arXiv subjects

Arezoo M. Ardekani

Publications and source records attributed to Arezoo M. Ardekani.

At least 19 recordsLinked to original sources

arXiv2026

Mixing Performance of Toroidal Ring Mixers: Effects of Flow Rate Ratios and Geometric Asymmetry

Microfluidic mixing is important for nanoparticle fabrication, where rapid contact between the solvent and nonsolvent streams is needed to control the formation process. Various micromixer geometries have been developed and analyzed to improve mixing efficiency. However, for toroidal micromixers, the role of flow rate ratio and geometric asymmetry has not been examined in detail. In this study, the mixing process of two toroidal micromixer designs is investigated, namely symmetric and asymmetric, with emphasis on the impact of flow rate ratio and geometric asymmetry during the mixing of miscible fluids. Numerical simulations are carried out to examine the mixing behavior of these toroidal micromixers for different flow rates and flow rate ratios. High-fidelity numerical simulations are performed using the stabilized finite element method. The concentration and velocity fields are used to examine how the chamber asymmetry can affect the mixing performance. Experiments are also conducted to provide a validation for the numerical results. We demonstrate that the asymmetric toroidal mixer design generally improves the mixing over the conventional design, especially at low to moderate total flow rates. The results obtained show that improved mixing can be achieved without changing the overall mixer size.

physics.flu-dyn↗

arXiv2026

Lagrangian evaluation of polymeric stress in viscoelastic fluids

Polymeric stresses in viscoelastic flows arise from the deformation of polymer chains and are commonly computed using Eulerian constitutive models, in which the conformation tensor is evolved as a transported field over the entire domain. This approach is computationally intensive, prone to numerical instabilities, and not directly applicable to experimentally measured velocity fields. In this work, we develop a Lagrangian integration scheme that reconstructs the polymeric stress field from the deformation-gradient history along fluid element trajectories in a known, steady velocity field. This approach avoids solving the full Eulerian constitutive transport equation, which we develop for the nonlinear FENE-P model as well as the Oldroyd-B model as a reference case. After validation on unidirectional, canonical flows, the scheme is applied to non-trivial channel flows past circular obstacles using velocity fields quantified from both numerical simulations and microfluidic experiments. The reconstructed stress fields across both experiments and simulations are in agreement with traditional Eulerian reference solutions. Not only does this new Lagrangian scheme enable the quantification of stress fields directly from experimental velocity field data, but it also enables partial or whole-field mapping of stresses without solving fully-coupled viscoelastic constitutive equations.

physics.flu-dyn↗

arXiv2025

Complex Pressure Node Formation and Resonances Induced by Scatterers in a Standing-Wave Acoustic Cavity

Acoustic pressure nodes in acoustophoretic devices are crucial for applications in tissue engineering, cell analysis, and particle trapping. Typically, a single primary node forms at the half-wavelength resonance condition, while its shape and position are constrained by the dimensions of the channel. The generation of additional nodes, along with control over their positions and shapes, is highly desirable in biomedical applications and could significantly enhance particle manipulation capabilities. To explore this potential, we numerically demonstrate the formation of additional, complex-shaped, nodes alongside the primary one, by a circular scatterer within a rectangular cavity. We identify three distinct types: ring, protuberant, and crescent nodes, whose formation depends on the size of the scatterer, its placement in the middle of the channel, and its corresponding resonant frequency. The key mechanism behind this formation is the enhancement of internal reflections, increasing destructive interference to promote node generation. To achieve this, we employ three key concepts: using a low-aspect ratio channel, positioning a rigid circular scatterer in the middle of the channel, and modeling all surfaces as perfect reflectors. Furthermore, we analyze the impact of the scatterer on acoustic pressure and quality factor, which is defined as the ratio of stored acoustic energy to damped acoustic energy per cycle. We show that additional nodes emerge in the presence of a scatterer, but they come at the cost of reduced acoustic pressure and energy in the channel. In summary, this study provides information on generating complex nodes in a standing wave acoustic cavity at the fundamental frequency, which has numerous applications for particle manipulation in acoustofluidic devices.

