Researchers Discover Hysteretic Behavior in Turbulent Flow Dissipation
A new study combining wind tunnel experiments and computer simulations reveals that energy dissipation in oscillating turbulent flows exhibits hysteresis—the dissipation rate depends on whether the flow is accelerating or decelerating. This phenomenon occurs at constant mean Reynolds numbers and is explained through the unsteady term in the Kármán–Howarth equation. The findings have implications for understanding energy loss in various out-of-equilibrium systems across physics and engineering.
Researchers have identified a previously undocumented hysteretic effect in how turbulent flows dissipate energy. Using both experimental wind tunnel data and direct numerical simulations of oscillating flows, the team found that at a fixed mean Reynolds number, decelerating flows exhibit larger dissipation constants than accelerating flows. This asymmetry produces a characteristic hysteresis cycle when the flow undergoes periodic oscillations, with the cycle's area scaling predictably with a dimensionless parameter combining the Strouhal number and relative forcing amplitude. The effect is quantitatively explained through the influence of the unsteady term in the Kármán–Howarth equation, a fundamental relationship in turbulence theory. The discovery extends beyond classical turbulence physics, offering insights relevant to a broader class of out-of-equilibrium systems.
What different sources said
- arXiv physicsCenter
Dynamical hysteresis in the dissipation in turbulent flows
Related
Genetic Drift, Not Selection, Drives Rapid Feather Color Evolution in Island Bird Radiation
A new study of an island bird radiation found that rapid evolution of feather coloration is driven primarily by genetic drift in small populations rather than sexual or ecological selection. The research integrated whole-genome data with detailed plumage measurements across complete species sampling to test whether signaling trait evolution correlates with speciation rates. The findings suggest that neutral demographic processes play a central role in generating phenotypic diversity during island radiations, challenging assumptions about the mechanisms driving rapid evolution.
New AI Model Improves Prediction of Therapeutic Peptide Function from Protein Sequences
Researchers developed a lightweight CNN classifier that predicts whether peptide sequences have therapeutic properties, trained on a database of 54,655 peptides across 48 functional categories. The model uses a novel negative sampling strategy to reduce false positive rates from over 60% in previous approaches to 2.1%. This advancement could accelerate drug discovery by enabling faster computational screening of peptide candidates before expensive experimental testing.
Study Shows Different Metabolic Stress Models Produce Distinct Effects on Human Neuronal Networks
Researchers tested three common in vitro metabolic stress models on human-derived neuronal networks and found each produced different patterns of neuronal activity and cell damage. The models tested were hypoxia alone, oxygen-glucose deprivation (OGD), and hypoxia combined with glutamate exposure. The findings suggest that choice of experimental model significantly affects results and that combining electrophysiological and structural analyses is important for accurately assessing metabolic stress in stroke research.