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

Abstract

dentifying accurate and yet interpretable low-order models from data has gained a 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 a similar structure and exhibiting the same physical properties. Finally, extensions to other flow configurations with or without control are discussed.

References in corpus (10)

• Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
• A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
• On Dynamic Mode Decomposition: Theory and Applications
• Sparsity-promoting dynamic mode decomposition
• Chaos as an Intermittently Forced Linear System
• Data-Driven Sparse Sensor Placement for Reconstruction
• Sparse identification of nonlinear dynamics for model predictive control in the low-data limit
• Characterizing and correcting for the effect of sensor noise in the dynamic mode decomposition
• Sparse reduced-order modeling : Sensor-based dynamics to full-state estimation
• Transient growth in Rayleigh-Bénard-Poiseuille/Couette convection

Cited by in corpus (8)

• Enhancing Computational Fluid Dynamics with Machine Learning
• Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control
• Physics-constrained, low-dimensional models for MHD: First-principles and data-driven approaches
• On the role of nonlinear correlations in reduced-order modeling
• Sparse nonlinear models of chaotic electroconvection
• Data-Driven Stabilization of Periodic Orbits
• Data-driven Discovery of Delay Differential Equations with Discrete Delays
• Data-assisted, physics-informed propagators for recurrent flows