HyunKi Lee

(Advisor: Prof. Mavris)

will defend a doctoral thesis entitled,

RUNWAY SAFETY IMPROVEMENTS THROUGH A DATA DRIVEN

APPROACH FOR RISK FLIGHT PREDICTION AND SIMULATION

On

Tuesday, November 8 at 11:00 a.m.

Weber SST Bldg., room #304

And

Join Zoom Meeting: https://gatech.zoom.us/j/7564555254?pwd=b1JRa0VYL2VqanlGMVUzMlp4OVhJZz09

Meeting ID: 7564555254

Passcode: aL9CNe

Abstract

Runway overrun is one of the most frequently occurring flight accident types threatening the safety of flights. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.

To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.

A series of experiments are performed to evaluate the robustness and performance of these models to adverse wind and flare. The most robust models are then used to identify significant features for prediction and the control space for the simulation. The outcomes of the most robust models also translate over to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.

Committee

• Prof. Dimitri N. Mavris – School of Aerospace Engineering (advisor)
• Prof. Daniel P. Schrage – School of Aerospace Engineering
• Dr. Alexia P. Payan – School of Aerospace Engineering
• Dr. Tejas G. Puranik – School of Aerospace Engineering
• Dr. Angela Campbell – Federal Aviation Administration
• Dr. Youngjun Choi – United Parcel Service (UPS), Inc.