arXiv subjects

Gaurav Bhutani

Publications and source records attributed to Gaurav Bhutani.

5 recordsLinked to original sources

arXiv2026

Inhalation-exhalation asymmetry in pressure-driven airflow through a population-derived median unilateral human nasal cavity

This study investigates inhalation-exhalation asymmetry in population-derived median unilateral human nasal cavity under pressure-driven breathing using time-resolved computational fluid dynamics (CFD). A standardized geometry reconstructed from averaged CT data of 30 adults was subjected to a sinusoidal nasopharyngeal pressure waveform over a complete breathing cycle. Low, intermediate, and high pressure amplitudes were considered to quantify breathing-phase-dependent changes in flow rate, Reynolds number, wall shear stress, nasal resistance, and olfactory-region airflow. For identical imposed pressure magnitudes, inhalation produced a consistently larger flow rate and lower aerodynamic resistance than exhalation, demonstrating a direction-dependent pressure-flow response. Increasing breathing intensity increased Reynolds number and nasal resistance while largely preserving the spatial pattern of the flow. During inhalation, stronger airflow penetrated further into the superior nasal cavity and produced higher and more localized wall shear stress, whereas exhalation exhibited smoother airflow redistribution. The breathing-phase asymmetry also affected regional ventilation: the fraction of total airflow reaching the olfactory region increased with inspiratory intensity and reached approximately 6% under the high-pressure condition, while the expiratory fraction remained comparatively insensitive to breathing intensity. These results demonstrate that pressure-driven transient simulations naturally capture geometry-dependent inhalation-exhalation differences that are suppressed when identical flow rates are prescribed for the two breathing phases, with consequences for both global nasal resistance and regional airflow delivery.

physics.med-ph↗

arXiv2026

Physics-Informed Neural Networks for Depth-Averaged Avalanche Dynamics

Accurate prediction of avalanche motion is essential for hazard assessment in mountainous terrain. This study develops and evaluates a physics-informed neural network (PINN) framework for the Savage-Hutter model of depth-averaged granular flow, progressing from 1D analytical verification to 2D experimental validation. First, three 1D problems of increasing complexity were verified against the analytical solution: height prediction with prescribed velocity, velocity prediction with prescribed height, and coupled prediction of both fields using the conservative formulation. The decoupled tests accurately reconstructed the spatio-temporal evolution of each field when the other was prescribed. The coupled formulation learned both fields without prescribed data, achieving mean height and velocity RMSEs of 0.043 and 0.079 in non-dimensional units. A hyperparameter sensitivity study evaluated the effects of network depth, width, collocation density, learning rate, and epochs. The framework was then extended to 2D and validated against laboratory experiments of a cylindrical granular pile collapsing on an inclined plane, with TITAN2D providing numerical comparisons. Purely physics-based training converged to the trivial zero solution; augmenting the loss with 10 sparse training points from final deposit profiles produced a physics-informed, data-assisted hybrid framework. Peak flow depth, depth-averaged velocity, RMSE, and wetted-area IoU evaluated global and local agreement. Global height RMSE ranged from 2.7 to 6.7 mm across four experimental cases, while mean wetted-area IoU ranged from 69 to 81 %, demonstrating consistent performance across variations in pile mass and slope angle.

physics.flu-dyn↗

arXiv2026

Influence of fluidizing medium on hydrodynamics and particle mixing in a binary fluidized bed: a CFD-DEM study

Three-dimensional CFD-DEM simulations were used to investigate the influence of fluidizing medium on the hydrodynamics and mixing of a binary fluidized bed containing equal-density 3 and 4 mm particles. Water and air were compared using identical geometry, particle properties, and initial conditions over matched normalized superficial velocities of 0.5-3.0 times the minimum fluidization velocity. The numerical framework was validated against published liquid-solid bed-expansion data and benchmarked against an established gas-solid CFD-DEM case. At matched normalized superficial velocities, water fluidization produced a comparatively dense and homogeneous bed with stable bed height and pressure drop and weak velocity fluctuations. Air fluidization resulted in greater expansion, persistent oscillations, void- and bubble-like structures, stronger circulation, and greater spatial heterogeneity. Mixing, quantified using the Lacey mixing index, increased with superficial velocity in both media but developed more rapidly and reached higher levels in air. Particle trajectories and mean-square displacement further showed greater particle mobility and transport in air. The results demonstrate that normalization by the minimum fluidization velocity does not collapse the hydrodynamic or mixing behavior across fluidizing media because fluid properties strongly influence the underlying particle-transport mechanisms.

physics.flu-dyn↗

arXiv2026

Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography

Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dynamics over curved terrain remains largely unexplored. This study extends a depth-averaged PINN formulation based on the Savage-Hutter equations to an exponentially curved chute with spatially varying inclination and a strain-rate-dependent Mohr-Coulomb earth-pressure closure. The model is validated against measured front- and rear-edge trajectories from a laboratory granular-avalanche experiment, with selected observations withheld from training. A staged temporal curriculum proved essential for accurate prediction, reducing the held-out trajectory error by approximately two orders of magnitude compared with training over the full time domain from the outset. Sparse-data experiments further showed that observation placement was more influential than observation number within the configurations tested. Four observations bracketing the transition from acceleration to deceleration achieved nearly the same accuracy as the eight-observation reference configuration, whereas observations clustered at early or late times performed poorly. The results demonstrate the importance of both training strategy and informative data placement when applying PINNs to granular flows over curved topography.

cond-mat.soft↗

arXiv2026

Transient Electrical Response Beyond Quasistatic Capacitance at Mechanically Excited Droplet--Dielectric Interfaces

Dynamic electrowetting of conducting droplets under mechanical deformation is conventionally modeled as a quasi-static variable-capacitance system, in which the electrical response is assumed to be governed solely by the evolution of the droplet--electrode contact area. Under the assumption of instantaneous charge equilibration, this framework successfully describes the cyclic steady-state electromechanical response of the system. However, its validity for transient interfacial electrical dynamics remains largely unexplored. Here, the transient electrowetting response of mercury droplets confined between a polymeric dielectric-coated electrode (PTFE or PVDF) and an opposing copper electrode is investigated under periodic mechanical excitation with multiple waveforms at 2 Hz. The measured contact area and corresponding capacitance evolve closely as predicted from instantaneous surface-energy minimization, confirming that the liquid-interface mechanics remain quasi-static. In contrast, the measured transient current and instantaneous electrical power exhibit pronounced asymmetric excitation and relaxation phases that are independent of the excitation waveform, demonstrating that transient charge evolution cannot be inferred from the instantaneous geometric capacitance alone. This transient behavior is phenomenologically interpreted using constituent first-order interfacial dielectric charge-relaxation kinetics, indicating that the measured current arises from slow dielectric charging followed by dielectric relaxation over the timescale of the imposed periodic mechanical oscillations during discharging. These findings establish that transient electrowetting is governed by the coupled interplay of droplet electrohydrodynamics and dielectric interfacial polarization, requiring a constitutive description beyond quasi-static variable-capacitance models based solely on contact-line dynamics.

physics.flu-dyn↗