(Georgia Institute of Technology, 2020-03)
Puranik, Tejas G.; Harrison, Evan D.; Chakraborty, Imon; Mavris, Dimitri N.
Performance models facilitate a wide range of safety analyses in aviation. In an ideal scenario, the performance models would show inherently good agreement with the true performance of the aircraft. However, in reality, this is rarely the case: either owing to underlying simplifications or due to the limited fidelity of applicable tools or data. In such cases, calibration is required to fine-tune the behavior of the performance models. For point-mass steady-state performance models, challenges arise due to the fact that there is no obvious, unique metric or flight condition at which to assess the accuracy of the model predictions, as well as because a large number of model parameters may potentially influence model accuracy. This work presents a two-level approach to aircraft performance model calibration. The first level consists of using manufacturer-developed performance manuals for calibration, whereas the second level provides additional refinement when flight data are available. The performance models considered in this work consist of aerodynamic and propulsion models (performance curves) that are capable of predicting the non-dimensional lift, drag, thrust, and torque at any given point in time. The framework is demonstrated on two representative general aviation aircraft. The demonstrated approach results in models that can predict critical energy-based safety metrics with improved accuracy for use in retrospective safety analyses.
(Georgia Institute of Technology, 2018-06)
Rajaram, Dushhyanth; Yu, Cai; Chakraborty, Imon; Mavris, Dimitri N.
The aerospace industry’s current trend towards novel or More Electric architectures results in some unique challenges for designers due to both the scarcity or absence of historical data and a potentially large combinatorial space of possible architectures. These add to the already existing challenges of attempting to optimize an aircraft design in the presence of multiple possible objective functions while avoiding an overly compartmentalized approach. This paper uses the Integrated Subsystem Sizing and Architecture Assessment Capability to pursue a multi-objective optimization for a large twin-aisle aircraft and a small single-aisle aircraft using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm with parallel function evaluations. One novelty of the optimization setup is that it explicitly considers the impacts of subsystem architectures in addition to those of traditional aircraft-level design variables. The optimization yields generations of nondominated designs in which substantially electrified subsystem architectures are found to predominate. As a first assessment of the impact of epistemic uncertainty on the results obtained, the optimization is rerun with altered sensitivities for the thrust-specific fuel consumption penalties due to shaft-power and bleed air extraction. This analysis demonstrated that the composition of architectures on the Pareto frontier is sensitive to the secondary power extraction penalties, but more so for the small single-aisle aircraft than the large twin-aisle aircraft.