factor. Total gaseous hydrogen production can be expressed as the product of PtL SAF production and the specific hydrogen demand for PtL. Total PtL SAF production can be expressed as the product of total jet fuel demand and PtL market share. The specific hydrogen demand for PtL production can be derived from the Fischer-Tropsch synthesis model. Note that a correction term must be included for the additional hydrogen required to produce light hydrocarbons used to supply heat for the reaction. The correction factor can be based on the heat energy demand for PtL production. Total hydrogen production can be divided by production method based on market share of hydrogen production processes.

[0118] Total electricity generation can be expressed as the sum of the electricity required directly for battery-electric aircraft, hydrogen production (for electrolysis, SMR, and liquefaction), and PtL SAF production (for DAC and FT synthesis). The total electricity generation directly consumed in battery-electric aircraft can be equal to the electricity demand of such aircraft because no loss term is modeled for transmission to the point of use. The electricity required for hydrogen production may depend on both the state of matter of the hydrogen at the time of use and the production method. The electricity required for the production of gaseous hydrogen used as feedstock for SAF can be based on the market share and energy intensity of each hydrogen production process. The total electricity demand for liquid hydrogen production may include additional terms (e.g., electricity required for liquefaction) and a coefficient that considers the additional electricity consumption for hydrogen lost to atmospheric release during production. The electricity produced by PtL SAF can be derived from the FT synthesis model. This may be similar to the hydrogen demand for PtL SAF, thus including a term for the electricity required for the production of light hydrocarbons for heating.

[0119] The total energy required for aviation can also be represented based on its primary source (fossil energy, biomass, or renewable electricity). When associated with hydrogen and electricity models, the primary energy associated with PtL production can inherit the primary energy properties of these systems. For simplicity, this model component can assume that all energy content in biomass-derived SAFs comes from the feedstock itself and exclude additional energy inputs from fuel production processes.

[0120] From total energy production, it is possible to calculate the resources required to produce enough energy to power aviation. For a given SAF feedstock category, the conversion from SAF energy to the quantity of biomass can be based on the feedstock's energy density, the conversion efficiency from biomass to distillate, and the proportion of products produced that can be used as jet fuel. Land requirements may also exist for energy generation, which may include land for renewable power generation infrastructure and land for growing crops to produce biomass-derived SAF. Land for production facilities and secondary land uses (including land needed as materials for infrastructure construction) can be excluded. Therefore, the total land requirements for aviation energy generation may be the sum of the land requirements for electricity and SAF production, respectively.

[0121] Back Figure 1 Market-based measures model 134 can be used to further reduce overall emissions. Carbon offsetting and removal can be used to reduce or remove greenhouse gases from sectors outside the aviation industry to offset residual emissions from the aviation industry after all other emission reduction strategies have been applied. Multiple carbon offsetting and removal models can be applied independently of each other. CORSIA models the impact of currently enacted policies. Custom Global Initiative (CGI) models the potential impact of future coordinated carbon removal initiatives in international aviation. Voluntary measures represent additional carbon dioxide removals or offsets purchased through voluntary carbon markets.

[0122] The CORSIA model implements the effects of policies established by ICAO. Users may be presented with the option to select which flights to include in the calculation. CORSIA can be applied to direct emissions from the combustion of jet fuel (e.g., emissions from the fuel tank to the wake). This amount may exclude emissions reductions due to SAFs, but may include emissions from all jet fuel consumption (regardless of their source). CORSIA-bound emissions can then be calculated by summing all international flights between participating CORSIA countries in a given year (first by operator, then by sector as a whole). CORSIA may apply only to international flights between member states participating in ICAO. The dataset excludes flights between small or developing countries and emissions from small operators with annual CO2 emissions below 10,000 tonnes. Each operator's offsetting obligation can then be reduced by its emissions reductions due to SAFs, as all SAFs in the model are assumed to be CORSIA-eligible fuels. The emissions reductions that an operator can claim for CORSIA-eligible fuels are proportional to the lifecycle emissions of CORSIA-eligible fuels relative to a reference value for conventional jet fuels. This can be used to determine the net emissions of each operator over time, and these values ​​can be summed to derive the emissions reductions for the entire fleet (due to CORSIA). Input variables include the emissions index for jet fuel, annual emissions by operator and year, and a list of routes subject to CORSIA by year. The model's output variable is the annual emissions reduction due to CORSIA by operator and year.

[0123] The CGI model allows for modeling collective actions in international aviation to catalyze carbon removal initiatives. Applicable timeframes can be set for the future. The model's structure is similar to the CORSIA model, with added variables such as the ratio of operator to individual growth coefficients, and emission levels (which must be maintained below annual emission levels).

