arXiv subjects

Pranay P. Nagrani

Publications and source records attributed to Pranay P. Nagrani.

6 recordsLinked to original sources

arXiv2025

Hierarchical Bayesian inference for uncertainty quantification of thermal grease rheology

Rheologically complex soft solids such as thermal greases consist of filler particles within a polymer matrix. These materials find applications in improving the conformity of solid-solid contacts and enhancing heat transfer. Complex soft solids exhibit a transient non-Newtonian rheological response, including thixotropy and viscoelasticity. Previously, stress relaxation and buildup in sheared commercial thermal greases were successfully captured using a nonlinear elasto-visco-plastic (NEVP) model and a thixo-elasto-visco-plastic (TEVP). However, the previous model calibration methods ignored parameter uncertainty, providing only single values of the rheological parameters, and did not quantitatively address the chosen model's identifiability from the data or credibility of the calibration. We address these limitations via hierarchical Bayesian inference, accounting for uncertainties arising from epistemic and aleatoric sources. Importantly, the hierarchical approach allows us to assimilate experiments measuring the stress responses at various startup shear rates by allowing the models' parameters to vary across different shear rates. Then, a global distribution and the associated uncertainty are obtained by pooling. We also propagate uncertainties to the transient shear stress response predicted by the models. Overall, we demonstrate that the chosen NEVP and TEVP models are identifiable from rheometric startup data. However, for the TEVP model, the uncertainty of the parameters is lower (narrower distributions) when higher shear rates are used for inference.

cond-mat.soft↗

arXiv2025

Correlations for the Interphase Drag in the Two-Fluid Model of Gas--Liquid Flows through Packed-Bed Reactors

Experiments conducted by NASA measured the pressure drop due to gas--liquid flow through a packed-bed reactor under microgravity conditions. From these experiments, we develop correlations for the gas--liquid $f_{gl}$ interphase drag in a two-fluid model (TFM). We use an Ergun-type closure for liquid--solid drag. Then, under a 1D flow assumption, $f_{gl}$ is the only unknown in the TFM. Using a data-driven approach, we determine $f_{gl}$ and correlate it (via composite fits) with the liquid and gas Reynolds numbers, $Re_{l}$ and $Re_{g}$, respectively, and the Suratman number $Su_{l}$. To validate the proposed $f_{gl}(Re_{l},Re_{g},Su_{l})$ closure, we perform two-dimensional transient simulations at microgravity conditions using ANSYS Fluent and employing an Euler--Euler formulation. We find good agreement between the simulations based on the proposed $f_{gl}$ closure and the experimental data.

physics.flu-dyn↗

arXiv2023

Hydrodynamics of bubble flow through a porous medium with applications to packed bed reactors

Gas-liquid flows through packed bed reactors (PBRs) are challenging to predict due to the tortuous flow paths that fluid interfaces must traverse. Experiments at the International Space Station showed that bubble and pulse flows are predominately observed under microgravity conditions, while the trickle and spray flows observed under terrestrial conditions are not present in microgravity. To understand the physics behind the former experiments, we simulate bubble flow through a PBR for different packing-particle-diameter-based Weber numbers and under different gravity conditions. We demonstrate different pore-scale mechanisms, such as capillary entrapment, buoyancy entrapment, and inertia-induced bubble displacement. Then, we perform a quantitative analysis by introducing new dynamic scales, dependent upon the evolving gas-liquid interfacial area, to understand the dynamic trade-offs between the inertia, capillary, and buoyancy forces on a bubble passing through a PBR. This analysis leads us to define new dimensionless Weber-like numbers that delineate bubble entrapment from bubble displacement.

physics.flu-dyn↗

arXiv2023

Data-driven rheological characterization of stress buildup and relaxation in thermal greases

Thermal greases, often used as thermal interface materials, are complex paste-like mixtures composed of a base polymer in which dense metallic (or ceramic) filler particles are dispersed to improve the heat transfer properties of the material. They have complex rheological properties that impact the performance of the thermal interface material over its lifetime. We perform rheological experiments on thermal greases and observe both stress relaxation and stress buildup regimes. This time-dependent rheological behavior of such complex fluid-like materials is not captured by steady shear-thinning models often used to describe these materials. We find that thixo-elasto-visco-plastic (TEVP) and nonlinear-elasto-visco-plastic (NEVP) constitutive models characterize the observed stress relaxation and buildup regimes respectively. Specifically, we use the models within a data-driven approach based on physics-informed neural networks (PINNs). PINNs are used to solve the inverse problem of determining the rheological model parameters from the dynamic response in experiments. This training data is generated by startup flow experiments at different (constant) shear rates using a shear rheometer. We validate the ``learned'' models by comparing their predicted shear stress evolution to experiments under shear rates not used in the training datasets. We further validate the learned TEVP model by solving a forward problem numerically to determine the shear stress evolution for an input step-strain profile. Meanwhile, the NEVP model is further validated by comparison to a steady Herschel--Bulkley fit of the material's flow curve.

cond-mat.soft↗

arXiv2021

Two-fluid modeling of heat transfer in flows of dense suspensions

We develop a two-fluid model (TFM) for heat transfer in dense non-Brownian suspensions. Specifically, we propose closure relations for the inter-phase heat transfer coefficient and the thermal diffusivity of the particle phase based on calibration against experimental data. The model is then employed to simulate non-isothermal flow in an annular Couette cell. We find that, when the shear rate is controlled by the rotation of the inner cylinder, both the shear and thermal gradients are responsible for particle migration. Within the TFM framework, we identify the origin and functional form of a "thermo-rheological" migration force that rationalizes our observations. Furthermore, we apply our model to flow in eccentric Couette cells. Our simulations reveal that the system's heat transfer coefficient is affected by both the classic shear-induced migration of particles and the newly identified thermo-rheological migration effect.

physics.flu-dyn↗

arXiv2019

A two-fluid model for numerical simulation of shear-dominated suspension flows

Suspension flows are ubiquitous in nature (hemodynamics, subsurface fluid mechanics, etc.) and industrial applications (hydraulic fracturing, CO$_2$ storage, etc.). However, such flows are notoriously difficult to model due to the variety of fluid-particle and particle-particle interactions that can occur. In this work, we focus on non-Brownian shear-dominated suspensions, where kinetic collisions are negligible and frictional effects play a dominant role. Under these circumstances, irreversible phenomena such as particle diffusion and migration arise, requiring anisotropic stress models to describe the suspension rheology. On a continuum level, reduced-order models such as the suspension balance model (SBM) or the diffusive flux model are commonly used to predict particle migration phenomena. We propose a new method based on a two-fluid model (TFM), where both the phases are considered as interpenetrating continua with their own conservation of mass and momentum equations. Without employing the nowadays customary simplifications in applying the SBM, we close the ``full'' TFM instead. Specifically, we show that when an anisotropic stress analogous to that used in the SBM is added to the equilibrium equations for the particle phase, the TFM is able to accurately predict particle migration. Thus, the TFM does not require the assumptions of a steady suspension velocity and a Stokesian (inertialess) fluid, and the TFM can be easily extended to include buoyancy and even kinetic collisional models. We present several benchmark simulations of our TFM implementation in OpenFOAM{\textsuperscript\textregistered}, including in curvilinear coordinates and three-dimensional flow. Good agreement between the TFM solutions and previous experimental and numerical results is found.

physics.flu-dyn↗