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

Li，Mingwu1; Yan，Hao2; Wang，Lin3,4

2024-09-01

10.1016/j.ijmecsci.2024.109414

International Journal of Mechanical Sciences   影响因子和分区

0020-7403

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.

Fluid–structure interaction Model reduction Nonlinear dynamics Nonlinear vibration Pipes conveying fluid Spectral submanifold

SCI ; EI

英语

第一

20242316210934

Data reduction ; Extrapolation ; Forecasting ; Vibration analysis

Fluid Flow:631 ; Data Processing and Image Processing:723.2 ; Numerical Methods:921.6 ; Mechanical Variables Measurements:943.2

ENGINEERING

2-s2.0-85195049135

Scopus

1.Department of Mechanics and Aerospace Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Hunan Aviation Powerplant Research Institute,Aero Engine Corporation of China,Zhuzhou,Hunan,412002,China
3.Department of Engineering Mechanics,School of Aerospace Engineering,Huazhong University of Science and Technology,Wuhan,430074,China
4.Hubei Key Laboratory for Engineering Structural Analysis and Safety Assessment,Wuhan,430074,China

Li，Mingwu,Yan，Hao,Wang，Lin. Data-driven model reduction for pipes conveying fluid via spectral submanifolds[J]. International Journal of Mechanical Sciences,2024,277.

Li，Mingwu,Yan，Hao,&Wang，Lin.(2024).Data-driven model reduction for pipes conveying fluid via spectral submanifolds.International Journal of Mechanical Sciences,277.

Li，Mingwu,et al."Data-driven model reduction for pipes conveying fluid via spectral submanifolds".International Journal of Mechanical Sciences 277(2024).