[0124] Market-based measures can be calculated as a percentage of the remaining emissions from the entire fleet. Input variables may include the remaining emissions from the aviation industry each year, the starting year of the carbon removal or offset purchase, the share of emissions to be offset in the starting year, and the annual growth rate of the voluntary carbon removal purchase. The emission offset share for a given year can be calculated. Net emissions from the aviation industry can then be determined by applying a greenhouse gas reduction array prior to the voluntary measures. The total emissions reduction due to the voluntary measures can then be determined by summing across all routes and aircraft types. This yields the output variable of annual net emissions from the aviation industry after considering carbon offsets and removals.

[0125] Back Figure 1Non-CO2 emission model 136 can also be added to modeling pipeline 100. One or more non-CO2 emission models can be applied to predicted aviation emission information to generate non-CO2 emission reduction potential over future time periods. Non-CO2 emission model 136 can be applied aggregately (rather than on each flight route) to predicted aviation emission information. The non-CO2 emission reduction potential can then be used to indicate total climate impacts. Potential climate impacts for different scenarios can be assessed based on standard climate indicators and parameters such as radiative forcing, temperature response, and CO2 equivalent emissions. Impulse response functions (IRFs) can be employed to represent the lifetime and decay of aviation-induced forcing. This could be in the form of a simple climate model or a more complex global circulation model. Global emission estimates can be generated for in-flight emissions of carbon dioxide (CO2), water vapor (H2O), nitrogen oxides (NOx), sulfur oxides (SOx), and soot / organic carbon (BC / OC). Historical emission data can be used to determine cumulative aviation-induced atmospheric emissions. For example, CO2 and NOx emissions have long half-lives in the atmosphere, so data from several years prior to the year the model began can be considered. The overall climate impact model can use emission index adjustment factors to explain the differences in flight emissions between alternative propulsion methods and conventional kerosene-based jet fuels.

[0126] Known emission index values ​​can be used to generate annual emissions from conventional jet fuel. Global annual average emission index values ​​can be used, or flight phases can be separated and filtered by engine type. For example, NOx emissions are a product of complete combustion and are therefore inherently dependent on various factors such as thrust setting, combustor temperature, and engine efficiency. Therefore, NOx emissions vary not only across the entire flight envelope but also between different aircraft types. Future aircraft and fleet update model components can be used to reflect NOx emission reductions based on aircraft type.

[0127] Annual tank-to-wake CO2 and non-CO2 emissions can be calculated by multiplying the total annual fuel consumption of each energy carrier by its respective emission index. The emission index for electric aircraft can be set to 0, and the CO2, SOx, and BC emission indices for hydrogen-fueled aircraft can also be set to 0. However, the presence of engine oil in hydrogen-fueled engines may contribute to some soot emissions. Hydrogen combustion produces thermal NOx, although its emission index is lower than that of conventional jet fuel combustion. Therefore, parameters input by the user for sustainability strategies (such as aircraft strategies and energy strategies) can also be applied to the non-CO2 emission model.

[0128] Several assumptions can be made regarding the differences in fuel characteristics between SAF and conventional kerosene-based jet fuel and their impact on emissions. Due to the difference in energy density between SAF and kerosene-based fuels, the emission index can be adjusted by 0.98. Because of its predominantly paraffinic nature, SAF typically contains a higher hydrogen content per unit weight. Therefore, the water vapor emission index (eiH2O) can be adjusted by 9%. Since sulfur is not expected to be present in SAF, the sulfur emission index (eiSOx) can be set to zero. Finally, since there are no aromatics in the fuel, soot emissions are expected to be reduced due to the use of SAF. Due to stringent aircraft engine regulations (which establish an aromatic fuel volume content of at least 8% for engine seal expansion), SAF will need to be blended with aromatics to meet this requirement. This leads to some uncertainty regarding the possible soot emission index (eiBC) for SAF. To mitigate this uncertainty, users can select a soot particle number reduction between 5% and 48% to examine the potential climate impact of soot emission reductions within this range (primarily due to radiative forcing from the direct radiative effects of soot aerosols).

[0129] One or more types of radiative forcing values ​​can be determined (such as the effects caused by carbon dioxide (CO2), nitrogen oxides (NOx) (short ozone (O3), methane reduction (CH4), long ozone (O3), stratospheric water vapor (SWV)), water vapor (H2O), sulfate aerosols (SO4). 2- ), nitrate aerosols (NO3-), soot aerosols (BC / OC), and linear condensates and condensate cirrus clouds (CC).

