Modeling Aircraft Wing Loads from Flight Data Using Neural Networks * †

2003-01-3025

09/08/2003

Event
World Aviation Congress & Exposition
Authors Abstract
Content
This paper documents input data conditioning, input parameter selection, structure, training, and validation of neural network models of the Active Aeroelastic Wing aircraft. Neural networks can account for uncharacterized nonlinear effects and retain generalization capability. Model inputs include aircraft rates, accelerations, and control surface positions. Linear loads models were developed for network training starting points. The models were trained with rolls, loaded reversals, windup turns, and individual control surface doublets for load excitation. Data results from all loads models at Mach 0.90 and altitude of 15,000 ft. show an average model prediction error reduction of 18.6 percent.
Meta TagsDetails
DOI
https://doi.org/10.4271/2003-01-3025
Pages
11
Citation
Allen, M., and Dibley, R., "Modeling Aircraft Wing Loads from Flight Data Using Neural Networks * †," SAE Technical Paper 2003-01-3025, 2003, https://doi.org/10.4271/2003-01-3025.
Additional Details
Publisher
Published
Sep 8, 2003
Product Code
2003-01-3025
Content Type
Technical Paper
Language
English