Energy analysis of 2D electro-thermo-hydrodynamic turbulent convection

TL;DR

This paper conducts a comprehensive numerical and analytical study of 2D electro-thermo-hydrodynamic turbulent convection, exploring energy dynamics, predicting chaotic behavior, and identifying coherent structures in multi-physical fluid systems.

Contribution

It introduces a high-fidelity spectral solver, derives governing dynamical systems, and applies machine learning and modal decomposition to analyze energy transfer and structures in charged particle convection.

Findings

Analytical dynamical systems for energy components derived.

LSTM neural network successfully predicts chaotic energy time series.

Modal decomposition reveals energy-containing coherent structures.

Abstract

Turbulent convection is ubiquitous in fluid systems. In particular, multi-physical convection problems involve mass, heat, and particle transfer. When the particles are charged and driven by a high-voltage electric field, both buoyancy and electric forces contribute to driving and maintaining the convection. In this work, we perform numerical analysis using a high-fidelity Fourier-Chebyshev spectral solver. We further derive the dynamical systems governing the kinetic energy, the enstrophy, the potential energy, and the electric energy analytically. Using the simulated data, we apply a long short-term memory recurrent neural network to predict the chaotic time series of domain-average energy terms. Finally, we perform a data-driven modal decomposition to show the coherent structures that contain energy and enstrophy in 2D turbulent convection.