Energy analysis of 2D electro-thermo-hydrodynamic turbulent convection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917303332896768 |
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| author | Hutchinson, Owen Kostova, Katerina Wu, Jian Guan, Yifei |
| author_facet | Hutchinson, Owen Kostova, Katerina Wu, Jian Guan, Yifei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy analysis of 2D electro-thermo-hydrodynamic turbulent convection Hutchinson, Owen Kostova, Katerina Wu, Jian Guan, Yifei Fluid Dynamics 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. |
| title | Energy analysis of 2D electro-thermo-hydrodynamic turbulent convection |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2603.01279 |