Data-driven model reduction for pipes conveying fluid via spectral submanifolds.

Pipes conveying fluid have non-linearizable dynamics when the flow velocity is large enough. Developing analytical models that accurately capture these highly nonlinear and complex dynamics is challenging. Efficient analysis and predictions of these nonlinear dynamics are essential in the design and control of pipe systems in engineering applications. To address these challenges, here we construct low-dimensional reduced-order models in a data-driven setting. In particular, we perform model reduction on spectral submanifolds (SSMs) via SSMLearn. This open-source package automates the identification of low-dimensional SSMs and their associated reduced-order models (ROMs). Our reduction via SSMs treats pipes with various boundary conditions and flow velocities in a unified way. It achieves a significant reduction because the ROMs are two-dimensional, which enables efficient and even analytical predictions on the nonlinear vibration of the pipes, paving the way for real-time prediction of the non-linearizable dynamics. In addition, our SSM-based reduction shows remarkable extrapolation capability because it is built on invariant manifolds and normal form theory. We demonstrate that the ROMs trained on unforced responses can be directly extrapolated to make reliable predictions on forced vibrations under various excitation frequencies and amplitudes, including periodic and quasi-periodic orbits and their bifurcations. [Display omitted] • A unified data-driven model reduction framework is proposed for pipes conveying fluid. • A significant dimension reduction is obtained via two-dimensional spectral submanifolds. • Reduced-order models enable analytical and efficient predictions. • Reduced-order models show remarkable extrapolation capability. [ABSTRACT FROM AUTHOR]