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Annual Advisory Board Review Tool
- 1. 23rd Annual External Advisory Board Review – April, 2014 – Atlanta, GA Advisor: Dr. Mavris Research Engineer: Dr. Pfaender Project Manager: Matt Schmit Chief Engineer: Erik Viken 2014 NASA Tech Integration Grand Challenge
- 2. Thank Sponsors • Thank you to NASA and Mr. Robert A. Pearce for the opportunity to work on this project • Special thanks to: – Advisors: Dr. Mavris and Dr. Pfaender – PhD Students: Muhammed Hassan, and Matt LeVine 2
- 3. Team Organization Case Study Project Overview Model Plan and Requirements Case Study Model Plan and Requirements Project Overview Team Organization Agenda 3 • Team Organization • Project Overview – Describe project objectives – Provide motivation for our work – Discuss findings from background research • Methodology of our Process – Describe goals of our tool – Outline the process and assumptions of tool • Case Study – Demonstration of tool • Concluding Remarks – Summarize work and results – Discuss future plans Conclusions and Future Work Conclusions and Future Work
- 4. Team Organization Prof. Dimitri Mavris Faculty Advisor ASDL Mohammed Hassan Technical Advisor Matthew LeVine Technical Advisor Matthew Schmit Project Manager Erik Viken Chief Engineer Charlotte Gill Aubrey Clausse Stephen Kim Dr. Jens Holger Pfaender Research Engineer 4 Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization
- 5. Modeling Environment? Project Objectives 5 • Develop an interface capable of quantifying NAS wide impact on CO2 emissions of: – New technologies – NextGen operational techniques – Low-carbon propulsion & biofuels • Provide tool for evaluating most effective way to invest in the “strategic thrusts” • Allow for real-time evaluation: – Fuel Burn – CO2 emissions • Capturing functional dependence between the dynamics of change and environmental impact: – Given a set of changes, what will be the net impact? – Given a specific environmental goal, how can I alter the dynamics to meet that goal? Dynamics of Change System Level Environmental Impact 1 2 1 2 Motivation Matt Schmit Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization
- 6. Climate Change • Anthropogenic greenhouse gases (GHG) are believed to be a major contributor • Why focus on CO2? – Radiative forcing – Direct correlation between temperature CO2 concentration 6 Major GHG Include: N2O CH4 CO2 Matt Schmit Increasing heat trapping ability per molecule Increasing radiative forcing of GHG [1] [2] Credit: IPCC [12] Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization
- 7. Global Carbon Cycle 7 Matt Schmit • Carbon Cycle represents the fluctuation of Earth’s Carbon between sources and sinks • Major Carbon Sinks: – Oceans – Plant photosynthesis – Earth’s crust – Atmosphere – Fossil fuel reserves • Major Carbon Emission Sources: – Fossil fuels – Net land use change – Plant respiration • IPCC estimates that the amount of CO2 in the atmosphere is increasing by ≈14.8 billion tons a year – Without fossil fuel emissions, CO2 concentration would decrease by ≈14.01 billion tons a year Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization Credit: IPCC [12] 𝟏 𝐏𝐠𝐂 = 𝟏 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 𝐭𝐨𝐧𝐬 𝐨𝐟 𝐜𝐚𝐫𝐛𝐨𝐧 = 𝟑. 𝟕 𝐛𝐢𝐥𝐥𝐢𝐨𝐧 𝐭𝐨𝐧𝐬 𝐨𝐟 𝐂𝐎 𝟐 To achieve a balanced Carbon Cycle, fossil fuel emissions must be reduced by ≈51%
- 8. CO2 Emissions in Aviation • Commercial aviation contributed ≈ 2% of global anthropogenic CO2 emissions in 2013 [3] • Air traffic operations are forecasted by the FAA to steadily increase through 2050 • In 2009, the International Air Transportation Association (IATA) adopted three high-level goals to mitigate aviation greenhouse emissions through 2050 [3] – Average improvement in fuel efficiency of 2% per year from 2009 to 2020 – Carbon-neutral growth by 2020 relative to 2005 levels – Reduction in net CO2 emissions of 50% by 2050 relative to 2005 levels 8 Matt Schmit [5] Recession [4] No action taken Number of Operations Steadily Increasing Recession Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization
- 9. [4] CO2 Emissions in Aviation 9 Matt Schmit • Commercial aviation contributed ≈ 2% of global anthropogenic CO2 emissions in 2013 [3] • Air traffic operations are forecasted by the FAA to steadily increase through 2050 • In 2009, the International Air Transportation Association (IATA) adopted three high-level goals to mitigate aviation greenhouse emissions through 2050 [3] – Average improvement in fuel efficiency of 2% per year from 2009 to 2020 – Carbon-neutral growth by 2020 relative to 2005 levels – Reduction in net CO2 emissions of 50% by 2050 relative to 2005 levels • Plan to achieve these CO2 emission goals using a series of “strategic thrusts” Thrust 3: Low Carbon Propulsion & Biofuels Thrust 2: NextGen / Efficient Flight Path Management Thrust 1: Ultra-Efficient Aircraft Technologies Which thrusts should be invested in order to achieve the desired reduction in CO2 emissions? Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization
- 10. Thrust 1: Efficient Aircraft Technologies 10 Matt Schmit • Achieve reduction in CO2 emissions by improving fuel efficiency of aircraft – Technologies will be pursued as long as they present an economic advantage • What is the expected improvement? – What is the reference point? – When will the technology/aircraft be ready for entry into service? – What is the scope of the application? • Evolution of fleet must account for: aircraft retirements, replacements, and fleet growth – Retired vehicles are replaced with newer ones (replacement vehicles) – In addition, new vehicles are added to satisfy operational growth (according to forecast) • Future fleet will be dominated by new- generation technology vehicles incorporating technologies from: CLEEN, ERA, and N+3 Airbus A320 Airbus A320Neo 15% Improvement in Fuel Efficiency Expected Starting 2016 Seating capacity: 150 – 189 Range: up to 3,300 nmi Baseline Fleet Future Fleet Retired Vehicles Replacement Vehicles Fleet Growth Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization
- 11. Thrust 1: Efficient Aircraft Technologies 11 Matt Schmit • FAA CLEEN (Continuous Lower Energy, Emissions, and Noise) Program (N+1 Vehicle) – By 2015, Technology Readiness Level of 4 – 6 – Adaptive Trailing Edge – Ceramic Matrix Composite Nozzle • ERA (Environmentally Responsible Aviation) Project (N+2 Vehicle) – By 2020, Technology Readiness Level of 4 – 6 – AFC Vertical Tail – Flow Control Concepts for Drag Reduction • NASA (N+3 Concepts) – By 2035, Technology Readiness Level of 4 – 6 – Advanced vehicle concepts – Hybrid electric distributed propulsion Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization [16] Hybrid Wing-Body Advanced Concept [17] Hybrid Electric Distribution Propulsion [15] AFC Vertical Tail [13] Adaptive Trailing Edge [14] Ceramic Matrix Composite Nozzles Drag Reduction via Laminar Flow
- 12. These technologies allow for improved operational capabilities, but do not impact the fuel burn efficiency of aircraft. • Flight management shift to smarter, satellite-based, digital technologies and new procedures – Improve throughput and airport capacity while maintaining safety Thrust 2: FAA NextGen Operations 12 Matt Schmit Reduction in CO2 Emissions Fuel Savings [7] [8] [9] • Data Communication (Data Comm) – Digital data exchange between air traffic control and pilots Reduce Flight Time / Delays Improved Operational Capabilities [10] Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization • Automatic Dependent Surveillance- Broadcast (ADS-B) In/Out – More precise tracking leads to more direct routes Notable NextGen Programs Improved Operational Capabilities • Optimized Profile Descent (OPD) • Required Navigation Performance (RNP)
- 13. Thrust 3: Transition to Low-Carbon Propulsion 13 Matt Schmit • Low-Carbon propulsion options: – Biofuels (Near term solution) – Alternative fuel sources (Far term solution) • Biofuels – Consider “Well to Pump” CO2 emissions – “Drop-In” fuels – CO2 reduction dependent on percent blend of mixture, and specific biofuel used – Requires fuel to be sustainable • Alternative fuel sources – Non “Drop-In” fuel alternatives • Hydrogen • Liquefied Natural Gas (LNG) • Hybrid Electric Vehicle – Require extensive development as well as safety and design certification (available 2040 – 2050 timeframe) [11] Fossil Fuels Biofuels Boeing Phantom Eye Boeing Sugar Freeze Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization NASA Hybrid Electric Concept
- 14. Global Market Measures 14 Matt Schmit • Last resort to fill in the gaps if desired CO2 reductions aren’t met in time – Economic incentives to reduce carbon emissions • Carbon Tax – Carbon “pollution’ fee assessed to carbon content of fuels – Factored into airline ticket price – Increased ticket price → less passengers → reduced operations → reduced CO2 emissions • Carbon offsetting – unit of carbon dioxide-equivalent (CO2e) that is reduced, avoided, or sequestered to compensate for emissions occurring elsewhere [19] – i.e. planting forests to offset carbon emissions Motivation Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Project Objectives Strategic Thrusts Team Organization [4]