physics.flu-dyn↗

arXiv2025

Rheology of concentrated suspension of fibers with load dependent friction coefficient

We numerically investigate the effects of fiber aspect ratio, roughness, flexibility, and flow inertia on the rheology of concentrated suspensions. We perform direct numerical simulations modeling the fibers, suspended in an incompressible Newtonian fluid, as continuous flexible slender bodies obeying the Euler-Bernoulli beam equation. An immersed Boundary Method (IBM) is employed to solve for the motion of fibers. In concentrated suspensions, fibers come into contact due to the presence of asperities on their surface. We assume a normal load-dependent friction coefficient to model contact dynamics and friction, which successfully recovers the shear-thinning behaviour observed in experiments. First, we report the shear rate dependent behavior and the increase in the suspension viscosity with the increasing volume fraction of fibers, fiber roughness, and rigidity. The increase in the viscosity is stronger at a lower shear rate and finite inertia. Simulation results indicate that for the concentrated suspensions, contact stresses between fibers form the dominant contribution to the viscosity. Moreover, we find the first normal stress difference to be positive and to increase with the Reynolds number for flexible fibers. Lastly, we explore the divergence of viscosity for different aspect ratios and roughness of the fibers and predict the jamming volume fraction by fitting the data to the Maron-Pierce law. The jamming volume fraction decreases with increasing the aspect ratio and roughness. We conclude that the contact forces and interparticle friction become one of the crucial factors governing the rheology of flexible fiber suspensions at high concentrations.

physics.flu-dyn↗

arXiv2025

Orientation instability of settling spheroids in a linearly density stratified fluid

Much work has been done to understand the settling dynamics of spherical particles in a homogeneous and a stratified fluid. However, the effects of shape anisotropy on the settling dynamics of a particle in a stratified fluid are not completely understood. To this end, we perform numerical simulations for settling oblate and prolate spheroids in a stratified fluid. We present the results for the Reynolds number, $Re$, in the range $80-250$ and the Richardson number, $Ri$, in the range $0-10$. We find that both the oblate and prolate spheroids reorient to the edge-wise and partially edge-wise orientations, respectively, as they settle in a stratified fluid completely different from the steady-state broad-side on orientation observed in a homogeneous fluid. We observe that reorientation instabilities emerge when the velocity magnitude of the spheroids fall below a particular threshold. We also report the enhancement of the drag on the particle from stratification. The torque due to buoyancy effects tries to orient the spheroid in an edge-wise orientation while the hydrodynamic torque tries to orient it to a broad-side on orientation. Below the velocity threshold, the buoyancy torque dominates; resulting in the onset of reorientation instability. Finally, the asymmetry in the distribution of the baroclinic vorticity generation term around the spheroids explains the onset of the reorientation instability.

physics.flu-dyn↗

arXiv2024

Machine learning framework to predict the performance of lipid nanoparticles for nucleic acid delivery

Lipid nanoparticles (LNPs) are highly effective carriers for gene therapies, including mRNA and siRNA delivery, due to their ability to transport nucleic acids across biological membranes, low cytotoxicity, improved pharmacokinetics, and scalability. A typical approach to formulate LNPs is to establish a quantitative structure-activity relationship (QSAR) between their compositions and in vitro/in vivo activities which allows for the prediction of activity based on molecular structure. However, developing QSAR for LNPs can be challenging due to the complexity of multi-component formulations, interactions with biological membranes, and stability in physiological environments. To address these challenges, we developed a machine learning framework to predict the activity and cell viability of LNPs for nucleic acid delivery. We curated data from 6,398 LNP formulations in the literature, applied nine featurization techniques to extract chemical information, and trained five machine learning models for binary and multiclass classification. Our binary models achieved over 90% accuracy, while the multiclass models reached over 95% accuracy. Our results demonstrated that molecular descriptors, particularly when used with random forest and gradient boosting models, provided the most accurate predictions. Our findings also emphasized the need for large training datasets and comprehensive LNP composition details, such as constituent structures, molar ratios, nucleic acid types, and dosages, to enhance predictive performance.

