Gabriel Achour
(Advisor: Prof. Dimitri Mavris]

will defend a doctoral thesis entitled,

A Multi-Objective Deep Learning Methodology for Morphing Wings

On

Friday, April 5 at 12:00 p.m.
Collaborative Visualization Environment (CoVE)
Weber Space Science and Technology Building (SST II)

And

Teams

Abstract
Conventional aircraft are currently unable to achieve maximum aerodynamic performance when operating under varying missions and weather conditions due to design constrains. One of these constraints is the traditional approach of optimizing aircraft wings to achieve the best average aerodynamic performance for a specific mission while maintaining structural integrity. Previous studies have however shown that changing the shape of wings at different points of a mission profile improves the aerodynamic performance of aircraft. As such, efforts have been made by stakeholders to explore the viability and feasibility of changing or morphing the shape of aircraft wings to enable aircraft adapt to varying missions and/or weather conditions. However, as with any other aspect of aircraft design, there are challenges that currently exist which hinder the development of conventional aircraft with morphing wings.

First, the computational cost of flow solvers makes aerodynamic shape optimization time consuming and computationally expensive due to its iterative nature. When designing a morphing wing, different configurations are computed for different points in the flight envelope. This multiplies the computational cost necessary for morphing wing aircraft design. Consequently, a framework capable of performing shape optimization at a reduced computational cost is needed.

Second, morphing can lead to high variation of wing shapes, which can generate high aerodynamic loads and minimize the aerodynamic benefits of morphing wings. Moreover, structural analysis is also computationally expensive replicating the same challenges as for aerodynamic optimization. As such, a multi-objective framework capable of optimizing morphing wings to increase aerodynamic efficiency while addressing aeroelastic constraints, at a lower computational cost is needed.

Finally, even though changing the shape of an aircraft’s wing at each segment of a mission profile is the most efficient approach to maximize the benefits of morphing wings, this is not ideal as flight and weather conditions are not constant throughout the flight segment. As such, a framework that can adapt the wing shapes to varying flow conditions during the flight is needed.

Consequently, this thesis aims to address these gaps by 1) developing a Conditional Generative Adversarial Network-based algorithm capable of generating optimal wing shapes of a morphing wing vehicle for each segment of a given mission profile, 2) training a Reinforcement Learning agent to modify the optimized shape and design the wing structure to ensure the structural integrity of morphing wings throughout the flight while maintaining a high aerodynamic performance 3) implementing a Meta Reinforcement Learning agent to make aircraft wings adapt their shapes to variations in flow conditions during each mission segment.

Committee

·         Dr. Dimitri Mavris – School of Aerospace Engineering (advisor)

·         Dr. Graeme Kennedy – School of Aerospace Engineering

·         Dr. Daniel Schrage – School of Aerospace Engineering

·         Dr. Woong Je Sung – School of Aerospace Engineering

·         Mr. Dino Roman – Chief Aerodynamicist, Advanced Concepts, Boeing Commercial Aircraft