- 15. Global Perspective of NAS Analysis and Forecasting 15 Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Strategic Initiatives Aircraft Technologies NextGen Low Carbon Propulsion CFD, Engine Modeling, Vehicle Sizing and Integration, Mission Profile Optimization, Air Traffic Models Finite Element Analysis Operational Improvements Alternative Fuels/Propulsion Fuel Burn Reduction Aero Technologies Fuel Burn Reduction M&S Environment National Airspace M&S Environment Vehicle Level Improvements Decision Making Forecasted NAS Impact Fleet-Wide Fuel Burn Analysis and Forecasting Environment Matt Schmit
- 16. Need for New Forecast Environment 16 Matt Schmit - Traffic - Weather - Management - Other factors Variation - Flight time and distance - Loitering time - Time on the ground Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization • Most forecast tools utilize distance as metric to calculate fuel burn – No available database for actual flight distance – Great circle distance is used, but not accurate – Deterministic model • Use flight time as fuel burn metric – Publically available databases – Capable of modeling with probabilistic approach • Modeling uncertainties: – Uncertainties interact and propagate across fleet – Flight time captures these uncertainties – Can create probabilistic time model by segment Uncertainties Exist: Variation in Flight Time: Must account for variation in flight times Clear need to develop new, probabilistic forecasting environment. RamptoRampTime(min) Distance (mi)
- 17. • Requirements for NAS wide assessment of future CO2 emissions: – Determine current fleet network – Determine probabilistic operational times – Establish future network – Model impact of NextGen operation techniques – Model impact of future vehicle performance – Model impact of biofuels – Evaluate NAS wide CO2 emissions • Architectural environment that satisfies the requirements • Future Air Traffic Emissions Modeling Environment Evaluate NAS Wide CO2 Emissions 7 Model Plan and Requirements 17 Determine Current Fleet Network 1 Determine Probabilistic Operational Times 2 Establish Future Network 3 Model NextGen Operation Techniques 4 Model Impact of Biofuels 6 Model Future Vehicle Performance 5 Matt Schmit Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Model Future Operations Modeling Improvement Evaluate CO2 Emissions
- 18. 18 Determine Probabilistic Operational Times Model Impact of Biofuels Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Matt Schmit Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Evaluate NAS Wide CO2 Emissions 7 Determine Current Fleet Network 1 Determine Probabilistic Operational Times 2 Establish Future Network 3 Model NextGen Operation Techniques 4 Model Impact of Biofuels 6 Model Future Vehicle Performance 5
- 19. 19 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 20. Determine Current Fleet Network 20 • Need a NAS wide performance model for: – Taxi out time – Air time – Taxi in time • Performance model should be specific to aircraft class • On Time Performance Data – Aircraft Tail # – Origin – Destination – Air time, Taxi in, Taxi out • N-Inquiry: FAA registry of aircraft – Tail number – Specific aircraft type Tail # Origin Dest Taxi Out Air Time Taxi In N525US ATL BOS 19 105 13 N143DA ATL LAX 16 256 15 N172AA BOS ORF 15 110 10 Tail # Aircraft Type N525US 757-251 N143DA 767-332 N172AA A320-200 Aircraft Type Origin Dest Taxi Out Air Time Taxi In 757-251 ATL BOS 19 105 13 767-332 ATL LAX 16 256 15 A320-200 BOS ORF 15 110 10 Available Public Databases Time Breakdown: On Time Performance Data: N-Inquiry: Combined Database: Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions
