Characteristics and Control of Nonlinear Flutter in Extra-Large-Span Bridges
Year: 2025
Type: article
Abstract: As main spans of bridges approach the 2,000 m class, flutter exhibits significant structural and aerodynamic nonlinearities. Traditional linear theories, which focus on a single critical wind speed, fail to explain post-critical behaviors such as limit-cycle oscillations, hysteresis, and multistability. This review synthesizes recent experimental, theoretical, and computational advances to elucidate key characteristics and fluid-structure interaction mechanisms in nonlinear flutter. It highlights amplitude-dependent aerodynamic and structural damping, flutter derivatives, multi-modal interactions, and vortex-induced–flutter coupling. The paper summarizes modern methods for identifying amplitude-varying parameters through free and forced vibration tests, nonlinear frequency- and time-domain analyses in 2D and 3D, and multi-modal coupling solvers, including two-layer iterative eigen analyses and rational-function–based approaches. Control strategies—such as central stabilizers, inverted-L guide vanes, and bio-inspired flexible devices—are reviewed, along with TMD optimization that explicitly incorporates aero-structural nonlinearities. Flow-field diagnostics provide mechanistic insights, while deep learning and reinforcement learning show potential for nonlinear aerodynamic modeling and aerodynamic-shape optimization. Future challenges include developing generalizable self-excited force models, efficient 3D multi-modal solvers, addressing complex non-uniform mountainous wind fields and multi-physics coupling, and transitioning from response prediction to resilience-based design and reliability standards, supported by open benchmarks and data sharing. This review aims to facilitate mechanism discovery, modeling, and stabilization of nonlinear flutter in long-span bridges.
Author: Xin Li
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414612637
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