Paola Zanella

(Advisor: Prof. Mavris, Co-advisor: Prof. Prasad)

will defend a doctoral thesis entitled,

A Methodology to Improve the Proactive Mitigation of Helicopter Accidents Related to Loss of Tail Rotor Effectiveness

On

Tuesday, July 6 at 11:00 a.m. EDT

https://bluejeans.com/577531287/5729

Abstract
Rotorcraft are a unique and valuable component within our modern aviation system. Their ability to hover and take-off and land vertically, along with their good low-speed handling qualities gives them great versatility. Because of these characteristics, rotorcraft are often operated in environments that are prone to accidents. However, reducing the accident rate of rotorcraft continues to remain a challenge. The percentage of loss of control accidents has almost doubled since 1964, with a majority of these accidents related to loss of directional control. In particular, Loss of Tail rotor Effectiveness (LTE) has been found to be a leading problem within all loss of directional control accidents for small-size helicopters flying in high-risk operational conditions.

Both the Federal Aviation Administration and the National Transportation Safety Board have recognized LTE to be a major contributing factor in several accidents where pilots lost control. However, even if multiple helicopter accident investigations have been attributed to LTE, it has been noticed that within the aviation community there is a lack of understanding and a variety of opinions about this phenomenon. Therefore, pilots are often not adequately trained to anticipate and mitigate an LTE event. Furthermore, the large number of factors that influence LTE increase the difficulty for the pilots to predict all the dangerous scenarios while flying. One significant method to specifically address those gaps and promote rotorcraft safety involves the proactive mitigation of LTE via the analysis of flight data within the Helicopter Flight Data Monitoring (HFDM) program. Through this program, the pilots receive constant flight evaluation reports, to increase their awareness of the proximity to LTE and educate them on conducting improved LTE risk evaluations. The main method of flight data analysis is the detection of safety metrics. This method compares flight data to a large safety metric database, which includes predefined hazardous flight conditions and different levels of proximity to events. Nevertheless, if the safety metric is not well defined, it may lead to false or missed detections degrading the quality of the overall safety analysis. Unfortunately, a sufficiently reliable LTE safety metric still does not exist, hindering the efficacy of the LTE detection within flight data.

The objective of this thesis is to formulate a methodology to improve the detection capability of the proximity to LTE within the HFDM program. This enables a more accurate prediction of the proximity to LTE to promote awareness of this critical helicopter safety threat within the rotorcraft community and support the proactive mitigation of helicopter accidents related to LTE. To enhance the understanding of the nature of the LTE flight characteristics, a physics-based investigation is performed. A more comprehensive LTE definition is proposed including three different aspects that can lead to LTE behavior, i.e., loss of weathercock stability, running out of pedal (tail rotor collective) for trim, and tail rotor vortex ring state. The modeling of the flight dynamics of each phenomenon is individually analyzed to ensure an accurate physics-based representation of LTE. Further, the parameters that support the detection of LTE are found to enable the recognition and classification of each LTE phenomenon in simulation results. To provide the operator with an improved LTE safety metric designed to analyze flight data and easily detect the proximity to LTE without the need for a simulation model, an alternative approach is used. Specifically, a physics-based investigation of the aircraft flight envelope is combined with the application of supervised learning techniques to develop the predictive models of the LTE flight characteristics. The LTE safety metric is formed by the collection of the predictive models obtained, enabling the fast and reliable identification of the proximity to LTE within the HFDM program.

Committee

• Professor Dimitri N. Mavris – School of Aerospace Engineering, Georgia Institute of Technology
• Professor J. V. R. Prasad – School of Aerospace Engineering, Georgia Institute of Technology
• Professor Daniel P. Schrage – School of Aerospace Engineering, Georgia Institute of Technology
• Professor Lakshmi N. Sankar – School of Aerospace Engineering, Georgia Institute of Technology
• Mr. Charles C. Johnson – Aviation Research Division, Federal Aviation Administration