Date of Award
Summer 7-10-2023
Access Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Hever Moncayo
Committee Advisor
Hever Moncayo
First Committee Member
Richard Prazenica
Second Committee Member
Richard Stansbury
College Dean
James Gregory
Abstract
Flight test data is a valuable resource used in many aerospace applications. However, procuring a sufficiently large database of flight test data poses several challenges. Nominal flight tests can be expensive and time-consuming and require much post-processing depending on the availability of sensors and the quality of the sensor output. Flight test performed outside of nominal flight conditions, or flight tests in which failures are introduced, add to the inherent risk and danger associated with flight tests. The most popular alternative to flight test, numerical simulations, may fail to fully capture all non-linear behavior. While flight tests will always be required, a method for augmenting an existing database of flight test data with synthetically generated data could help alleviate some of the aforementioned challenges. Over the past few decades, generative machine learning has a emerged as a popular tool for data augmentation. In this thesis, several neural network architectures were investigated as methods of generating synthetic flight data that is consistent with the aircraft dynamics.
Scholarly Commons Citation
Sisson, Nathaniel, "Neural Network Models for Generating Synthetic Flight Data" (2023). Doctoral Dissertations and Master's Theses. 762.
https://commons.erau.edu/edt/762
