A methodology to enable rapid evaluation of aviation environmental metrics and aircraft technologies
Becker, Keith Frederick
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Commercial aviation has become an integral part of modern society and enables unprecedented global connectivity by increasing rapid business, cultural, and personal connectivity. In the decades following World War II, passenger travel through commercial aviation quickly grew at a rate of roughly 8% per year globally. The FAA's most recent Terminal Area Forecast predicts growth to continue at a rate of 2.5% domestically, and the market outlooks produced by Airbus and Boeing generally predict growth to continue at a rate of 5% per year globally over the next several decades, which translates into a need for up to 30,000 new aircraft produced by 2025. With such large numbers of new aircraft potentially entering service, any negative consequences of commercial aviation must undergo examination and mitigation by governing bodies so that growth may still be achieved. Options to simultaneously grow while reducing environmental impact include evolution of the commercial fleet through changes in operations, aircraft mix, and technology adoption. Methods to rapidly evaluate fleet environmental metrics are needed to enable decision makers to quickly compare the impact of different scenarios and weigh the impact of multiple policy options. As the fleet evolves, interdependencies may emerge in the form of tradeoffs between improvements in different environmental metrics as new technologies are brought into service. In order to include the impacts of these interdependencies on fleet evolution, physics-based modeling is required at the appropriate level of fidelity. Evaluation of environmental metrics in a physics-based manner can be done at the individual aircraft level, but will then not capture aggregate fleet metrics. Contrastingly, evaluation of environmental metrics at the fleet level is already being done for aircraft in the commercial fleet, but current tools and approaches require enhancement because they currently capture technology implementation through post-processing, which does not capture physical interdependencies that may arise at the aircraft-level. The goal of the work that has been conducted here was the development of a methodology to develop surrogate fleet approaches that leverage the capability of physics-based aircraft models and the development of connectivity to fleet-level analysis tools to enable rapid evaluation of fuel burn and emissions metrics. Instead of requiring development of an individual physics-based model for each vehicle in the fleet, the surrogate fleet approaches seek to reduce the number of such models needed while still accurately capturing performance of the fleet. By reducing the number of models, both development time and execution time to generate fleet-level results may also be reduced. The initial steps leading to surrogate fleet formulation were a characterization of the commercial fleet into groups based on capability followed by the selection of a reference vehicle model and a reference set of operations for each group. Next, three potential surrogate fleet approaches were formulated. These approaches include the parametric correction factor approach, in which the results of a reference vehicle model are corrected to match the aggregate results of each group; the average replacement approach, in which a new vehicle model is developed to generate aggregate results of each group, and the best-in-class replacement approach, in which results for a reference vehicle are simply substituted for the entire group. Once candidate surrogate fleet approaches were developed, they were each applied to and evaluated over the set of reference operations. Then each approach was evaluated for their ability to model variations in operations. Finally, the ability of each surrogate fleet approach to capture implementation of different technology suites along with corresponding interdependencies between fuel burn and emissions was evaluated using the concept of a virtual fleet to simulate the technology response of multiple aircraft families. The results of experimentation led to a down selection to the best approach to use to rapidly characterize the performance of the commercial fleet for accurately in the context of acceptability of current fleet evaluation methods. The parametric correction factor and average replacement approaches were shown to be successful in capturing reference fleet results as well as fleet performance with variations in operations. The best-in-class replacement approach was shown to be unacceptable as a model for the larger fleet in each of the scenarios tested. Finally, the average replacement approach was the only one that was successful in capturing the impact of technologies on a larger fleet. These results are meaningful because they show that it is possible to calculate the fuel burn and emissions of a larger fleet with a reduced number of physics-based models within acceptable bounds of accuracy. At the same time, the physics-based modeling also provides the ability to evaluate the impact of technologies on fleet-level fuel burn and emissions metrics. The value of such a capability is that multiple future fleet scenarios involving changes in both aircraft operations and technology levels may now be rapidly evaluated to inform and equip policy makers of the implications of impacts of changes on fleet-level metrics.