- 21. 21 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 22. Probabilistic Time Modeling 22 • Factors of influence on time performance • Classification needed – Airport size – Aircraft class • Creation of distribution database – Taxi In – Taxi Out – Air Time • Enable to generate new flight times – Based on the current NAS system – No new routes added – Behavior of the NAS independent of the number of flight 0 20 'ATL' 'BOS' 'LAS' 'SFO' Time(min) Airport City Average Taxi Out 0 25 '717' '757' 'A330' Time(min) Aircraft Type Atlanta Average Taxi out Input Impacts: Fitting Distribution: Origin Class 1 Class 2 … ATL 𝑝 = 𝑓(𝑡) … … BOS … … … Distribution Database – Taxi Out Generate New Flight Time: Origin Dest Class1 ATL BOS 1200 0 100 1 16 31 Count Time (min) Taxi Out - ATL 0 200 1 11 21 Count Time (min) Taxi In - BOS 0 90 105 120 135 150 Count Time (min) Air Time - ATL-BOS Aubrey Clausse 0 2000 4000 6000 1 11 21 31 41 51 Count Time (min) ATL - Taxi Out – Class 1 0 2000 4000 6000 1 11 21 31 41 51 Count Time (min) ATL - Taxi Out – Class 1 - Airport - Type of Aircraft - Weather - Queue Management - Other factors Probabilistic Approach - Type of Aircraft - Airport Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions
- 23. Classification 23 • Airport Classification – Commercial Service Airports • Public Owned • At least 2,500 passengers per year – Primary Hub • More than 10,000 passengers per year • Aircraft Classification – Based on previous work done at ASDL [18] – Vehicle grouped by • Fuel burned • NOx emissions • Sound Exposure Level Aubrey Clausse Name Large Hub Medium Hub Small Hub NonHub Primary Annual Passenger Boardings NP > 1% NP< 1% NP>0.25% NP< 0.25 % NP> 0.05 % NP< 0.05% Airport Classification Definition Aircraft Classification Aircraft Class Acronym Example(s) Small Regional Jet SRJ CRJ2-ER / ERJ 135 Large Regional Jet LRJ CRJ-900 / ERJ 170 Small Single Aisle SSA B737-7 / A319-1 Large Single Aisle LSA B737-9 / A320-2 Small Twin Aisle STA B767-3 / A330-2 Large Twin Aisle LTA B777 Very Large Aircraft VLA B747 Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Classifications represent averaged vehicle properties for a given group of similar aircraft NP = percentage of annual passenger boardings at a given airport
- 24. 24 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 25. • Baseline network established – Total number of operation for every airport – On Time Performance database • Terminal Area Forecast – Forecast future operations at every airport – Demand driven forecast • Fares, regional demographics factors • Local/National economic conditions • Fratar Distribution Algorithm – Distribute total operations between origin and destination airports of network – Iterative process • Assumptions: – Network structure stays the same – Operations based on growth of Origin and Destination Future Network 25 Baseline Operations Forecast Operations … … … … … … … … … … … … Origin Destination Σ… Σ… Σ… Σ… Σ… Σ… Σ… Total number of operations for every airport … … … … … … … … … … … … Origin Destination Total number of operations for every airport Fratar Distribution Algorithm Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization
- 26. Generating Future Flight Times 26 • Read in forecast for every year – Number of flights per year for every OD pair – Group by aircraft class • Generate future flight and taxi times – For each OD pair – For each class of aircraft – Based on probabilistic models of NAS • Operational times are stored in Flight Time Database • Assumptions: – Proportions of aircraft class flown between each OD pair are known – Next generations of vehicles introduced in future network replace aircraft of similar class Origin Destination Number of Flights ATL BOS 1200 BOS LAS 1500 Future Flight Forecast: Origin Dest Class 1 … Class 4 ATL BOS 288 … 912 BOS LAS 700 … 800 Origin Dest Class Taxi Out Air Time Taxi In ATL BOS 1 [10,15,...] [97, 92,…] [5, 8,...] ATL BOS 4 [14,19,...] [88, 95,…] [10, 12,...] BOS LAS 3 [8,18,...] [132, 128,…] [7, 9,...] BOS LAS 4 [12,9,...] [130, 142,…] [12, 15,...] 0 50 100 1 11 21 31 Count Time (min) Taxi Out - ATL 0 200 1 6 11 16 21 Count Time (min) Taxi In - BOS Flight Time Database: 0 20 40 60 90 105 120 135 150 Count Time (min) Air Time - ATL-BOS Forecast by Aircraft Class: Class 1 24% Class 4 76% Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions
- 27. 27 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 28. Origin Class 1 … Class 4 ATL 𝑝1 = 𝑓(𝑡) … 𝑝2 = 𝑓(𝑡) BOS 𝑝3 = 𝑓(𝑡) … 𝑝4 = 𝑓(𝑡) Modeling Impact of New Operations 28 • Operation performances are modeled by: – Probabilistic model – Based on historical data • Introducing operation improvement: – Efficiency (mean) improvement – Consistency (variance) improvement • Flexibility in implementation: – Year of introduction – Airport affected – Class of aircraft affected Updated Operations Database Taxi Out Model the NAS performance: 0 0.1 0 10 20 30 40 Probability Time (min) Consistency Improvement Original Variance: -20% 0 0.1 0 10 20 30 40 Probability Time (min) Efficiency Improvement Original Mean: -10% Flexibility in implementation: Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Aubrey Clausse
