Ph.D. Thesis Defense

By

Tejas Puranik

(Advisor: Prof. Dimitri N. Mavris)

10:00 AM, Friday, March 30, 2018

Weber Space Science and Technology Building

Collaborative Visualization Environment (CoVE)

A METHODOLOGY FOR QUANTITATIVE DATA-DRIVEN SAFETY ASSESSMENT FOR GENERAL AVIATION

ABSTRACT:

The safety record of aviation operations has been steadily improving for the past few decades, however, accident rates in General Aviation (GA) have not improved significantly compared to scheduled commercial airline operations. According to the Federal Aviation Administration (FAA), the demand for air travel and traffic is predicted to grow steadily for the next two decades at a rate of approximately 1.8% annually with GA set to receive a much-needed revitalization. However, safety remains a major hurdle and with such a large increase in expected operations, there is an ever-increasing demand for improving safety of GA operations.

Various data-driven safety programs such as Flight Data Monitoring (FDM) that exist in commercial aviation domain have percolated in GA with the aim of improving safety. These programs typically feature a continuous cycle involving data collection from on-board recorders, retrospective analysis of flight data records, identification of operational safety exceedances, design and implementation of corrective measures, and monitoring to assess their effectiveness. While these programs have been shown to be effective in reducing accident rates, there are certain obstacles in their widespread implementation in the GA domain. The variability in recorded parameters in GA flight data recorders (FDR), heterogeneity in GA fleet, different missions flown, etc. are some of the important hurdles. Additionally, existing techniques of analysis such as exceedance detection are designed to identify known unsafe conditions but are potentially blind to safety-critical conditions that may be captured in flight data records but are not present in the set of predefined safety events. With the availability of recorded data in the GA domain, there is an opportunity to improve safety through the use of more quantitative techniques.

The overarching objective of this dissertation is to develop a methodology that can provide objective metrics for quantifying GA flight safety, enable automatic identification of anomalous operations, and provide predictive capabilities that will complement existing approaches. The use of recorded flight data from GA operations is central to developing algorithms that are robust and applicable to the heterogeneous GA domain. The first objective of this dissertation is to obtain objective metrics for quantifying flight safety in GA operations. The development of metrics is pursued within the constraints imposed by GA FDR and through the examination of past criteria and historical data for identifying important parameters available for defining metrics. The dissertation presents the use of energy-based metrics as objective currency that can be used for quantifying flight safety across the heterogeneous GA fleet. These metrics satisfy some of the important criteria that are desired in metrics - parsimony, safety-relevance, and generalizability. The second objective of the dissertation is enabling automatic identification of anomalous operations. In order to facilitate this, an anomaly detection framework is developed using the defined safety metrics for identifying different types of anomalies (flight-level and instantaneous) in GA operations. The same general framework is adapted to identify both types of anomalous operations and understand their relationship with each other. The third objective of the dissertation is to provide predictive capabilities to improve the quality of the safety assessment task. To that end, models of aerodynamic and propulsion performance are utilized for obtaining unrecorded quantities of interest. A novel technique of calibrating these aircraft performance models starting from a generic GA model is developed. Different options for calibration depending on the type of calibration data available are proposed and tested to be applicable in multiple scenarios. 

Committee members:

1.       Prof. Dimitri Mavris (Advisor)

School of Aerospace Engineering, Georgia Institute of Technology

2.       Dr. Simon Briceno 

School of Aerospace Engineering, Georgia Institute of Technology

3.       Prof. Karen Marais

School of Aeronautics and Astronautics, Purdue University

4.       Dr. Hossein Eghbali 

Federal Aviation Administration

5.       Prof. Daniel Schrage

School of Aerospace Engineering, Georgia Institute of Technology