Two formulations are at the heart of the study of turbulence: Kolmogorov’s universal framework for small-scale turbulence, which describes how energy propagates and dissipates through increasingly small eddies; and Taylor-Couette (TC) flows, which are very simple to create yet exhibit extremely complex behaviors, thereby setting the benchmark for the study of the fundamental characteristics of complex flows.

For the past many decades, a central contradiction between these potent formulations has plagued the field. Despite extensive experimental research and despite being found universal to almost all turbulent flows, Kolmogorov’s framework has apparently failed to apply to turbulent TC flows.

But now, after nine years developing a world-class TC setup at the Okinawa Institute of Science and Technology (OIST), researchers have finally resolved this tension by conclusively demonstrating that, contrary to the prevailing understanding, Kolmogorov’s framework does apply universally to the small scales of turbulent TC flows – precisely as predicted. 

The University of Houston has been redesignated by the National Security Agency (NSA) as a Center for Academic Excellence in Cyber Research (CAE-R) through 2029, reaffirming UH’s national leadership in advancing cybersecurity innovation, education and workforce development to help protect America’s critical infrastructure.

Using tools from network science, researchers built a statistical model that observed how senators and congresspeople interacted with one another on X, formerly known as Twitter, which deduced their positions and potential ambitions. 

Industrial dyes used in textiles, plastics, paper, and cosmetics make wastewater vividly colored and potentially toxic. Many of these dyes resist normal treatment, threatening aquatic life and human health. Now researchers have harnessed machine learning to predict how well biochar, a carbon-rich, low-cost material made from plant waste, can remove these stubborn pollutants from water.

A new KAUST-developed workflow helps researchers efficiently test and validate the assumptions underlying complex statistical models to ensure more reliable results.