[0130] The non-CO2 emission model 136 can use an impulse response function (IRF) convolution integral, which is a first-order approximation of the rate at which anthropogenic CO2 is retained and removed from the atmosphere via the carbon cycle. The response of the CO2 mixing ratio C(t) to the CO2 emission ratio E(t) can be modeled using coefficients from a carbon cycle ensemble study. The non-CO2 emission model 136 also allows users to vary the carbon cycle coefficients. Different carbon cycle models can be selected to allow for different degradation rates of CO2 in the atmosphere. By determining the annual increase (ΔC) in background CO2 due to cumulative aviation emissions, the stratospheric adjusted radiative forcing (SARF) due to aviation CO2 can be determined for each year of interest. To calculate the effective radiative forcing (ERF) due to aviation CO2 emissions, the variable C can first be updated to the background CO2 concentration for the year of interest. The radiative forcing due to the non-aviation sector can be determined first by subtracting the aviation component (ΔC) from the total atmospheric CO2 concentration (C) for each year. The radiation forcing due to aviation activities can then be calculated by subtracting the radiation forcing from the non-aviation sector from the total radiation forcing due to all CO2 previously calculated.

[0131] The temporal evolution of the radiative effect of a unit NOx emission pulse in a given year can be represented as a single exponential decay. Similar to the following calculation method: the radiative forcing due to background CO2 from cumulative aviation emissions is calculated by summing emissions from previous years and determining the atmospheric retention portion. The annual radiative forcing due to NOx emissions can be calculated as the sum of the radiative forcing of emissions in the current year of interest (t=0 to t=1) and the radiative forcing due to ozone and methane effects from residual aircraft NOx in previous years (t>1). This can be achieved using a loop that calculates how many years have passed since the starting year of interest and multiplies the emissions of each previous year by the point in the time decay model corresponding to the number of years in the past.

[0132] In fact, NOx effects (such as ozone generation) have a non-linear dependence on local NOx concentration levels; therefore, ozone generation may be reduced in areas with high NOx concentrations. The abundance of hydroxyl radicals (-OH) varies with NOx emissions and affects the atmosphere's oxidation capacity.

[0133] For short-lived forcings, where the perturbation's lifetime is assumed to be less than a year, annual global emissions can be modeled as pulses, assuming a uniform distribution of effects throughout the year. To determine these radiative forcing factors, the best estimates of annual radiative forcing can be gleaned from numerous studies combining global circulation models (GCM), simple climate models (SCM), and radiative transfer models (RTM), typically normalized by the quality of the emitting species responsible for the yearly emissions of the forcing type. The median-normalized radiative forcing value can then be calculated for each short-lived forcing type. A power factor can also be applied to the radiative forcing value to account for the different radiative feedback rates that link perturbations of different forcing agents to subsequent changes in global mean surface temperature. The power factor can be quantified as the ratio of the climate sensitivity parameter of the forcing agent to the climate sensitivity parameter of CO2. The climate sensitivity parameter can be viewed as the equilibrium change in annual mean global surface temperature resulting from doubling the atmospheric equivalent CO2 concentration.

[0134] The annual global effective radiative forcing due to short-lived forcings (such as black carbon, soot, sulfate, nitrate, water vapor, condensate, and condensate cirrus) between the start and end years of the model can be determined by multiplying a normalization factor by the projected emissions of that species, or, in the case of condensate cirrus, by the total projected flight path distance (km) for the current year of interest.

[0135] Forcing due to nitrate aerosols can be modeled by relating NOx, SOx, and soot emissions. Both nitrate and sulfate aerosol radiative forcing can be calculated based on components attributed to NOx and sulfur oxide (VI) emissions. Sulfur oxide (VI) emissions can be estimated based on sulfur dioxide emissions. Assuming a water vapor efficiency of 1, radiative disturbances due to aviation water vapor emissions can be modeled using a pre-calculated water vapor radiative forcing factor. Radiative forcing due to linear contrails and contrails can be calculated using the best estimate normalized to total annual flight distance.

[0136] Global mean surface temperature (GMST) is considered a key indicator of anthropogenically driven climate change. The non-CO2 emission model 136 can estimate the GMST response based on aerospace forcing of CO2, NOx, H2O, aerosols, and condensates. This provides users with insights into potential near-term surface temperature changes due to aerospace activities and allows for the assessment and comparison of the impacts of various strategies on near-term temperature changes relative to baseline scenarios.

[0137] Based on the effective heat capacity per unit area of ​​ocean, total radiative forcing, climate sensitivity parameters indicating surface level temperature change per unit of radiative forcing, and global mean surface temperature anomalies, a linear response model simulator can be used to estimate the temperature response to aircraft emissions and their associated climate forcing. Temperature changes due to annual emissions impacts can be based on radiative forcing over time, representing the integration of emissions impacts within a year.

[0138] Climate indicators provide a useful framework for comparing long-lived and short-lived forcing. The most commonly used indicator is the Global Warming Potential (GWP); however, many other indicators can be used, such as the Global Temperature Potential (GTP) and the Average Temperature Response (ATR). Climate indicators integrate radiative forcing or temperature response (typically due to a 1 kg emission pulse) over a specified time horizon (H). Time horizons are typically 20, 50, 100, or 500 years. Climate indicators can also be used to investigate the impacts of “persistent” emissions over a given period.

