Session: 02-04-01: Computer Methods and Reduced Order Modeling

Paper Number: 152198

152198 - Configurable Parametric State-Consistent Aeroservoelastic-Gust Reduced-Order Models 

High-fidelity numerical analysis, such as computational structural dynamics (CSD) and computational fluid dynamics (CFD), is essential for analysis and design of modern, high-performance aerostructural systems. These simulations can be computationally costly, leading to long development cycles. Reduced-order models (ROMs) can be employed to significantly accelerate analysis while maintaining the desired accuracy. However, there are two prominent challenges in constructing parametric, data-driven ROMs for aeroservoelastic-gust (ASEG) systems in the state-space form: (1) how to retain state or model consistence at different flight conditions − inconsistent ROMs could lead to physically meaningless ROM interpolation and failure to accommodate the entire parameter space; and (2) how to effectively model multi-domain physics to accommodate various usage scenarios.

We present a new system identification and hierarchical data-driven ROM framework to specifically combat these two challenges. First, by leveraging the principle of superposition, a complex ASEG system is broken down into a set of constituent models. Each constituent model captures physics in a single domain, such as aerodynamics, control surfaces, gust load, and structural dynamics, representing a significant level of complexity reduction for aerostructural ROM development. A consistence enforcement (SCE) approach, which modifies the traditional AutoRegressive eXogenous (ARX) formulation to penalize inconsistence between ROMs at different flight parameters, is used to build the constituent models. The resulting SCE approach enables salient model interpolation capabilities across the entire flight parameter space. A genetic algorithm (GA) is used to find optimal hyperparameters to automatically construct constituent ROMs that balance between ROM consistence and accuracy. Once developed, the constituent ROMs can be integrated in a configurable (“building block-like”) manner to handle various physics domains and different flight conditions without need for model regeneration.

This new ROM method is used to construct parameter ASEG ROM databases. Constituent ROMs are developed to account for their individual contributions to generalized aerodynamic force (GAF) and generalized displacement in broad Mach regimes (0.5 – 0.95), which are then coupled and configured in various manners to enable efficient and accurate AE, ASE, AEG, and ASEG analysis. All parametric ROMs are compared with high-fidelity FUN3D data, and the results demonstrate that the proposed approach significantly improves ASEG ROM consistence and accuracy (relative error < 4%) at varying flight conditions, and most importantly, boosts ROM reusability for different aerostructural application scenarios.

Presenting Author: Yi Wang University of South Carolina

Presenting Author Biography: Dr. Yi Wang is a Professor in mechanical engineering at the University of South Carolina (USC). He obtained his Ph.D. at Carnegie Mellon University in 2005 and his B.S. and M.S. from Shanghai Jiaotong University in China in 1998 and 2000, respectively. From 2005 to 2017, he held several positions of increasing responsibility at the CFD Research Corporation (CFDRC), Huntsville, Alabama. In 2017, he joined the University of South Carolina to start his academic career. His research interests focus on computational and data-enabled science and engineering (CDS&E), including reduced order modeling, large-scale and/or real-time data analytics, system-level simulation, computer vision, and cyberphysical system and autonomy with applications in aerospace, unmanned systems, manufacturing, and biomedical devices. He has been a PI or Co-PI of 70 external projects sponsored by DoD, NASA, DOT, NIH, MDA, FRA, and industries. He has published over 160 papers in books, peer-reviewed journals, and conference proceedings and held five US patents. He is also the recipient of the 2021 Research Breakthrough Star Award of USC, and the recipient of the 2023 Research Progress Award of the College of Engineering and Computing, USC.