- 29. 29 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 30. Modeling Future Vehicle Performance Impact 30 • Modeling vehicle performance for each aircraft class: – Air time: regression coefficients of fuel burn vs. air time – Taxi: idle fuel flow coefficients of engine (constant) • Introducing performance improvement: – Technology packages • Contain fuel flow coefficients for each class of aircraft – User can specify new or additional set of technology packages • Flexibility in implementation: – Year of introduction – Technology package used – Percentage of the fleet affected Flexibility in implementation: Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions N+1 N+2 N+3 Forecasting Environment Fuel Flow Rates
- 31. Model Impact of Biofuels 31 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions
- 32. Assumptions for Modeling Biofuels 32 • CO2 emissions of biofuels are dependent on control volume – Flight emissions identical to fossil fuels – Transportation, refining, and distribution processes comparable to fossil fuels – Receive “Biomass Credit” from feedstock growth • Final CO2 emissions reduced due to “Biomass Credit” – CO2 reduction is function of biofuel percent blend and type of biofuel used – Fuel burn is still identical • Flexibility in implementation: – Type of biofuel used – Year of implementation – Biofuel percent blend – Percentage of the fleet using biofuel Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Flight Emissions Processing and Refining Transport Distribution Feedstock Growth Biomass Credits Control Volume 𝑪𝑶 𝟐 𝒃𝒊𝒐𝒇𝒖𝒆𝒍𝒔 = 𝑪𝑶 𝟐 𝒇𝒐𝒔𝒔𝒊𝒍 𝒇𝒖𝒆𝒍𝒔 Flexibility in implementation: 𝑪𝑶 𝟐 𝒃𝒊𝒐𝒇𝒖𝒆𝒍𝒔 ≈ 𝑪𝑶 𝟐 𝒇𝒐𝒔𝒔𝒊𝒍 𝒇𝒖𝒆𝒍𝒔𝑪𝑶 𝟐 𝒃𝒊𝒐𝒇𝒖𝒆𝒍𝒔 < 𝑪𝑶 𝟐 𝒇𝒐𝒔𝒔𝒊𝒍 𝒇𝒖𝒆𝒍𝒔CO2 emissions:
- 33. 33 Determine Probabilistic Operational Times Establish Future Network Model Future Vehicle Performance Model NextGen Operation Techniques Determine Current Fleet Network Evaluate NAS Wide CO2 Emissions Aubrey Clausse Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Model Impact of Biofuels
- 34. Evaluating NAS Wide CO2 Emissions 34 • Apply operational performance changes – Adjust efficiency/consistency of operations – Generate flight and taxi times for all OD pairs for each year • Calculate NAS wide fuel burn – Fuel burn for a given flight is based on: • Aircraft type (Performance) • Stage time spent in each operational phase – Summation across all OD pairs • CO2 emissions is directly proportional to fuel burn • Apply reductions to CO2 emissions due to use of biofuels where applicable Total CO2 emissions ∝ Total Fuel Burn Final CO2 emissions = 𝑇𝑜𝑡𝑎𝑙 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑠 − 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝐶𝑟𝑒𝑑𝑖𝑡𝑠 Aubrey Clausse 0 0.1 0 10 20 30 40 Probability Time (min) Consistency Improvement Original Variance: -20% 0 0.1 0 10 20 30 40 Probability Time (min) Efficiency Improvement Original Mean: -10% Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Improvement Future Operations Evaluate CO2 Emissions Total Fuel Burn = j=OD pair i=AC Type k=Op. Phase m=Stage Time Performancej,i,k ∗ Timej,i,k,m
- 35. Case Study 35 Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo Matt Schmit • Created flight time probability distributions based on 2010 On Time Performance database – Used Fratar algorithm to forecast operations through 2050 • Used work previously done at ASDL to model flight performance of baseline aircraft fleet – Aircraft modeled in Environmental Design Space (EDS) • Conducted a review of industry leaders and policy makers – Based impacts and implementations of strategic thrust on their estimations
- 36. Thrust 1: Efficient Aircraft Technologies 36 • New technologies will be implemented on new vehicles – Entered as technology packages containing fuel burn for each class of aircraft • Boeing’s Market Outlook was used to predict the number of new aircraft per year [21] – Includes number of replacement vehicles and fleet growth vehicles – Depending on the year, N+1, N+2 or N+3 goals were applied to find the fuel burn reduction • Fuel burn reductions applied relative to baseline fleet – EDS physics based models used to calculate fuel burn of baseline fleet Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo Matt Schmit N+1 (2015) N+2 (2020) N + 3 (2025) Aircraft Fuel/Energy Consumption [22] (rel. to 2005 best in class) -33% -50% -60%
