• State-based Modeling of General Aviation Fixed-wing Accidents

With all the abundant accident data available, how can we analyze it to understand accident causation, and how a seemingly normal flight transitioned into an accident?

We developed a state-based modeling framework that maximizes data extraction and insight formation from the NTSB accident reports by (1) developing a structured modeling language to represent fixed-wing aircraft accident causation in the form of states and triggers. The model provides a comprehensive understanding of what factors trigger a flight to different hazardous states, and then finally to an accident. This research was conducted at Purdue University and has been published in the AVIATION journal.

• General Aviation Instructional Accident Analysis

The National Transportation Safety Board (NTSB) investigates U.S. civil aviation accidents, including General Aviation. General Aviation (GA) is a category of aircraft operations, exclusive of commercial airline or military operations. GA includes activities like private and recreational flying, flight training, and business aviation. While student pilots dedicate extensive hours to honing their airmanship skills and reducing the likelihood of accidents, nearly 50% of all GA accidents take place during flight training (source: NTSB). Using the NTSB accident reports, our lab is analyzing how these instructional accidents happen and what causes them.

• General Aviation Fixed-wing Stall and Spin Accidents

A stall is the decrease in lift due to the smooth airflow over a wing becoming more turbulent and spin is a descent in a helical path due to an aggravated stall. Stall and spin accidents are the third most common causes of fatal General Aviation accidents, accounting for 7.28% of such accidents in 2013–2022 (source: NTSB). At AYST lab, we are exploring stall and spin accident causation using historical accident data analysis and machine learning methods.

• Unmanned Air Vehicle Safety

Unmanned Aerial Vehicles (UAVs), or drones, are rapidly integrating into various industries. However, safety concerns regarding their design, training, and operation are rising. We are analyzing the status of UAV safety reporting in the U.S. and using publicly available safety reports to identify trends and contributing factors in reported incidents, ultimately informing safety decisions and highlighting potential limitations in the current reporting system.

• Using Natural Language Processing to Analyze Aviation Accident Reports

The NTSB accident reports provide an abundance of information in the form of narratives. Unlike commercial aviation, General Aviation accident reports are less detailed and short. Not all the information written in the narratives necessarily gets translated to the findings. Because most previous studies use only the NTSB codes to analyze accident causation, the incomplete causation in the form of codes leads to partial information extraction and therefore incomplete understanding of accident causation. Using all the available information in reports including narratives may help provide a better understanding of accident causation. We are exploring different neural network models to extract information from thousands of accident narratives.

• Assessing Factors Affecting the Implementation of Advanced Air Mobility

Advanced Air Mobility (AAM) is a next generation of air transportation for efficient and sustainable urban air mobility solutions, often using electric aircraft. With AAM poised to revolutionize urban mobility, it is imperative to understand public attitudes toward this transformative technology. This research uses a methodological approach to investigating public acceptance of AAM, which is crucial for integrating emerging aviation technologies into the national airspace. We aim to develop a set of design parameters, considering human factors, for a successful AAM implementation.

• Assessing Pilots’ Energy Management using Flight Simulator

General Aviation has many risk factors that are not well studied, evaluated, or understood; one of these key issues is energy management within airplanes. Energy management refers to the process of planning, monitoring, and controlling altitude and airspeed targets in relation to the airplane’s energy. We plan to assess pilots’ and student pilots’ ability to control the aircraft in terms of energy management.

• Surveying Pilots with Loss of Control Experiences

Inflight loss of control (LOC-I) involves an unintended departure of an aircraft from a controlled flight while in the air, i.e., pilot is unable to control the aircraft. Since GA accident reports include limited detail on human factors, we conducted surveys with pilots who had experience some kind of loss of control to understand the role of human factors in LOC-I. This research was conducted at Purdue University and is currently under submission process for a journal publication.

• Interview of Pilots with Loss of Control Experiences

To gain a deeper understanding of loss of control causation, we conducted a follow-up interview with pilots about their loss of control experiences, recovery methods, and training. We also included certified flight instructors to understand more about loss of control training. The findings from the study may help improve training methods and operating procedures for General Aviation pilots. This research was conducted at Purdue University and is currently under submission process for a journal publication.