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Tushar Srivastava

Publications and source records attributed to Tushar Srivastava.

2 recordsLinked to original sources

On Free Moving Micron-Sized Droplet-Particle Collisions

Predictive modelling of agglomeration in spray drying and particle capture in aerosol scavenging requires a fundamental understanding of droplet-particle collisions. The study complements prior work by investigating mid-air collisions between free micron-sized spherical droplets and particles with a size ratio of three. Particle wettability and density are varied to elucidate the mechanisms governing collision outcomes and the role of collision offset. Results show that particle density determines whether a particle is engulfed by the droplet or remains at the droplet interface during capture, while high wettability suppresses particle separation even in glancing collisions. A modified effective Weber number incorporating particle density and wettability is proposed to map collision outcomes. To assess its robustness, the present data are combined with literature results in a unified regime map. The regime boundaries separating collision outcomes collapse when the size ratio and Ohnesorge number are held constant. However, at a given collision offset, variations in size ratio and Ohnesorge number alter the critical effective Weber number for particle separation through changes in collision geometry and viscous resistance.

physics.flu-dyn

Correcting User Decisions Based on Incorrect Machine Learning Decisions

. It is typically assumed that for the successful use of machine learning algorithms, these algorithms should have a higher accuracy than a human expert. Moreover, if the average accuracy of ML algorithms is lower than that of a human expert, such algorithms should not be considered and are counter-productive. However, this is not always true. We provide strong statistical evidence that shows that even if a human expert is more accurate than a machine, an interaction with such a machine is beneficial when communication with the machine is non-public. The existence of a conflict between the user and ML model, and the private nature of user-AI communication will have the effect of making the user think about their decision and hence increase overall accuracy.

cs.HC