- 37. Thrust 2: FAA NextGen Operations 37 • Required Navigation Performance – Allows aircraft to fly wind optimal routes – CNA solutions and analysis conducted a study on flight time reductions when using 4-D flight routing • Optimized Profile Descent (OPD) – Used effectively in NAS – US Airways flight into Washington Reagan saved 70 gallons [9] • Can enter flight time reductions into tool – Flight time reduction decreases total fuel burn, decreasing CO2 emissions – OPD modeled by converting fuel savings to a reduction in flight time [23] Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo Matt Schmit [9] Optimized Profile Descent Required Navigation Performance
- 38. Thrust 3: Transition to Low-Carbon Propulsion 38 2010 2015 2020 Scenario 0.00% 10.00% 23.40% Percent Replacement Year FT-CBTL (millions of gallons) HEFA-J (millions of gallons) 2013 124.2 153.2 … … … 2020 1046.3 2673.7 Availability Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo Matt Schmit Biofuels • U.S. Department of Transportation study on use of biofuels through 2020 [20] – Availability of biofuels – Demand of biofuels (replacement over conventional jet fuel) – Extrapolated to get estimations through 2050 • Modeled impact of two biofuels: – Hydro-Processed Esters and Fatty Acids (HEFA) – Fischer-Tropsch Coal/Biomass to Liquid (FT-CBTL) • Not directly modeled yet – Assumed notional values for CO2 reductions Alternative Fuel Sources
- 39. Demo of FATE 39 Matt Schmit Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo
- 40. Proof of Concept • Our results indicate that new efficient aircraft technologies will have greatest impact – Modeling low-carbon propulsion will improve results – Economic measures are likely to be required • FATE demonstrates a capability – Probabilistic model of ramp-to-ramp flight times – Uses inputs from high fidelity M&S tools – Provides real time feedback • Results are only as reliable as inputs – Our work based on public domain databases – Impacts based on industry predictions and forecasts – Accuracy and fidelity of tool could be greatly improved if given access to better databases 40 Matt Schmit Methodology used in FATE is effective and viable for modeling NAS wide impact of strategic thrust on CO2 emissions. Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization Modeling Thrusts Overview Demo
- 41. [4] Conclusion and Future Work • FATE provides the capability to quantify and evaluate the NAS wide impact of: technologies, operational improvements, and biofuels – Aid in determining which strategic thrusts to invest in – Provides real-time estimations of CO2 emissions – Flexibility to add new components to tool • Future Work: – Model low-carbon propulsion technologies – Quantify interaction between operations and technologies – Add capability to vary assumptions to assess their impacts – Capture economic measures through reduction in operations 41 Matt Schmit Inputs and Parameters • Base Fleet Composition • Base Flight Operations • Technology Packages • Biofuel Packages Outputs and Metrics • NAS Wide Fuel Burn through 2050 • NAS Wide CO2 Emissions through 2050 Project Overview Case Study Conclusions and Future Work Model Plan and Requirements Team Organization
- 42. Questions? 42
- 43. References 1. "How Much Has the Global Temperature Risen in the Last 100 Years? | UCAR - University Corporation for Atmospheric Research." University Corporation for Atmospheric Research. National Center for Atmospheric Research, n.d. Web. 14 Mar. 2015. <https://www2.ucar.edu/news/how-much-has-global-temperature-risen-last-100-years>. 2. "Global Climate Change Indicators." National Climate Data Center. National Oceanic and Atmospheric Administration, n.d. Web. 14 Mar. 2015. <http://www.ncdc.noaa.gov/indicators/>. 3. “IATA Technology Road Map." IATA Technology Roadmap. IATA, 4th Edition, June 2013. Web. 17 Oct. 2014. 4. Rogers, M. M., “Technical Challenges to Reducing Subsonic Transport Drag,” National Aeronautics and Space Administration, URL:http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20120006660_2012004695.pdf 5. Georgia Tech paper on CO2 emissions 6. http://www.theguardian.com/environment/2012/jan/16/greenhouse-gases-remain-air 7. "How Plane Finder Works Using ADS-B." Planefindernet RSS. N.p., n.d. Web. 16 Oct. 2014. <http://planefinder.net/about/ads-b-how-planefinder-works/>. 