期刊
PHYSICAL REVIEW FLUIDS
卷 7, 期 8, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevFluids.7.L082402
关键词
-
资金
- German National Science Foundation (DFG) [SPP 1881]
This study derives low-dimensional models using data-driven methods to describe transitions among exact coherent states in plane Couette flow. These models can accurately predict off-SSM transitions that were not used in their training.
We derive low-dimensional, data-driven models for transitions among exact coherent states in one of the most studied canonical shear flows, the plane Couette flow. These one -or two-dimensional nonlinear models represent the leading-order reduced dynamics on attracting spectral submanifolds (SSMs), which we construct using the recently developed SSMLearn algorithm from a small number of simulated transitions. We find that the energy input and dissipation rates provide efficient parametrizations for the most important SSMs. By restricting the dynamics to these SSMs, we obtain reduced-order models that also reliably predict nearby, off-SSM transitions that were not used in their training.
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