Ph.D. Proposal

Jamey Ackley

(Advisor: Prof. Dimitri Mavris)

“An Approach to Robust Turbofan Engine Fleet Lifecycle Management”

On

Tuesday, December 10 

1:00 p.m.
Weber Space and Technology Building (SST II),

Collaborative Visualization Environment (CoVE)

Abstract
The commercial airline industry is a critical transportation sector that has experienced tremendous growth over the last five decades. Despite this growth, the enactment of the Deregulation Act of 1978 forced more competitive pricing, driving down fares and making profitability a challenge. Balancing revenue with costs is more critical than ever. The field of revenue management is quite mature, having undergone extensive research for many decades. However, opportunities to significantly reduce costs remain, particularly in engine fleet management.

Robust engine fleet management is a multi-disciplinary problem requiring expertise in propulsion engineering, performance engineering, reliability theory, operations research, and logistics. Due to the complexity of the field, it is common practice to manage the engine fleet in disciplinary silos using short-term objectives. This approach often results in multiple conflicting objectives that increase fleet management costs and make system-level uncertainty quantification and mitigation challenging. Black-box commercial solutions that address some of the interdisciplinary challenges are available. Still, the proprietary nature of these products makes it difficult to understand their logic and measure the quality of their performance. Researchers have addressed specific, independent aspects of fleet management, including material optimization, spare engine management, and opportunistic LLP replacement. However, a system-level framework jointly considering all disciplines and principal sources of uncertainty has yet to be identified.

The methodology proposed in this thesis includes a framework for comprehensive lifecycle maintenance optimization, enhanced material forecasting, and uncertainty quantification and mitigation. The lifecycle optimization approach jointly evaluates essential maintenance drivers such as regulatory requirements, engine performance, and engine reliability over the lifespan of each engine in the fleet. Optimizer-generated maintenance plans produce the features necessary for advanced analytics, enabling predictive modeling that improves the fidelity of material forecasting models. Finally, surrogate modeling techniques correlate fleet control factors to key performance metrics while quantifying the effects of the principal sources of system uncertainty. This framework provides operators with the tools necessary for robust parameter design, reducing system performance variation and minimizing fleet lifecycle cost.

Committee

• Prof. Dimitri Mavris – School of Aerospace Engineering (advisor)
• Prof. Graeme Kennedy – School of Aerospace Engineering
• Prof. Daniel Schrage – School of Mechanical Engineering
• John Laughter – COO, Delta Air Lines
• Dr. Alexander Karl – Fellow, Rolls-Royce