arXiv subjects

Shivam Prajapati

Publications and source records attributed to Shivam Prajapati.

5 recordsLinked to original sources

arXiv2026

On the scaling of bubble interactions in dynamic turbulence: theoretical, numerical, and experimental study

This study investigates dilute bubbly decaying homogeneous isotropic turbulence at high Reynolds number using theory, direct numerical simulation, and experiments. The turbulent kinetic energy and dissipation rate follow power-law decay, while the bubble population reorganizes relative to the evolving Hinze scale. When the dissipation decays sufficiently rapidly, the Hinze scale grows faster than the characteristic bubble diameter, driving the population from super-Hinze toward sub-Hinze sizes. The system passes through a mixed regime in which coalescence dominates but breakup remains active, followed by a pure-coalescence regime. Residual breakup in the mixed regime increases the number of small bubbles and enhances coalescence, leading to faster growth of the characteristic bubble size. DNS of dilute bubble-laden turbulence shows decay exponents close to single-phase turbulence and a bubble-size distribution that shifts toward smaller diameter relative to the Hinze scale. Before the transition, the distribution exhibits two power-law ranges associated with capillary effects and inertial breakup; after the transition, it approaches a single capillary-dominated scaling. Theory and DNS predict distinct growth laws for bubble diameter in the mixed and pure-coalescence regimes, together with corresponding scalings for number density, interfacial area, and coalescence rate. These predictions are further assessed in a spatially developing pump-driven bubbly duct flow at higher Reynolds number. Despite confinement, inhomogeneity, and wall production, the measured trends agree with the theoretical and DNS-based scalings. The results identify Hinze-scale drift as the organizing mechanism for bubble interactions in both idealized and practical decaying turbulent flows, and provide guidance for population-balance and interfacial-area-transport models.

physics.flu-dyn↗

arXiv2026

Mass-Transfer Control With Microbubbles in Highly Turbulent Decaying Flows

We hypothesize that combining extreme turbulence with a minute reduction in surface tension $\sigma$ (surface tension of the liquid) using surfactant provides a simple and scalable route for controlling micron scale bubble size in gas--liquid systems. To test this, we generate high-intensity turbulence using a multiphase pump [turbulent intensity $\ge 40\%$; Taylor Reynolds number $Re_\lambda=\mathcal{O}(10^3)$; bulk Reynolds number $Re=\mathcal{O}(10^5)$] feeding a straight duct, which produces a decaying turbulent flow where, without additives, bubble coalescence dominates and causes monotonic downstream growth in the mean diameter $d_\mathrm{avg}$ of the bubbles. This growth is governed by the turbulent dissipation rate $\varepsilon$. High-speed imaging, back-lit shadowgraph and particle shadow velocimetry (PSV) quantify bubble statistics ($d_\mathrm{avg}$, and the bubble-size distribution) and turbulence metrics (turbulent kinetic energy $k$, turbulence intensity $\mathcal{I}$, and dissipation rate $\varepsilon$). We then introduce a minute amount ($\sim 0.01\%$ critical micelle concentration) of additive that produces a slight reduction in $\sigma$, used here only as an interfacial tuning knob because the same change in surface tension can be achieved with non surface active agents. This small decrease in $\sigma$ enhances breakup, slightly suppresses coalescence, and makes smaller bubbles more breakup prone, resulting in reduced $d_\mathrm{avg}$ and a narrower bubble-size distribution. Turbulence statistics remain unchanged within experimental uncertainty, indicating that the effect arises entirely from interface rather than hydrodynamic changes. Overall, combining extreme turbulence with a minute reduction in surface tension offers a low complexity and tunable lever for setting bubble-size distributions and intensifying mass transfer in industrial multiphase flows.

physics.flu-dyn↗

arXiv2026

Laminar-to-Turbulent Transition of Yield-Stress Fluids in Pipe and Channel Flows

We present direct numerical simulations (DNS) of laminar to turbulent transition in Herschel-Bulkley (HB) yield-stress fluids flowing through pipes and rectangular channels. The simulations employ a Herschel-Bulkley formulation that captures the yield-stress-driven plug, its breakdown, and the emergence of near-wall turbulent structures, enabling direct resolution of the transition mechanisms. The DNS cover a broad range of generalized Reynolds numbers, Re_G = 378 to 5300, allowing us to resolve plug formation, transition onset, and fully turbulent regimes. In pipe flow, the simulations reproduce the characteristic transition sequence, which includes a strong plug and negligible turbulence at low Re_G, a sharp rise in turbulence intensity and u'rms within a narrow transitional window (Re_G ~ 2000 to 3000), and wall-dominated turbulence with a weakened core at higher Re_G. Transition occurs only when local Reynolds stresses exceed the yield stress. The resulting regime boundaries (Re_G < 1735 laminar, 1735 < Re_G < 2920 transitional, and Re_G > 2920 turbulent) align with trends reported for Carbopol fluids. This work provides the first DNS resolving the complete laminar to turbulent transition in HB fluids for both pipe and channel configurations, offering unified insight into plug breakdown, turbulence localization, and the role of yield stress in transition mechanisms. Experimental validation using a 3.6 m acrylic channel with particle image velocimetry (PIV) is planned to further assess the DNS predictions and quantify geometry-dependent transition thresholds.

physics.flu-dyn↗

arXiv2026

Depth-Aware Machine Learning Framework for Bubble Characterization in Two-Phase Flows

Understanding the three-dimensional motion of bubbles is essential for interpreting transport and mixing in multiphase flows, especially when bubbles deform under shear or move rapidly through the flow field. In many laboratory setups, only a single high-speed camera is available, which limits measurements to two dimensions. Traditional image-processing tools can identify bubbles only when they appear circular and isolated, but they struggle with irregularly shaped bubbles, shear-induced deformations, strong blurring, and partial overlaps. Multi-camera systems could overcome these issues, but require significant hardware additions and calibration effort. In this work, we introduce a new machine-learning framework that can detect bubbles and estimate their depth using only a single 20 kHz high-speed camera with 3 \textmu m resolution. The method first uses a large unlabeled dataset and clusters the bubbles with an unsupervised algorithm to reveal their underlying structure. These clusters provide pseudo labels, which are combined with a small set of true in-plane bubble labels to train a semi-supervised model that generalizes across different bubble appearances. These components produce a continuous depth-proxy score that indicates how close each bubble is to the imaging plane, even when bubbles are distorted or irregularly shaped. In parallel, we perform robust bubble identification using instance segmentation, which separates touching, overlapping, and elongated bubbles generated by high-velocity shear. Quantitatively, the in-plane segmentation baseline achieves strong held-out performance with Average Precision (AP) = 0.818, implying stable detection across thresholds, clutter, bubble detection Precision of 0.901, and a False-Positive Rate (FPR) near 6.1\%, hence low spurious bubbles and cleaner statistics under the tested acquisition conditions.

physics.flu-dyn↗

arXiv2021

Recent Trends in Artificial Intelligence-inspired Electronic Thermal Management

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.

cs.LG↗