New Framework for Efficient Aeroelastic Modeling Reduces Computational Load

Recent advancements in the field of fluid dynamics have been presented in a paper titled "Efficient Transonic Aeroelastic Model Reduction Using Optimized Sparse Multi-Input Polynomial Functionals" by Michael Candon, Maciej Balajewicz, Arturo Delgado-Gutierrez, Pier Marzocca, and Earl H. Dowell. This research, submitted on August 29, 2024, addresses the challenges associated with nonlinear aeroelastic reduced-order models (ROMs), which are often complex and computationally intensive to train.

The authors propose a new formulation for identifying a compact multi-input Volterra series. This approach utilizes Orthogonal Matching Pursuit to derive a set of optimally sparse nonlinear multi-input ROM coefficients from unsteady aerodynamic training data. The framework is exemplified using the Benchmark Supercritical Wing, focusing on forced response, flutter, and limit cycle oscillation.

One of the key findings of this research is the introduction of the Optimal Sparsity Multi-Input ROM (OSM-ROM) framework. This framework demonstrates high accuracy when compared to full-order aeroelastic models, achieving a 96% reduction in the number of training samples required. The implications of this work may significantly enhance the efficiency of aeroelastic modeling, making it more accessible for practical applications in engineering and aerospace industries.

For further details, the paper can be accessed at arXiv:2408.16941.