10/23/2023
By Danielle Fretwell

The Francis College of Engineering, Department of Industrial Engineering, invites you to attend a Doctoral Dissertation Proposal defense by Joel Walker on "Taking Off with Machine Learning for Flight Modeling."

Candidate Name: Joel Walker
Degree: Doctoral
Defense Date: Friday, Oct. 27, 2023
Time: 4:30-5 p.m. ET
If interested in attending, please email David Claudio 

Committee:

  • Advisor David Claudio, IE Program Director, M&I Eng, UML
  • Carter Keough, Assistant Professor, Mechanical & Industrial Engineering, UML
  • Maru Cabrera, Assistant Professor, Computer & Information Sciences, UML

Brief Abstract:
Flight test for aircraft certification is a fundamental method to ensure safe aircraft and air travel worldwide. Flight test data is collected through a limited number of discrete subspace points in the flight envelope to verify and validate preselected linear model coefficients chosen before the test program. The finalized model is presented as evidence that the platform meets the certification requirements throughout the expected flight envelope. It is also utilized to develop high-fidelity full-motion simulators, the primary training method for business aviation and airline industries. System identification utilizing local linear approximations is still the dominant flight test approach, exacerbating the development of a global model when trying to capture typical non-linear flight dynamics. This makes it appealing to leverage the robust and highly evolving machine learning techniques successfully used to find physical and physics-based insights. A literature review summarizes the historical developments of flight modeling and proposes coupling machine learning methodologies with airworthiness model validation to reduce the uncertainties of current approaches while providing dynamic and real-time model development with little programmatic overhead. Machine learning will allow active capture of flight dynamics independently of the historical preselected model validation and reduce risks to the flight test crew. The limited number of studies using machine learning for flight dynamics modeling will be discussed, showing their strong potential and specific scope limitations. The constrained overlap of these two topics will also be discussed with comments and discussion toward potential areas of exploration and methods to be used in this work. Flight simulations will be leveraged to explore machine learning architectures, sub-components, and approaches as applied in new and novel ways for flight test modeling.