Achintya Mukhopadhyay

Droplet-based microfluidics enables the manipulation of discrete fluid quanta in the form of microdroplets that provide numerous benefits for analyzing biological and chemical assays. However, because of the extremely low Reynolds numbers in such systems, mixing is limited by molecular diffusion only, which is often insufficient for many lab-on-a-chip applications. Therefore, dedicated strategies to enhance mixing within microdroplets are essential. Magnetic bead-based mixing leverages self-assembled, rotating chains of magnetic micro-particles, suspended in a droplet, behaving as a stirrer for mixing. Herein, we present a numerical investigation of mixing dynamics in a two-dimensional cylindrical micro-well stirred by a rotating magnetic bead-chain. The simulations solve the conservation equations for mass, momentum, and species transport in a boundary-fitted coordinate system. The effect of rotational speed and rotational direction on mixing efficiency is analyzed through observing massless tracer-species contour plots and a quantitative mixing index based on its concentration profile. Results show that chain-rotation significantly enhances mixing via chaotic advection compared to the diffusive mixing. Higher rotational speeds accelerate mixing in early stages, although long-term mixing plateaus at higher speeds. Importantly, alternating the rotation direction at regular, short intervals produces stronger vortex shedding and leads to faster mixing as compared to that in unidirectional rotation. These findings demonstrate that chain-rotation, specifically with directional flipping, can be tuned to optimize droplet-scale mixing, offering practical guidelines for the design of efficient micromixers in lab-on-chip applications.

The two-dimensional unsteady laminar flow past a slotted cylinder facilitated with fluid injection is studied to examine the mixing behavior between upstream air and the fluid injected from the cylinder-slots for different slot angular positions (0°–90°). Additionally, wake dynamics and the resulting variations in key hydrodynamic quantities resulting from fluid–fluid interaction are analyzed. Simulations are primarily conducted for an injected fluid and air velocity ratio (Vr) of 1, with additional cases up to 3 to assess the impact of injection momentum. Pressure, velocity, and vorticity contours are used to analyze wake dynamics, while mass-fraction and mixture-fraction fields are examined to check mixing characteristics. Furthermore, the recurrence quantification analysis (RQA) is used to understand the underlying flow dynamics. For Vr=1, two distinct wake characteristics are identified. At low injection angles (0°–20°), the wake remains symmetric with the absence of vortex shedding, resulting in minimal lift and drag. Beyond 20°, vortex shedding emerges for all configurations. For 0°–20° cases, the flow exhibits a narrow mixing layer and high downstream mass-fraction variance, indicating weak mixing. Mixing is enhanced at higher injection angles (60°–90°), as indicated by a wider mixing layer and the lowest downstream variance. However, intensified vortex shedding for 60°–90° cases leads to stronger wake oscillations and higher hydrodynamic forces. RQA of the lift coefficient shows stable equilibrium at low injection angles, transitioning to periodic dynamics at higher angles. Despite the few dynamical variations, similar mixing and hydrodynamic trends persist for all velocity ratios considered.

The runout table (ROT) cooling acts as an effective metallurgical tool to provide the desired cooling rate (CR) so as to obtain the required transformation product in the hot rolling process. Prediction and control of temperature in ROT is important to ensure microstructure and mechanical properties of thermo-mechanically processed steel strip. In the present study, a mathematical model is developed to simulate the thermal and metallurgical behaviour of the steel strip on a ROT. The model takes into account the effect of phase transformation by incorporating fundamental equation explaining the kinetics of austenite decomposition of steel under water jet impingement cooling. Particular attention is paid to account for the variation in heat transfer coefficient due to temperature change and operating conditions. Results from the proposed model are compared with available results and validated with industrial operational data. Various parametric studies are undertaken to examine the effects of finish roll temperature (FRT), strip thickness, strip velocity and cooling pattern on the strip temperature and coiling temperature (CT). It is seen that an increase in strip motion by 50% raises the CT by 10%. A change in strip thickness and FRT also alters the CT significantly. With the decrease in strip thickness and FRT, the CT also decreases. It is observed that early cooling with active headers at the beginning of ROT cools the strip faster than with late cooling. The average CR is influenced by the position and number of active headers. The overall cooling increases by up to 13% for continuous cooling when compared with late distributed cooling, while the later consumes 88% less water than continuous cooling scenario. The proposed approach can determine phase transformations over a wide range of operating conditions with high degree of accuracy and can contribute to efficient energy estimations.

The inverse diffusion flame (IDF) can experience thermoacoustic instability due to variations in power input or flow conditions. However, the dynamical transitions in IDF that lead to this instability when altering control parameters have not been thoroughly investigated. In this study, we explore the control parameters through two different approaches and employ multifractal detrended fluctuation analysis to characterize the transitions observed prior to the onset of thermoacoustic instability in the inverse diffusion flame. Our findings reveal a loss of multifractality near the region associated with thermoacoustic instability, which suggests a more ordered behavior. We determine that the singularity exponent, the width of the multifractal spectrum, and the Hurst exponent are reliable indicators of thermoacoustic instability and serve as effective classifiers of dynamical states in inverse diffusion flames.

Efficient particle sorting in microfluidic systems is vital for advancements in biomedical diagnostics and industrial applications. This study numerically investigates particle migration and passive sorting in symmetric serpentine microchannels, leveraging inertial and centrifugal forces for label-free, high-throughput separation. Using a two-dimensional numerical model, particle dynamics were analyzed across varying flow rates, diameter ratios (1.2, 1.5, and 2), and channel configurations. The optimized serpentine geometry achieved particle separation efficiencies exceeding 95% and throughput greater than 99%. A novel scaling framework was developed to predict the minimum number of channel loops required for efficient sorting. Additionally, the robustness of the proposed scaling framework is demonstrated by its consistency with findings from previous studies, which exhibit the same trend as predicted by the scaling laws, underscoring the universality and reliability of the model. Additionally, the study revealed the significant influence of density ratio (α) on sorting efficiency, where higher α values enhanced separation through amplified hydrodynamic forces. Optimal flow rates tailored to particle sizes were identified, enabling the formation of focused particle streaks for precise sorting. However, efficiency declined beyond these thresholds due to particle entrapment in micro-vortices or boundary layers. This work provides valuable insights and design principles for developing compact, cost-effective microfluidic systems, with broad applications in biomedical fields like cell sorting and pathogen detection, as well as industrial processes requiring precise particle handling.

Bioaerosols produced within the respiratory system play an important role in respiratory disease transmission. These include infectious diseases such as common cold, influenza, tuberculosis, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among several others. It is, therefore, of immense interest to understand how bioaerosols are produced within the respiratory system. This has not been extensively investigated. The present study computationally investigates how bioaerosols are produced in a model respiratory tract due to hydrodynamic interactions between breathed air and a thin mucus layer, which lines the inner surface of the tract. It is observed that Kelvin–Helmholtz instability is established in the thin mucus layer due to associated fluid dynamics. This induces interfacial surface waves which fragment forming bioaerosols under certain conditions. A regime map is created—based on pertinent dimensionless parameters—to enable identification of such conditions. Analysis indicates that bioaerosols may be produced even under normal breathing conditions, contrary to expectations, depending on mucus rheology and thickness of the mucus layer. This is possible during medical conditions as well as during some treatment protocols. However, such bioaerosols are observed to be larger (∼O(100)μm) and are produced in less numbers (∼100), as compared to those produced under coughing conditions. Treatment protocols and therapeutic strategies may be suitably devised based on these findings.