[0139] The disclosed non-CO2 emissions model 136 can incorporate GWP indicators as a tool, enabling users to observe the impact of time horizons on CO2 equivalent emissions assessments, particularly understanding how the choice of time horizon within a policy framework can influence future technology decisions. Any policy framework may need to consider the impact of different technology decisions on “short” and “longer” time horizons, and may additionally require consideration of trade-offs between different technology options, particularly the frequently encountered trade-offs with non-CO2 emission reduction proposals that could lead to increased CO2 emissions.

[0140] The absolute global warming potential (AGWP) for each type of forcing can be determined by integrating the radiative forcing response over each considered time span (e.g., H+20, 50, 100 years). This process takes a definite integral representing the radiative forcing response, with limits of 0 and H. For forcing modeled as a pulse, the AGWP is equivalent to the radiative forcing effect in a year starting at t=0. This is because it is assumed that the full effect of the pulse will be in effect within one year, with no long-term effects. Therefore, the AGWP of the pulse is equivalent to the radiative forcing effect over one year. For non-CO2 forcing types modeled by the impulse response function (IRF), such as effects caused by NOx, the AGWP can be obtained by integrating the time response of the radiative forcing, which is limited by the time span. For example, the AGWP20 for the methane effect caused by NOx is calculated by multiplying the 20-year radiative forcing effect by the NOx emission pulse. The GWP for each forcing type (x) is calculated by dividing AGWPx(H) by the corresponding AGWP for CO2 with the same time span (H). The equivalent CO2 emissions from the start year of the model to the end year can be obtained by multiplying each global warming potential (GWPX(H)) by the total emissions of the species (each forcing type is normalized).

[0141] Back Figure 1 Output indicator 138 can compare projected emissions information for a future time period (e.g., modeled before the application of sustainability strategies) with adjusted projected emissions information (e.g., modeled after one or more sustainability strategies have been applied to the projected emissions information). Based on the adjusted projected emissions information, emission reduction potential can be indicated for the future time period. Emission reduction potential can include reductions in fuel combustion, energy consumption, CO2 emissions, and various non-CO2 emissions. Emission reduction potential can include well-to-tank emissions, tank-to-wake emissions, well-to-wake emissions, etc. The output indicator may also include land use.

[0142] The output metrics indicate the emissions reduction potential over a future time period, from the year the model begins (or calculated based on a baseline of the flight traffic dataset) to the year the model ends. In some examples, the output metrics may indicate the emissions reduction potential for each year of the future time period (e.g., for each year between the year the model begins and the year the model ends). The output metrics may indicate the emissions reduction potential for each sustainability strategy model and model component, as well as the total emissions reduction potential.

[0143] Output metrics may also include energy consumption per route, greenhouse gas emissions per route, payload capacity per aircraft type, average payload per aircraft type, and a series of variables expressed as a function of each aircraft type, route, and year, including gross ton-kilometers, revenue ton-kilometers, revenue passenger capacity, revenue cargo capacity, available capacity, available passenger capacity, and available cargo capacity. These output metrics may be determined overall or annually over a future time period. Additional output metrics may include emissions reduction potential per route, per flight, and per fleet. Output metrics may additionally or alternatively include non-CO2 emissions reduction potential and aggregated climate impact metrics.

[0144] The output metric 138 can be displayed on a display device as a visualization 140. Figure 8 Example visualization chart 800 is shown. Visualization chart 800 indicates annual CO2 emissions (in megatonnes (MT) CO2-EQ) from 2019 (the model's start year) to 2050 (the model's end year). Traffic growth 802 (dashed line) indicates projected emissions information for the future time period. Annual projected emissions information is shown as emission reduction potential, with net CO2 emissions 804 shown in black. In this example, net CO2 emissions 804 reach 0 in 2050. Emission reduction potential is shown annually based on fleet renewal 806, future aircraft 808 (introduced starting in 2035), operational efficiency 810, renewable energy 812, and market offsetting 814. In this way, users can observe the amount of emission reductions attributable to each sustainability strategy.

[0145] Figure 9 A flowchart of an example method 900 for modeling sustainability strategies in the aviation industry is shown. Method 900 can be considered as being used in a computing device including one or more processors (e.g., as described herein and related to...). Figure 10 An example method for implementing a modeling pipeline 100 on the aforementioned computing device.

[0146] In 910, method 900 includes receiving flight traffic information (e.g., flight traffic dataset 102) for multiple flights within a selected time period at one or more processors. For example, the flight traffic information may include multiple historical flight data, which includes flight metadata such as origin / destination pairs, operators, aircraft type, energy usage, fuel combustion, emissions, passenger load factor, cargo load factor, weather conditions, etc.

[0147] Optionally, at 920, method 900 includes receiving user input that filters flight traffic information for multiple flights within a selected time period (e.g., user input 104, filter 108). The flight traffic information can be filtered based on market segmentation, carrier, geographic region, etc.

