Investigation of Aircraft Design Space Exploration with Machine Learning
Abstract
The goal of this work was to investigate the feasibility of implementing machine learning models for predicting the values of aircraft configuration design variables when provided with time-series of mission-informed performance parameters. Neural network models, along with its associated training data, have been generated and tested for aircraft design space exploration scenarios. The bounds of the data used to train the models was partially informed by the configuration characteristics of the Boeing 737 Next Generation family. The effects of varying neural network architecture, along with the application of different data filtering schemes, on the models’ predictive accuracy have been examined. The results obtained demonstrated that cascade-forward shallow neural network models not only exhibited excellent generalization across the design space for which the model was calibrated for, but also showcased its versatility when tasked with predicting design variable values for a configuration layout relatively different than the ones used for training. Furthermore, the models had favorable metrics in computational wall-clock time required and number of epochs needed for training.
Recommended Citation
R. S. Sharma and S. Hosder, "Investigation of Aircraft Design Space Exploration with Machine Learning," Proceedings of the AIAA Scitech 2021 Forum (2021, Nashville, TN), pp. 1 - 25, American Institute of Aeronautics and Astronautics (AIAA), Jan 2021.
Meeting Name
AIAA Scitech 2021 Forum (2021: Jan.11-15, Nashville, TN)
Department(s)
Mechanical and Aerospace Engineering
Research Center/Lab(s)
Center for High Performance Computing Research
International Standard Book Number (ISBN)
978-162410609-5
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2020 American Institute of Aeronautics and Astronautics (AIAA), All rights reserved.
Publication Date
15 Jan 2021
