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Project 10 Aircraft Technology Modeling and Assessment: Phase I Report

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Project 10 Aircraft Technology Modeling and Assessment: Phase I Report Project 10 Aircraft Technology Modeling and Assessment: Phase I Report Georgia Institute of Technology, Purdue University, Stanford University Project Lead Investigators Dimitri Mavris (PI) Regents Professor School of Aerospace Engineering Georgia Institute of Technology Mail Stop 0150 Atlanta, GA 30332-0150 Phone: 404-894-1557 Email: [email protected] Daniel DeLaurentis (PI) Professor School of Aeronautics and Astronautics Purdue University 701 W. Stadium Ave West Lafayette, IN 47907-2045 Phone: 765-494-0694 Email: [email protected] William Crossley (Co-PI) Professor School of Aeronautics and Astronautics Purdue University 701 W. Stadium Ave West Lafayette, IN 47907-2045 Phone: 765-496-2872 Email: [email protected] Juan J. Alonso (PI) Professor Department of Aeronautics & Astronautics Stanford University 496 Lomita Mall, Durand Bldg. Room 252 Stanford, CA 94305-4035 Phone: 650-723-9954 Email: [email protected] i Executive Summary Georgia Tech, Purdue, and Stanford partnered to investigate the impact of aircraft and vehicle technologies and the future state of demand for aviation on future environmental impacts of aviation. In the context of this research, environmental impacts includes direct CO2 emissions and noise. The research was conducted as a collaborative effort in order to leverage capabilities and knowledge available from the multiple entities that make up the ASCENT university partners and advisory committee. The primary objective of this research project was to support the Federal Aviation Administration (FAA) in modeling and assessing the potential future evolution of the next generation aircraft fleet. Research under this project consisted of three integrated focus areas: (1) Developing a set of harmonized fleet assumptions for use in future fleet assessments; (2) Modeling advanced aircraft technologies and advanced vehicles expected to enter the fleet through 2050; and (3) Performing vehicle and fleet level assessments based on input from the FAA and the results of (1) and (2). The team organized a series of virtual workshops to identify and define a standard set of assumptions to use as inputs to aircraft and fleet level modeling tools. A total of four workshops were held with a range of experts from industry, government, and academia. Two of the workshops focused on defining technology impact and development assumptions; the other two workshops focused on identifying and defining the values of factors important to fleet impacts modeling. The outcome of the technology modeling workshops is a series of infographics that provide recommended assumptions that can be used in any aircraft or engine conceptual design tool. Based on the variation in responses, the technology infographics contain minimum, nominal, and maximum estimates of the impact on natural metrics, such as lift-to-drag ratio, or structural efficiency relative to a current day baseline. Estimates are provided for twenty-two key impact areas for technology impact level, maturation rate, current Technology Readiness Level (TRL), applicable subsystems, and applicable vehicle size classes. Estimates are provided for near, medium, and far terms implementation to enable creation of N+1, N+2, and N+3 representative vehicles through modeling and simulation. Minimum, nominal, and maximum estimates of these metrics can be used to provide probabilistic estimates of future vehicle performance. In order to develop suitable assumptions for the forward looking fleet level analysis incorporating new vehicle technologies, it is necessary to forecast the future. However, most forecasts are extrapolations of the current status quo and trends, which assume an undisturbed continuation of historical and recent developments. In order to enable exploration of this assumption space, the two fleet workshops focused on the development of a standard methodology for capturing potential future states of technology and fleet development. The fleet impact, or the combined impact of multiple aircraft of varying technology levels flying within a given year, is defined broadly though demand and retirement assumptions. Demand is driven by external factors such as Gross Domestic Product (GDP) growth and ticket price. Retirement rates are also dependent on certain external factors. The first fleet workshop defined the external factors that are most important to fleet and technology evolution. A second workshop was held to define quantitative inputs for the defined factors. The outcome is a table of recommended scenarios for use in fleet analysis. The scenarios capture the most pessimistic and optimistic assumptions on technology availability, demand growth, and retirement assumptions. When run as a suite of scenarios, they provide a wide view on the potential future state of noise, fuel burn, and emissions of the fleet. Georgia Tech and Purdue exercised their respective fleet analysis tools (GREAT and FLEET) applying the technology and fleet scenarios defined through the community workshops. The results show that fleet direct carbon dioxide (CO2) emissions are unlikely to remain at or below 2005 levels without the addition of significant alternative fuels to offset direct emissions. Given the current state of new technology aircraft and projected introduction rates, it will take until 2030 for technology to have significant impact on direct CO2 emissions due to the time it takes to turnover existing aircraft. It was also found that the rate of technology maturation and introduction (i.e., new aircraft) is more important to reducing direct fleet CO2 emissions than reducing demand. Lowered demand certainly helps in reducing fleet CO2, but inserting technology as soon as possible has the largest impact on achieving carbon-neutral growth and CO2 emission levels below 2005 by 2050. Both the GREAT and FLEET tools predicted similar impacts for CO2 emissions. There was more variation between tools in the predicted noise levels of the future fleet, both Purdue and Georgia Tech found significant reductions in noise contour area are possible under a wider range of demand and technology assumptions. However, as is the case with fuel burn, the rate of new technology introduction is the primary driver of reducing noise since noise is a non-linear phenomenon and older, louder aircraft will dominate until retired. The outcome of this study is intended to provide a glimpse into the future potential states of aviation, but also to provide future researchers with a standard set of assumptions which can be reevaluated and applied in a consistent manner in future years. ii Table of Contents 1 University Participants ................................................................................................................................................................. 1 2 Project Funding Level .................................................................................................................................................................. 2 3 Investigation Team ...................................................................................................................................................................... 3 4 Project Overview .......................................................................................................................................................................... 4 4.1 Major Accomplishments ....................................................................................................................................................... 5 4.1.1 Fleet Level Workshop Assumption Setting (Section 5.3.2) ........................................................................................... 5 4.1.2 Technology Level Workshop Assumption Setting (Section 5.3.3) ................................................................................ 5 4.1.3 Evaluation of Impact of Demand and Technology on Future Fleet CO2 and Noise (Section 7) ................................... 5 4.1.4 Demonstration of FLEET (Section 7) .............................................................................................................................. 5 4.1.5 Vehicle-Level Assessment of Mission Specification Changes (Section 6.2.8) .............................................................. 5 5 Developing Technology and Fleet Evolution Scenarios .............................................................................................................. 6 5.1 Objective(s) ............................................................................................................................................................................ 6 5.2 Summary of Resulting Technology Evolution Scenarios ..................................................................................................... 6 5.3 Research Approach ............................................................................................................................................................... 6 5.3.1 Research Approach Overview......................................................................................................................................... 6 5.3.2 Fleet Workshops ............................................................................................................................................................. 6 5.3.3 Technology Roadmapping Workshops Overview ........................................................................................................ 29 6 Vehicle Modeling ......................................................................................................................................................................
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