mingwu-li / pipe-data-driven

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

This repo includes the code and data for our paper on the data-driven reduction for pipes conveying fluid [1]. We perform reduction on two-dimensional spectral submanifolds (SSMs). The associated 2D reduced-order models (ROMs) enable efficient and even analytic predictions on the free and forced vibrations of the pipe systems, including periodic and quasi-periodic orbits and their bifurcations.

You are supposed to install SSMLearn [2] first and add misc folder to MATLAB path. We use COCO [3] to provide post-processing capabilities and validations. So you also need to install COCO. Then, you are ready to run the examples to reproduce the results in our manuscript.

In case you have any questions, you are welcome to reach me at [email protected]

[1] Mingwu Li, Hao Yan, Lin Wang. Data-driven model reduction for pipes conveying fluid via spectral submanifolds. International Journal of Mechanical Sciences. https://doi.org/10.1016/j.ijmecsci.2024.109414

[2] Mattia Cenedese, Joar Axås, George Haller https://github.com/haller-group/SSMLearn

[3] Frank Schilder, Harry Dankowicz, Mingwu Li. Continuation core (coco) https://sourceforge.net/projects/cocotools/