8. "Performance Based Navigation (PBN)." NextGen Performance Based Navigation (PBN) (n.d.): n. pag. June 2013. Web. 16 Oct. 2014. 9. NextGen Update 2014. Tech. Washington D.C.: Federal Aviation Administration, 2014. Print. 10. https://www.faa.gov/about/office_org/headquarters_offices/ato/service_units/techops/atc_comms_services/datacomm/documentation/media/brochures/90818_DataComm_11x1 7_PRINT4.pdf 11. http://www.qantas.com.au/travel/airlines/sustainable-aviation-fuel/global/en 12. Gunnar Myhre (Norway), Drew Shindell (Usa). Climate Change 2013: The Physical Science Basis. (n.d.): n. pag. IPCC. Interngovernmental Panel of Climate Change. Web. 13. https://www.nasa.gov/press/2014/november/nasa-tests-revolutionary-shape-changing-aircraft-flap-for-the-first-time/ 14. http://globalaviationreport.com/2014/12/23/boeing-2014-top-photos/ 15. http://aviationweek.com/technology/757-ecodemo-focuses-laminar-and-active-flow 16. http://www.nasa.gov/content/down-to-earth-future-aircraft-0/#.VSvthfnF-So 17. http://www.slideshare.net/marcusforpresident2012/nasa-aviation-alternative-fuels-workshop 18. Matthew J. LeVine, Amelia Wilson, Dr. Michelle Kirby and Prof. Dimitri Mavris : Development of Generic Vehicles for Fleet-Level Analysis of Noise and Emissions Tradeoffs, AIAA Aviation, 16-20 June 2014, Atlanta, GA 19. http://www.wri.org/publication/bottom-line-offsets 20. Lewis, Kristin, Shuchi Mitra, and Sheila Xu. Alternative Jet Fuel Scenario Analysis Report. Rep. no. DOT-VNTSC-FAA-12-01. U.S. Department of Transportation, Nov. 2012. Web. 14 Apr. 2015. 21. “Current Market Outlook.” Boeing. Boeing, 2015. Web. 22. Collier, Fay. “Environmentally Responsible Aviation Project Real Solutions for Environmental Challenges Facing Aviation.” AIAA. NASA, 2012. Web. 43
- 45. Probabilistic NAS Performance Model 45 Origin Destination Air-Time Ramp to Ramp Time Taxi Out Taxi In
- 46. 46 Name of the person presenting this slide 0 2000 4000 6000 8000 10000 12000 14000 16000 2015 2020 2025 2030 2035 2040 2045 2050 BiofuelAvailable(millionsofgallons) Year FT-CBTL HEFA-J
- 47. Technology and Fuel Cost 1973: Oil Embargo 1975: Advanced Turboprop Project Announced 1980 and 1981: Iranian Revolution and Iran-Iraq War 1983: GE UDF unveiled 1989: End of Program 1986: UDF 1st test flight on 7J7
- 48. Fratar Algorithm • Iterative Growth Factor Method • Two big assumptions: 1. The previous pattern for flights between origin and destination pairs will remain the same 2. The volume of trips will change according to the growth of the origin and destination airports 48 Tij = tij *gi *gj * 1 2 di tim *gmm å + aj tjm *gmm å é ë ê ê ù û ú ú ,"i, j Î I Predicted traffic from airport i to airport j Previous year traffic from airport i to airport j From BTS data Growth factor: • gi is the ratio of predicted future departures from airport i to previous year departures in airport i • gj is the ratio of predicted future arrivals from airport j to previous year arrivals in airport j Predictions come from TAF data, previous year operations are from BTS data di= Total previous year departures at airport i ai= Total previous year arrivals at airport j tim=sum of all the traffic from airport I to airport m tjm=sum of all the traffic from airport I to airport m gm=growth factor airport m All but gm are from BTS data. gm is found using TAF
Hinweis der Redaktion
- Radiative forcing, or climate forcing, is defined as the difference between the sunlight absorbed by Earth and the energy radiated back into space Typically quantified at the tropopause in units of watts per square meter of the Earth’s surface
- Change word cominated
- Time frame: http://www.aeronautics.nasa.gov/pdf/era_preproposal_n2_adv_vechicle2010.pdf NASA ERA technologies: http://www.aeronautics.nasa.gov/iasp/era/index.htm Drag reduction picture: https://www.aiaa.org/uploadedFiles/About-AIAA/Press-Room/Key_Speeches-Reports-and-Presentations/2012/Collier-NASA-AVC-AIAA-GEPC2-2.pdf
- NextGen is the FAA’s initiative to improve flight management operations. They plan to accomplish by transitioning the current system towards smarter, satellite-based technologies while improving flight routes. This will lead to increased safety, reduced delays, fuel savings, and decreased emissions. On the right is a list of some of the significant NextGen programs. (Point out one or two that look interesting and just read the bullet) Data comm is a transition from verbal exchanges between pilots and controllers to digital messages projected onto the heads up displays CSS-Wx is a unified weather database that pilots and controllers can access in order to keep up to date with the weather conditions in the NAS