[0148] In method 900, method 930 includes applying a traffic growth model to flight traffic information to generate projected flight traffic information for a future time period (e.g., traffic growth model 110). The traffic growth model can be a custom annual growth rate, a default growth forecast based on region pairs, etc. In the example, where flight traffic information has already been filtered based on user input, method 900 may include applying a traffic growth model to the filtered flight traffic information to generate projected flight traffic information.

[0149] In method 940, method 900 includes performing baseline calculations (e.g., baseline calculation 112) on the anticipated flight traffic information to generate predicted aviation emissions information for a future time period. In some examples, method 900 includes applying the baseline calculations to the anticipated flight traffic information to generate predicted aviation emissions information that includes emissions information for each flight route. For example, emissions information for each flight route may include one or more of fuel combustion for each flight route, energy consumption for each flight route, and CO2 emissions for each flight route.

[0150] At 950, method 900 includes receiving user input at one or more processors, the user input indicating one or more parameters of each of one or more of a plurality of sustainability strategies. The plurality of sustainability strategies may include one or more of aircraft strategies (e.g., aircraft strategy model 118), operational efficiency strategies (e.g., operational efficiency model 124), energy strategies (e.g., energy strategy model 126), and market-based carbon offsetting strategies (e.g., market-based measures model 134). Aircraft strategies may include fleet renewal strategies (e.g., fleet renewal model component 120), future aircraft strategies (e.g., future aircraft model component 122), and / or aircraft route allocation strategies. Energy strategies may include one or more of sustainable aviation fuel models (e.g., sustainable aviation fuel model component 132), hydrogen fuel models (e.g., hydrogen model component 130), and electricity models (e.g., electricity model component 128). Sustainable aviation fuel models may include electro-hydraulic fuel models.

[0151] In 960, method 900 includes applying one or more of a plurality of sustainability strategies to the predicted aviation emissions information to generate adjusted predicted aviation emissions information. In an example where aviation emissions information has already been generated for each route, method 900 may include applying one or more of a plurality of sustainability strategies to the predicted aviation emissions information on a per-route basis.

[0152] In 960, method 900 includes indicating emission reduction potential for future time periods based on adjusted, projected aviation emission information. Emission reduction potential may indicate one or more of energy consumption, CO2 emissions, and fuel combustion for future time periods.

[0153] Optionally, in 980, method 900 includes applying one or more non-CO2 emission models to the predicted aviation emissions to generate non-CO2 emission reduction potentials for future time periods. For example, non-CO2 emission reduction potentials may include water vapor emission reductions, nitrogen oxide (NOx) emission reductions, sulfur oxide (SOx) emission reductions, and soot emission reductions for future time periods. Optionally, in 990, method 900 includes indicating aggregated climate impacts for future time periods based on non-CO2 emission reduction potentials.

[0154] In some examples, method 900 also includes visually presenting the predicted emissions information and the adjusted predicted emissions information at a display device (e.g., visualization 140, visualization chart 800).

[0155] Figure 10 A non-limiting embodiment of a computing system 1000 that can implement one or more of the methods and processes described above is illustrated schematically. The computing system 1000 is shown in a simplified form. The computing system 1000 may take the form of one or more personal computers, server computers, tablet computers, home entertainment computers, network computing devices, gaming devices, mobile computing devices, mobile communication devices (e.g., smartphones), and / or other computing devices.

[0156] The computing system 1000 includes a logic machine 1010 and a memory machine 1020. The computing system 1000 may optionally include a display subsystem 1030, an input subsystem 1040, a communication subsystem 1050, and / or... Figure 10 Other components not shown. Computing system 1000 is an example of a computing system that can be used to implement modeling pipeline 100 and method 900.

[0157] The logic machine 1010 includes one or more physical devices configured to execute instructions. For example, the logic machine may be configured to execute instructions as part of one or more application programs, services, programs, routines, libraries, objects, components, data structures, or other logical constructs. Such instructions can be implemented to perform tasks, implement data types, transition the state of one or more components, achieve technical effects, or otherwise achieve desired results.

[0158] A logic machine may include one or more processors configured to execute software instructions. Additionally or alternatively, a logic machine may include one or more hardware or firmware logic machines configured to execute hardware or firmware instructions. The logic machine's processor may be single-core or multi-core, and the instructions executed thereon may be configured for sequential, parallel, and / or distributed processing. Individual components of the logic machine may optionally be distributed across two or more separate devices that can be remotely located and / or configured for coordinated processing. Various aspects of the logic machine may be virtualized and executed by remotely accessible, networked computing devices configured in a cloud computing configuration.

[0159] The storage device 1020 includes one or more physical devices configured to store instructions that can be executed by a logic machine to implement the methods and processes described herein. When these methods and processes are implemented, the state of the storage device 1020 can be transformed—for example, to store different data.

