Title: Intelligent Data-Driven Aerodynamics Analysis and Optimization of Morphing Configurations

Date: Wednesday, November 29th, 2023

Time: 10:00 AM - 11:00 AM EST

Location: Montgomery Knight Building 317 - AE Department  (Physical)    

                  Microsoft Teams Meeting  (Virtual)

                  Meeting ID: 269 631 497 480 

                  Passcode: Pce4wA

Jose Magalhaes

Robotics PhD Candidate

School of Aerospace Engineering

Georgia Institute of Technology

Committee:

Dr. Kyriakos Vamvoudakis (Advisor) - School of Aerospace Engineering, Georgia Institute of Technology

Dr. Seth Hutchinson - School of Interactive Computing, Georgia Institute of Technology

Dr. Daniel P. Schrage - School of Aerospace Engineering, Georgia Institute of Technology

Dr. Lakshimi N. Sankar - School of Aerospace Engineering, Georgia Institute of Technology

Dr. Gustavo L. O. Halila - Technology Development – EMBRAER S.A - Brazil

Abstract:

The aeronautical industry is continuously looking for more efficient aircraft and provide a reduction on fuel or power consumption while guaranteeing safety, optimality, and stability. The advances of composite materials enable building morphing structures that adapt to a variety of flight and environmental conditions. Airplanes that use morphing technologies can achieve optimal performance and minimize the drag over the entire flight envelope and operate even in dangerous weather conditions.

In this dissertation, we propose a data-driven framework to control morphing airfoils in the subsonic flight regime, considering high Reynolds numbers to reach, in efficient and safe way, a shape with improved values of the aerodynamic coefficients. The online solution is based on a data-driven controller combined with a surrogate model and a multi-gradient descent algorithm considering objective functions that are relevant in aerodynamics: increase lift-drag ratio, reduce drag and increase lift. Without full knowledge of the aerodynamic parameters (lift, drag, and pitching moment coefficients), the learning framework searches for an airfoil shape that minimizes a metric of performance associated to drag, lift, and pitching moment coefficients. The solution uses online data to improve the accuracy of the predictions of the aerodynamic coefficients provided by the surrogate model along the trajectory. The optimization framework focuses on subtle airfoil deformations to assure a smooth trajectory between the initial and the final shape. Finally, the efficacy and the robustness of our proposed solution is shown in numerical examples, resulting in a significant reduction in the prediction error.