Efficient Transonic Aeroelastic Model Reduction Using Optimized Sparse Multi-Input Polynomial Functionals

TL;DR

This paper introduces an efficient nonlinear aeroelastic model reduction method using sparse multi-input polynomial functionals, significantly reducing computational complexity while maintaining high accuracy for transonic wing applications.

Contribution

It proposes a novel sparse Volterra series-based reduction framework with optimized coefficients, improving efficiency and accuracy over traditional methods.

Findings

Achieves 96% reduction in training data needed.

Maintains high accuracy compared to full-order models.

Effective for flutter and limit cycle oscillation analysis.

Abstract

Nonlinear aeroelastic reduced-order models (ROMs) based on machine learning or artificial intelligence algorithms can be complex and computationally demanding to train, meaning that for practical aeroelastic applications, the conservative nature of linearization is often favored. Therefore, there is a requirement for novel nonlinear aeroelastic model reduction approaches that are accurate, simple and, most importantly, efficient to generate. This paper proposes a novel formulation for the identification of a compact multi-input Volterra series, where Orthogonal Matching Pursuit is used to obtain a set of optimally sparse nonlinear multi-input ROM coefficients from unsteady aerodynamic training data. The framework is exemplified using the Benchmark Supercritical Wing, considering; forced response, flutter and limit cycle oscillation. The simple and efficient Optimal Sparsity Multi-Input ROM (OSM-ROM) framework performs with high accuracy compared to the full-order aeroelastic model, requiring only a fraction of the tens-of-thousands of possible multi-input terms to be identified and allowing a 96% reduction in the number of training samples.