Session: 20-17-01: Rising Stars of Mechanical Engineering

Paper Number: 173791

Reduced Order Modeling for Nonlinear Stability Analysis in Aeroelastic Systems 

The demand for increased fuel efficiency and the need to address structural damage caused by sudden loading are driving interest in new aircraft designs with lightweight and highly flexible structures. The introduction of greater flexibility, however, results in diverse nonlinear response phenomena due to complex fluid-structural interactions. This makes the system susceptible to unexpected nonlinear instabilities, emphasizing the need to incorporate nonlinear stability analysis early in the design process. Recent studies on theoretical and experimental nonlinear aeroelastic models demonstrate the importance of accounting for nonlinear effects in stability analysis to improve the prediction and control of instabilities in modern aerospace structures.

Evaluating the nonlinear stability of aeroelastic systems using high-fidelity models or experimental systems is a major challenge. In particular, performing time marching simulations for a large number of parameter values presents significant challenges due to computational requirements as well as the complexity of interpreting results for integration into subsequent analyses. These challenges are further amplified when dealing with experimental systems, where achieving maximum interpretability with minimal experiments is highly desired.

Reducing the dimensionality of the problem and performing nonlinear stability analysis in a reduced space could address these challenges. Available physics-based reduced order modeling techniques mostly rely on the availability of system equations as well as intrusive analysis of the equations. On the other hand, parametric nonlinear stability analysis demands numerous simulations at various range of parameters, limiting the development of purely data-driven reduced order modeling approaches for nonlinear analysis of the stability and dynamics in aeroelastic systems using traditional approaches.

Our research addresses this gap through the integration of analytical stability analysis techniques in nonlinear dynamics and state-of-the-art machine learning methods. This innovative integration facilitates the development of reduced-order modeling for stability analysis in nonlinear aeroelastic systems with computational efficiency and data requirements exceeding those of traditional approaches.  This approach is inspired by normal form identification techniques in the theory of dynamical systems as well as recent advances in data-driven discovery of coordinates, extended for nonlinear stability analysis in aeroelastic systems. Using trajectories measured from system dynamics, the proposed data-driven approach determines a closed form reduced order dynamics of aeroelastic systems exhibiting flutter instabilities and the nonlinear transformation to and from the physical space and the reduced coordinates.  Numerical results demonstrate the accuracy and performance of the proposed method in reduced order modeling and nonlinear stability analysis of a typical airfoil section exhibiting supercritical and subcritical flutter instabilities.

Presenting Author: Amin Ghadami University of Southern California

Presenting Author Biography: Dr. Amin Ghadami is a Research Assistant Professor of Civil and Environmental Engineering and Aerospace and Mechanical Engineering at the University of Southern California. He received his Ph.D. in Mechanical Engineering from the University of Michigan-Ann Arbor in 2019 and was a postdoctoral research fellow at the University of Michigan before joining the University of Southern California in 2023.
Dr. Ghadami’s research is at the intersection of nonlinear dynamics, data-driven analysis, and scientific computing with application in mechanical and aerospace systems and structures. Dr. Ghadami is the recipient of the 2025 AFOSR Young Investigator Program award for his research on data-driven reduced order modeling in fluid-structural systems. He was recognized as a Rising Star in Mechanical Engineering in 2020 and is the recipient of the University of Michigan Ivor K. McIvor Award for demonstrating excellence in research and scholarship in applied mechanics. Dr. Ghadami is a member of the ASME Technical Committee on Multibody Systems, Nonlinear Dynamics, and Control, as well as the ASME Technical Committee on Dynamics and Control of Systems and Structures.

Authors:

Amin Ghadami University of Southern California