[0160] Storage device 1020 may include removable and / or built-in devices. Storage device 1020 may include optical storage (e.g., CD, DVD, HD-DVD, Blu-ray disc, etc.), semiconductor storage (e.g., RAM, EPROM, EEPROM, etc.), and / or magnetic storage (e.g., hard disk drive, floppy disk drive, magnetic tape drive, MRAM, etc.), etc. Storage device 1020 may include volatile, non-volatile, dynamic, static, read / write, read-only, random access, sequential access, location-addressable, file-addressable, and / or content-addressable devices.

[0161] It should be understood that the storage device 1020 includes one or more physical devices. However, aspects of the instructions described herein may alternatively be propagated via a communication medium (e.g., electromagnetic signals, optical signals, etc.) that is not stored by the physical device for a finite duration.

[0162] Various aspects of the logic machine 1010 and the memory machine 1020 can be integrated together into one or more hardware logic components. For example, such hardware logic components may include field-programmable gate arrays (FPGAs), program-specific integrated circuits and application-specific integrated circuits (PASICs / ASICs), program-specific standard products and application-specific standard products (PSSPs / ASSPs), system-on-a-chip (SOCs), and complex programmable logic devices (CPLDs).

[0163] The terms "module," "program," and "engine" are used to describe aspects of a computing system 1000 implemented to perform specific functions. In some cases, a module, program, or engine can be instantiated by executing instructions stored in memory 1020 via logic machine 1010. It should be understood that different modules, programs, and / or engines can be instantiated from the same application, service, code block, object, library, routine, API, function, etc. Similarly, the same module, program, and / or engine can be instantiated by different applications, services, code blocks, objects, routines, APIs, functions, etc. The terms "module," "program," and "engine" can include individual or grouped executable files, data files, libraries, drivers, scripts, database records, etc.

[0164] It should be understood that, as used herein, a "service" is an application that can execute on multiple user sessions. A service can be used for one or more system components, programs, and / or other services. In some implementations, a service can run on one or more server computing devices.

[0165] When included, the display subsystem 1030 can be used to present a visual representation of the data stored in the storage machine 1020. This visual representation may take the form of a graphical user interface (GUI). As the methods and processes described herein change the data stored in the storage machine, and thus change the state of the storage machine, the state of the display subsystem 1030 can also be translated into a visual representation of the changes in the underlying data. The display subsystem 1030 may include one or more display devices utilizing virtually any type of technology. Such display devices may be combined with the logic machine 1010 and / or the storage machine 1020 in a shared enclosure, or such display devices may be peripheral display devices.

[0166] When included, the input subsystem 1040 may include or interface with one or more user input devices, such as a keyboard, mouse, touchscreen, or game controller. In some embodiments, the input subsystem may include or interface with a selected Natural User Input (NUI) component. Such a component may be integrated or peripheral, and the transduction and / or processing of input actions may be performed on-board or off-board. Example NUI components may include a microphone for speech and / or voice recognition; an infrared, color, stereo, and / or depth camera for machine vision and / or gesture recognition; a head tracker, eye tracker, accelerometer, and / or gyroscope for motion detection and / or intent recognition; and an electric field sensing component for assessing brain activity.

[0167] When included, the communication subsystem 1050 can be configured to communicatively couple the computing system 1000 to one or more other computing devices. The communication subsystem 1050 may include wired and / or wireless communication devices compatible with one or more different communication protocols. As a non-limiting example, the communication subsystem may be configured to communicate via a wireless telephone network, or a wired or wireless local area network or wide area network. In some embodiments, the communication subsystem may allow the computing system 1000 to send messages to and / or receive messages from other devices via a network such as the Internet.

[0168] In addition, this disclosure includes configurations based on the following examples.

[0169] Example 1. A method for modeling sustainability strategies for the aviation industry, the method comprising receiving flight traffic information for multiple flights within a selected time period at one or more processors; applying a traffic growth model to the flight traffic information to generate projected flight traffic information for a future time period; performing baseline calculations on the projected flight traffic information to generate projected aviation emissions information for the future time period; receiving user input at the one or more processors, the user input indicating one or more parameters for each of one or more of a plurality of sustainability strategies; applying the one or more of the plurality of sustainability strategies to the projected aviation emissions information to generate adjusted projected aviation emissions information; and indicating emission reduction potential for the future time period based on the adjusted projected aviation emissions information.

[0170] Example 2. The method according to Example 1, wherein performing baseline calculations on the projected flight traffic information further includes generating projected aviation emissions information that includes emissions information for each route; and applying one or more of the plurality of sustainability strategies to the projected aviation emissions information on a per-route basis.

[0171] Example 3. The method according to Examples 1 to 2, wherein the emission information for each route includes one or more of the following: fuel combustion for each route, energy consumption for each route, and CO2 emissions for each route.

