arXiv subjects

Faizaan Mohammed

Publications and source records attributed to Faizaan Mohammed.

2 recordsLinked to original sources

arXiv2026

Monocular 3D bubble reconstruction: DNS-informed ray tracing and deep learning

Bubble size, depth, and spatial distribution govern transport and uniformity in multiphase flows. A single shadowgraphic view cannot separate bubble diameter from focal-plane displacement, as image signatures depend on both, while multicamera systems add optical access, calibration, and cost. We develop a single-camera physics-guided framework combining direct numerical simulation (DNS), validated Monte Carlo ray tracing, preprocessing, and focus-conditioned multitask deep learning to recover three-dimensional bubble position and physical-equivalent diameter from two-dimensional shadowgraphs. DNS-resolved bubble label images rendered at multiple focal-plane settings. The ray tracer reproduces shadowgraphic signatures with errors below $3\%$ (intensity), $1\%$ (apparent size), $15\%$ (blur), $6\%$ (numerical aperture), and $4\%$ (edge-spread-function shape), running $\sim30\times$ faster than the CPU implementation. The dataset has $900$ frames and $87{,}728$ labeled instances, partitioned by DNS timestamp to prevent leakage and expanded to over $10^{5}$ crops by broad-depth controlled-density rendering. A multitask network predicts signed depth and diameter from crops and focal-plane coordinates, achieving $\sim97.6\%$ normalized depth accuracy on held-out data; a closed-loop DNS-to-image-to-reconstruction test of $180$ bubbles gives diameter error below $9.71\%$ ($\sim90.29\%$ accuracy). On experimental images at four radial planes ($142$--$971$ bubbles each), PDF overlap is $68\%$--$92\%$ and log-PDF correlation is $0.72$--$0.87$. At the focal reference plane ($11{,}679$ bubbles), overlap is $\sim86\%$, correlation is $0.93$, and full-distribution, $D_{99.8}$, and composite errors are below $7.7\%$, increasing with focal-plane distance and defining a recoverability limit. Without stereoscopic hardware, the framework offers a scalable diagnostic for dense bubbly flows.

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↗