(Georgia Institute of Technology, 2019-07)
Cinar, Gokcin; Garcia, Elena; Mavris, Dimitri N.
Purpose – The purpose of this paper was to create a generic and flexible framework for the exploration, evaluation and side-by-side comparison of novel propulsion architectures. The intent for these evaluations was to account for varying operation strategies and to support architectural design space decisions, at the conceptual design stages, rather than single-point design solutions.
Design/methodology/approach – To this end, main propulsion subsystems were categorized into energy, power and thrust sources. Two types of matrices, namely, the property and interdependency matrices, were created to describe the relationships and power flows among these sources. These matrices were used to define various electrified propulsion architectures, including, but not limited to, turboelectric, series-parallel and distributed electric propulsion configurations.
Findings – As a case study, the matrices were used to generate and operate the distributed electric propulsion architecture of NASA’s X-57 Mod IV aircraft concept. The mission performance results were acceptably close to the data obtained from the literature. Finally, the matrices were used to simulate the changes in the operation strategy under two motor failure scenarios to demonstrate the ease of use, rapidness and automation.
Originality/value – It was seen that this new framework enables rapid and analysis-based comparisons among unconventional propulsion architectures where solutions are driven by requirements.
(Georgia Institute of Technology, 2017-12)
Cinar, Gokcin; Jimenez, Hernando; Mavris, Dimitri N.
Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditions airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7-10%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate
other such models with different data sets is encouraged.