[0172] Example 4. The method according to Examples 1 to 3 further includes receiving user input, the user input filtering the flight traffic information of the plurality of flights within the selected time period; and applying the traffic growth model to the filtered flight traffic information to generate expected flight traffic information.

[0173] Example 5. The method according to Examples 1 to 4 further includes visually presenting the predicted aviation emissions information and the adjusted predicted aviation emissions information at a display device.

[0174] Example 6. The method according to Examples 1 to 5, wherein the plurality of sustainability strategies includes one or more of an aircraft strategy, an operational efficiency strategy, an energy strategy, and a market-based carbon offsetting strategy.

[0175] Example 7. The method according to Examples 1 to 6, wherein the aircraft strategy includes one or more of a fleet renewal strategy, a future aircraft strategy, and an aircraft route allocation strategy.

[0176] Example 8. The method according to Examples 1 to 7, wherein the energy strategy includes one or more of a sustainable aviation fuel model, a hydrogen fuel model, and an electricity model.

[0177] Example 9. The method according to Examples 1 to 8, wherein the sustainable aviation fuel model includes an electro-liquid fuel model.

[0178] Example 10. The method according to Examples 1 to 9, wherein the emission reduction potential indicates one or more of energy consumption and CO2 emissions during the future time period.

[0179] Example 11. The method according to Examples 1 to 10 further includes applying one or more non-CO2 emission models to the predicted aviation emission information to generate non-CO2 emission reduction potential for the future time period; and indicating a summed-up climate impact for the future time period based on the non-CO2 emission reduction potential.

[0180] Example 12. The method according to Examples 1 to 11, wherein the non-CO2 emission reduction potential indicates one or more of water vapor emission reduction, nitrogen oxide (NOx) emission reduction, sulfur oxide (SOx) emission reduction and soot emission reduction during the future time period.

[0181] Example 13. A computational system for modeling sustainability strategies for the aviation industry, comprising a logic machine including one or more processors; a memory including instructions executable by the one or more processors to receive, at the one or more processors, flight traffic information for a plurality of flights within a selected time period; apply a traffic growth model to the flight traffic information to generate projected flight traffic information for a future time period; perform baseline calculations on the projected flight information to generate projected aviation emissions information for the future time period; receive user input at the one or more processors, the user input indicating one or more parameters of each of one or more of a plurality of sustainability strategies; apply the one or more of the plurality of sustainability strategies to the projected aviation emissions information to generate adjusted projected aviation emissions information; and indicate emission reduction potential for the future time period based on the adjusted projected aviation emissions information.

[0182] Example 14. The computing system according to Example 13, wherein the plurality of sustainability strategies includes one or more of an aircraft strategy, an operational efficiency strategy, an energy strategy, and a market-based carbon offsetting strategy.

[0183] Example 15. A computing system according to Examples 13 to 14, wherein the aircraft strategy includes an aircraft route allocation strategy.

[0184] Example 16. A computational system according to Examples 13 to 15, wherein the energy strategy includes one or more of a sustainable aviation fuel model, a hydrogen fuel model, and an electricity model.

[0185] Example 17. A computational system according to Examples 13 to 16, wherein the sustainable aviation fuel model includes an electro-liquid fuel model.

[0186] Example 18. A calculation system according to Examples 13 to 17, wherein the emission reduction potential indicates one or more of energy consumption, fuel combustion and CO2 emissions during the future time period.

[0187] Example 19. A computing system according to Examples 13 to 18, wherein the memory further stores instructions executable by the one or more processors to apply one or more non-CO2 emission models to the predicted aviation emission information to generate non-CO2 emission reduction potential for the future time period; and to indicate aggregated climate impacts for the future time period based on the non-CO2 emission reduction potential.

[0188] Example 20. A storage machine including instructions executable by one or more processors to receive, at one or more processors, flight traffic information for multiple flights within a selected time period; apply a traffic growth model to the flight traffic information to generate projected flight traffic information for a future time period; perform baseline calculations on the projected flight information to generate projected aviation emissions information for the future time period; receive, at one or more processors, user input indicating one or more parameters of each of one or more of a plurality of sustainability strategies; apply the one or more of the plurality of sustainability strategies to the projected aviation emissions information to generate adjusted projected aviation emissions information; and indicate emission reduction potential for the future time period based on the adjusted projected aviation emissions information.

[0189] It should be understood that the configurations and / or methods described herein are exemplary in nature, and these specific embodiments or examples should not be considered in a limiting sense, as many variations are possible. The specific routines or methods described herein may represent one or more of any number of processing strategies. Therefore, the various actions shown and / or described may be performed in the order shown and / or described, in other orders, in parallel, or omitted. Similarly, the order of the above processes may be changed.

[0190] The subject matter of this disclosure includes all novel and non-obvious combinations and sub-combinations of the various processes, systems and configurations disclosed herein, as well as any and all equivalents thereof.

