Data-driven reduced-order prediction modeling for cylinder vortex-induced vibrations in the subcritical Reynolds number regime

Key Points

• Vortex-induced vibrations are effectively predicted by reduced-order models in the subcritical Reynolds number regime.
• The study finds that these models can significantly enhance prediction accuracy while reducing computational costs.
• Analysis involves a data-driven approach to develop prediction modeling techniques tailored for fluid dynamics scenarios.
• Potential applications include improved designs in engineering systems, highlighting the relevance of effective modeling methods.