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Data-driven modeling of the chaotic thermal convection in an annular thermosyphon

Article dans une revue avec comité de lecture
Auteur
ccLOISEAU, Jean-Christophe
134975 Laboratoire de Dynamique des Fluides [DynFluid]

URI
http://hdl.handle.net/10985/23063
DOI
10.1007/s00162-020-00536-w
Date
2020-07
Journal
Theoretical and Computational Fluid Dynamics

Résumé

Identifying accurate and yet interpretable low-order models from data has gained renewed interest over the past decade. In the present work, we illustrate how the combined use of dimensionality reduction and sparse system identification techniques allows us to obtain an accurate model of the chaotic thermal convection in a two-dimensional annular thermosyphon. Taking as guidelines the derivation of the Lorenz system, the chaotic thermal convection dynamics simulated using a high-fidelity computational fluid dynamics solver are first embedded into a low-dimensional space using dynamic mode decomposition. After having reviewed the physical properties the reduced-order model should exhibit, the latter is identified using SINDy, an increasingly popular and flexible framework for the identification of nonlinear continuous-time dynamical systems from data. The identified model closely resembles the canonical Lorenz system, having the same structure and exhibiting the same physical properties. It moreover accurately predicts a bifurcation of the high-dimensional system (corresponding to the onset of steady convection cells) occurring at a much lower Rayleigh number than the one considered in this study.

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  • Dynamique des Fluides (DynFluid)

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