A Hybrid Bayesian Risk Assessment Framework using Fault Trees and Regression Analysis to Predict Thermal Runaway Aviation

This praxis will quantify the cause-effect relationships driving thermal runaway incidents in the aviation industry. Additionally, it aims to increase the effectiveness of mitigation strategies for these escalating events. Despite ongoing efforts, mitigation strategies have proven inadequate as incident trends continue to increase. To fully address these incidents, new efforts need to be instituted to consolidate available probabilistic modeling tools in new ways to address the real operational root causes. This praxis is dedicated to developing a Hybrid Bayesian Risk Assessment Framework to do just that and improve the prediction and mitigation capabilities of Lithium Thermal Runaway incidents. This research will focus on the following three key areas to accomplish these objectives. First, to identify and quantify the external-to-device causal factors that influence the probability of lithium-ion battery thermal runaway incidents during terminal-to-terminal operations in U.S. commercial aviation. Secondly, it evaluates the external-to-device statistical relationships between incident-level variables and the likelihood of severe or fatal outcomes during terminal-to-terminal operations. Lastly and most importantly, to demonstrate that Bayesian Inference integrated with both Fault Tree Analysis and Logistic Regression together in a Hybrid Risk Assessment Framework will provide statistically significant improved performance. This is in comparison to the individual stand-alone methods themselves. The outcomes of this research provide quantifiable insights to help key aviation stakeholders rethink their strategic efforts associated with lithium-ion (Li-ion) thermal runaway risk. The research efforts and findings of this praxis did validate the framework’s ability to provides a statistically significant approach to aviation battery risk assessment compared to current standalone practices. Based on the results, key policy and procedural recommendations for improvement practices along with other needed supportive efforts are also provided.

Author

• Chandler, Richard Aaron

Language

• en

Keyword

• Hybrid Risk Assessment
• Li-ion Battery
• Risk Assessment Framework
• Thermal Runaway

Date created

• 2026

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Belesis, Konstantinos

Committee Member(s)

• Emmons, Deborah W
• Blackford, Joseph P

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/9z903109c