Quad-cascade picture of electrokinetic turbulence

Turbulence, ubiquitous in nature and across various disciplines, exhibits chaotic and intermittent fluctuations in space and time, defying precise prediction. For nearly a century, extensive efforts have been made to uncover the underlying universality and invariant laws from the immense disorder and chaotic nature of turbulence. While the celebrated Kolmogorov −5/3 law stands as a robust cornerstone, it falls short in capturing the diverse scaling behavior exhibited in turbulence influenced by external volume forces, like thermal convection and electrokinetic flows. This study proposes a general framework that couples the fluxes of kinetic energy and scalar variance, culminating in the formulation of a universal conservation law. This framework offers a comprehensive quad-cascade depiction of turbulence, enabling predictions that go beyond the limitations of existing models. We illustrate this framework with microfluidic experiments on electrokinetic turbulence, wherein power spectra of concentration and velocity fluctuations exhibit the predicted scaling behaviors, providing remarkable agreement with theory. These findings not only deepen our understanding of the complete cascades in turbulence driven by external volume forces but also hold promise for insights into other turbulent phenomena.

Authors

Institutions

Publication Details

Journal
Physics of Fluids
Published
2025-02-01
DOI
https://doi.org/10.1063/5.0250162
Primary Topic
Electrohydrodynamics and Fluid Dynamics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quad-cascade picture of electrokinetic turbulence

Keyi Nan, Guangyin Jing, Yueqiang Zhu, Wei Zhao et al.
Physics of Fluids
Electrohydrodynamics and Fluid Dynamics
article

Quad-cascade picture of electrokinetic turbulence

Keyi Nan, Guangyin Jing, Yueqiang Zhu, Wei Zhao, Yu Chen, Chen Zhang, Ce Zhang, Ming Zeng, Yanxia Shi, Kaige Wang, Jintao Bai, Tianyun Zhao, Jin’an Pang
article en

Abstract

Turbulence, ubiquitous in nature and across various disciplines, exhibits chaotic and intermittent fluctuations in space and time, defying precise prediction. For nearly a century, extensive efforts have been made to uncover the underlying universality and invariant laws from the immense disorder and chaotic nature of turbulence. While the celebrated Kolmogorov −5/3 law stands as a robust cornerstone, it falls short in capturing the diverse scaling behavior exhibited in turbulence influenced by external volume forces, like thermal convection and electrokinetic flows. This study proposes a general framework that couples the fluxes of kinetic energy and scalar variance, culminating in the formulation of a universal conservation law. This framework offers a comprehensive quad-cascade depiction of turbulence, enabling predictions that go beyond the limitations of existing models. We illustrate this framework with microfluidic experiments on electrokinetic turbulence, wherein power spectra of concentration and velocity fluctuations exhibit the predicted scaling behaviors, providing remarkable agreement with theory. These findings not only deepen our understanding of the complete cascades in turbulence driven by external volume forces but also hold promise for insights into other turbulent phenomena.

Physics of FluidsVol. 37(2)
Northwestern Polytechnical University (CN), Northwest University (CN)
National Natural Science Foundation of China, Natural Science Basic Research Program of Shaanxi Province
Openalex Percentile: Top 98%
Electrohydrodynamics and Fluid Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.