(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, 2019-12)
Puranik, Tejas G.; Mavris, Dimitri N.
Quantification and improvement of safety is one of the most important objectives among the General Aviation community. In recent years, machine learning techniques have emerged as an important enabler in the data-driven safety enhancement of aviation operations with a number of techniques being applied to flight data to identify and isolate anomalous (and potentially unsafe) operations. Energy-based metrics provide measurable indications of the energy state of the aircraft and can be viewed as an objective currency to evaluate various safety-critical conditions across a heterogeneous fleet of aircraft and operations. In this paper, a novel method of identifying instantaneous anomalies for retrospective safety analysis in General Aviation using energy-based metrics is proposed. Each flight data record is processed by a sliding window across the multi-variate time series of evaluated metrics. A Gaussian Mixture Model using energy metrics and their variability within each window is fit in order to predict the probability of any instant during the flight being nominal. Instances during flights that deviate from the nominal are isolated to identify potential increased levels of risk. The identified anomalies are compared with traditional methods of safety assessment such as exceedance detection to highlight the benefits of the developed method. The methodology is demonstrated using flight data records from two representative aircraft for critical phases of flight.