- The Roundtable on Sustainable Biomaterials is the standard of choice for the European Commission Alternative jet fuel emits about the same amount of CO2 as conventional jet fuel when burned due to its chemical similarity to conventional jet fuel since it has to be made to be a drop in fuel. Companies can consider the lifecycle emissions of creating the biofuel to reduce the overall emissions into the atmosphere While this allows some biofuels to have up to 80% less emissions than conventional fuel, the altitude of this fuel burn must be considered [1]. Critics of current EU biofuel policy say it is driving land-grabs in the developing world and diverting food crops to fuel use. Because of changes in land use, and resulting deforestation, first-generation biofuel is causing more CO2 to be released into the atmosphere than it saves through use as a fuel, it is alleged. Second-generation biofuel that is not derived from food crops should be incentivised in EU policy instead, say critics Altitude: ***Alternative fuel emits the same amount of GHG as conventional jet fuel due to its chemical similarity, but some have worse effects at altitude, such as NOx. ***“With CO2, it doesn’t matter where the emissions take place-whether it be the tropics or the north pole, the impact is the same, which isnt the case for Nox emissions.” [1] IATA 2011 Report on Alternative Fuels. Montreal, Quebec, Canada: International Air Transport Association, 2013. 2013. Web. 2 Dec. 2014. <http://www.iata.org/publications/Documents/2013-report-alternative-fuels.pdf>. [2] “RSB GHG Calculation Methodology.” Roundtable on Sustainable Biofuels 2.1 (2011): n. pag. 12 July 2012. Web. 2 Dec. 2014. <http://rsb.org/pdfs/12-12-20-RSB-STD-01-003-01-RSB-GHG-Calculation-Methodology-v2-1.pdf>. [3] “The High-altitude Effects of Non-CO2 Greenhouse Gases Caused by Aviation Are Still Uncertain, Say Scientists on GreenAir Online.” GreenAir Online. N.p., 6 May 2008. Web. 02 Dec. 2014. <http://www.greenaironline.com/news.php?viewStory=278>. [4] Keating, David. “Council Rejects Biofuel Compromise.” European Voice. N.p., 12 Dec. 2013. Web. 02 Dec. 2014. <http://www.europeanvoice.com/article/council-rejects-biofuel-compromise/>. [5] “Boeing SUGAR volt Hybrid Airplane.” IEEE. May 2012. Web. 18 Oct. 2014. [6] “Sustainable Aviation Fuel | Airbus, A Leading Aircraft Manufacturer.” Airbus. N.p., n.d. Web. 02 Dec. 2014. <http://www.airbus.com/innovation/eco-efficiency/operations/alternative-fuels/>. Other considerations citations: http://lae.mit.edu/fueling-the-future-of-flight/
- Exemple of January 20 12 – ATL – BOS Bigger fonts on on chart Aircraft type instead of class
- Show a chart that classifies aircraft types
- 1. Report on availability: Lewis, Kristin, Shuchi Mitra, and Sheila Xu. Alternative Jet Fuel Scenario Analysis Report. Rep. no. DOT-VNTSC-FAA-12-01. U.S. Department of Transportation, Nov. 2012. Web. 14 Apr. 2015. 2. Report on CO2 emissions: Stratton, Russell W. "Life Cycle Assessment of Greenhouse Gas Emissions and Non-CO2 Combustion Effects from Alternative Jet Fuels." Thesis. Queen's University, 2008. DSpace@MIT. 23 June 2010. Web. 14 Apr. 2015. 3. http://www.airportsinternational.com/2010/09/greener-skies/4790 4. http://www.intechopen.com/source/html/42030/media/fig3.png
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- Need a case study. ERA technology already in LAB, gives a bottom up assessment Get figures from NextGen and Biofuels in future
- Crude Oil first purchase price: http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=F000000__3&f=A Crude oil price graph: http://www.eia.gov/totalenergy/data/monthly/pdf/sec9.pdf (page 2) Articles on the matter: http://www.airspacemag.com/history-of-flight/the-short-happy-life-of-the-prop-fan-7856180/?no-ist http://www.flightglobal.com/news/articles/whatever-happened-to-propfans-214520/ http://www.sustainableaviation.co.uk/wp-content/uploads/open-rotor-engine-briefing-paper.pdf (page 4) Pictures: First picture: http://blog.cafefoundation.org/quiet-may-be-the-new-black/ Second Picture: http://mrlarry.org/serendipity/ Third Picture: http://www.flightglobal.com/news/articles/whatever-happened-to-propfans-214520/
- Assumptions: http://www.tcd.ie/civileng/Staff/Brian.Caulfield/T2%20-%20Transport%20Modelling/Lecture%203.pdf