Neural Network Based Predictions for the Aerodynamic Performance of Flapping Wings

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Creator: 

Chamberland, Olivia Marie

Date: 

2022

Abstract: 

There is unrealized potential in using Neural Network approaches in aerospace, particularly in assisting with aircraft design. The objective of this thesis was to determine if neural networks could predict the morphology, kinematics, and performance of flapping wings. A neural network was developed in Python and trained using a small dataset of biological insect data from literature. The model was then tested for biological data (small and large insects) as well as micro-aerial vehicles. The small insect test dataset performed best likely due to similarities with the training set. A larger dataset is needed to validate the use of neural networks in flapping wing micro-aerial vehicle design. This work is ground-breaking in the field of flapping wing micro-aerial vehicle design since it provides the foundation for a quick and accurate alternative to lengthy experiments and simulations.

Subject: 

Engineering - Aerospace
Artificial Intelligence

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Aerospace

Parent Collection: 

Theses and Dissertations

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