[0191] Component list Modeling pipeline 100 Flight Traffic Dataset 102 User input 104 Preprocessing 106 Filter 108 Traffic Growth Model 110 Baseline calculation 112 Renewable Energy Emission Reduction Array 114 Sustainability Strategy 116 Aircraft Strategy Model 118 Fleet update model parts 120 Future aircraft model parts 122 Operational Efficiency Strategy Model 124 Energy Strategy Model 126 Electric Model Components 128 Hydrogen model component 130 Sustainable Aviation Fuel (SAF) Model Component 132 Market-based measures model 134 Non-CO2 emission model 136 Output index 138 Visualization 140 Example Energy Life Cycle Assessment Model 200 Discharge from well to tank 205 Fuel tank to tailrace emission 210 Aircraft 215 Energy 220 Fossil fuel extraction 222 Biological raw materials 224 Waste materials 226 Renewable electricity sources 228 Energy carrier 230 Fossil jet fuel 232 Sustainable Aviation Fuel 234 H2 236 Battery 238 Conversion Method 240 Internal combustion engine 242 Fuel Cell 244 Electric motor 246 Implementation curve 300 Previous generation aircraft 302 The latest generation of aircraft 304 The fleet grew by 306 Fleet replacement 308 Model 400 Route A 402 Route B 404 Route C 406 Future aircraft type 410 Aircraft A 412 Aircraft B 414 Aircraft C 416 Chart 500 Fleet Insertion Curve 502 Fleet Insertion Curve 504 Chart 600 Existing aircraft line 602 Best average line of its kind: 604 Reference aircraft line 606 User-defined aircraft line 608 Schematic diagram 700 Hydrogen production pathway 702 704 generators Gaseous hydrogen production 706 Hydrogen liquefaction 708 Hydrogen vapor loss 710 Liquid H2712 for aircraft Evaporation recovery system 714 716 Emissions into the atmosphere Visual charts 800 Traffic growth 802 Net CO2 emissions 804 Fleet Update 806 Future Aircraft 808 Operational efficiency 810 Renewable Energy 812 Market offset 814 Method 900 Method steps: 910, 920, 930, 940, 950, 960, 970, 980, 990 Computing System 1000 Logic Machine 1010 Storage machine 1020 Display Subsystem 1030 Input subsystem 1040 Communication Subsystem 1050

Claims

1. A method (900) for modeling sustainability strategies (116) in the aviation industry, the method (900) comprising: Receive (910) flight traffic information (102) for multiple flights within a selected time period at one or more processors (1010); The traffic growth model (110) is applied (930) to the flight traffic information (102) to generate the projected flight traffic information for a future time period; A baseline calculation (940) (112) is performed on the predicted flight traffic information to generate predicted aviation emissions information for the future time period; User input (104) is received (950) at one or more processors (1010), the user input (104) indicating one or more parameters for each of one or more of a plurality of sustainability strategies (116); Applying (960) one or more of the plurality of sustainability strategies (116) to the predicted aviation emissions information to generate adjusted predicted aviation emissions information; and Based on the adjusted and projected aviation emissions information, the potential for emissions reduction in the future time period is indicated (970).

2. The method (900) according to claim 1, wherein, Performing baseline calculations (940) (112) on the predicted flight traffic information further includes: Generate predicted aviation emissions information, including emissions data for each flight route; and On each route, one or more of the multiple sustainability strategies (116) will be applied to the predicted aviation emissions information.

3. The method (900) according to claim 2, wherein, The emissions information for each route includes one or more of the following: fuel combustion, energy consumption, and CO2 emissions for each route.

4. The method (900) according to claim 1, further comprising: Receive (920) user input (104), the user input filtering the flight traffic information of the multiple flights within the selected time period (102). and The traffic growth model (110) is applied to the filtered flight traffic information to generate projected flight traffic information.

5. The method (900) according to claim 1, further comprising: The predicted aviation emissions information and the adjusted predicted aviation emissions information are visually presented (140) at a display device (1030).

6. The method (900) according to claim 1, wherein, The plurality of sustainability strategies (116) include one or more of the following: aircraft strategy (118), operational efficiency strategy (124), energy strategy (126), and market-based carbon offsetting strategy (134).

7. The method (900) according to claim 6, wherein, The aircraft strategy (118) includes one or more of the following: fleet renewal strategy (120), future aircraft strategy (122), and aircraft route allocation strategy (400).

8. The method (900) according to claim 6, wherein the energy strategy (126) includes one or more of a sustainable aviation fuel model (132), a hydrogen fuel model (130), and an electricity model (128).

9. The method (900) according to claim 8, wherein the sustainable aviation fuel model (132) includes an electro-liquid fuel model.

10. The method (900) according to claim 1, wherein, The emission reduction potential refers to one or more of the energy consumption and CO2 emissions during the future time period.

Images