Josephson-Anderson relation as diagnostic of turbulent drag reduction by polymers.

The detailed Josephson-Anderson relation, which equates instantaneously the volume-integrated vorticity flux and the work by pressure drop, has been the key to drag reduction in superconductors and superfluids. We employ a classical version of this relation to investigate the dynamics of polymer drag-reduced channel flows, particularly in the high-extent drag reduction (HDR) regime which is known to exhibit strong space-time intermittency. We show that high drag is not created instantaneously by near-wall coherent vortex structures as assumed in prior works.

Unravelling the role of pore structure of biomass-derived porous carbon in charge storage mechanisms for supercapacitors.

This study presents findings on the production and analysis of activated carbon (AC), which exhibits a significantly expansive surface area derived from readily available and inexpensive agroforestry waste, specifically coconut shells. The carbon materials displayed encouraging features for electrochemical energy storage applications with a high specific surface area (2920 m g), an ordered mesoporous structure (∼2.5 nm), and substantial electronic conductivity.

Establishing an affordable solar-floating FeO@AR-TiO photo-Fenton catalytic system through the cyclic utilization of iron waste to de-pollute textile water contamination.

This study focuses on developing a cost-effective FeO catalyst from oilfield iron waste to create a floating heterogeneous photo-Fenton system with anatase/rutile(A/R) TiO heterophase photocatalyst (cork-FeO@AR-TiO) for treating textile pollution in sunlight. Through controlling sol-gel (SG) microwave heating technique, the A/R ratio of AR-TiO crystal is tuned (A/R ratio = 1.13 and E = 3.

ML for fast assimilation of wall-pressure measurements from hypersonic flow over a cone.

Data assimilation (DA) integrates experimental measurements into computational models to enable high-fidelity predictions of dynamical systems. However, the cost associated with solving this inverse problem, from measurements to the state, can be prohibitive for complex systems such as transitional hypersonic flows. We introduce an accurate and efficient deep-learning approach that alleviates this computational burden, and that enables approximately three orders of magnitude computational acceleration relative to variational techniques.