q-bio.BM↗

arXiv2024

Swimmer-Types of Optimum Surface-Driven Active Particles

An optimal microswimmer with a given geometry has a surface velocity profile that minimizes energy dissipation for a given swimming speed. An axisymmetric swimmer can be puller-, pusher-, or neutral-type depending on the sign of the stresslet strength. We numerically investigate the type of optimal surface-driven active microswimmers using a minimum dissipation theorem for optimum microswimmers. We examine the hydrodynamic resistance and stresslet strength with nonlinear dependence on various deformation modes. The results show that microswimmers with fore-and-aft symmetry exhibit neutral-type behavior, while asymmetrical geometries exhibit pusher-, puller-type or neutral-type behavior, depending on the dominant deformation mode and the nonlinear dependence of the stresslet for an optimum microswimmer on deformation mode and amplitude.

physics.flu-dyn↗

arXiv2024

ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection

Viscoelastic fluids are a class of fluids that exhibit both viscous and elastic nature. Modelling such fluids requires constitutive equations for the stress, and choosing the most appropriate constitutive relationship can be difficult. We present viscoelasticNet, a physics-informed deep learning framework that uses the velocity flow field to select the constitutive model and learn the stress field. Our framework requires data only for the velocity field, initial \& boundary conditions for the stress tensor, and the boundary condition for the pressure field. Using this information, we learn the model parameters, the pressure field, and the stress tensor. {This work considers} three commonly used non-linear viscoelastic models: Oldroyd-B, Giesekus, and linear Phan-Thien-Tanner (PTT). We demonstrate that our framework works well with noisy and sparse data. Our framework can be combined with velocity fields acquired from experimental techniques like particle image velocimetry to get the pressure \& stress fields and model parameters for the constitutive equation. Once the model has been discovered using viscoelasticNet, the fluid can be simulated and modeled for further applications.

physics.flu-dyn↗

arXiv2024

Physics-Informed Neural Network based inverse framework for time-fractional differential equations for rheology

Time-fractional differential equations offer a robust framework for capturing intricate phenomena characterized by memory effects, particularly in fields like biotransport and rheology. However, solving inverse problems involving fractional derivatives presents notable challenges, including issues related to stability and uniqueness. While Physics-Informed Neural Networks (PINNs) have emerged as effective tools for solving inverse problems, most existing PINN frameworks primarily focus on integer-ordered derivatives. In this study, we extend the application of PINNs to address inverse problems involving time-fractional derivatives, specifically targeting two problems: 1) anomalous diffusion and 2) fractional viscoelastic constitutive equation. Leveraging both numerically generated datasets and experimental data, we calibrate the concentration-dependent generalized diffusion coefficient and parameters for the fractional Maxwell model. We devise a tailored residual loss function that scales with the standard deviation of observed data. We rigorously test our framework's efficacy in handling anomalous diffusion. Even after introducing 25% Gaussian noise to the concentration dataset, our framework demonstrates remarkable robustness. Notably, the relative error in predicting the generalized diffusion coefficient and the order of the fractional derivative is less than 10% for all cases, underscoring the resilience and accuracy of our approach. In another test case, we predict relaxation moduli for three pig tissue samples, consistently achieving relative errors below 10%. Furthermore, our framework exhibits promise in modeling anomalous diffusion and non-linear fractional viscoelasticity.

cs.NE↗

arXiv2024

Inverse resolution of spatially varying diffusion coefficient using Physics-Informed neural networks

Resolving the diffusion coefficient is a key element in many biological and engineering systems, including pharmacological drug transport and fluid mechanics analyses. Additionally, these systems often have spatial variation in the diffusion coefficient which must be determined, such as for injectable drug-eluting implants into heterogeneous tissues. Unfortunately, obtaining the diffusion coefficient from images in such cases is an inverse problem with only discrete data points. The development of a robust method that can work with such noisy and ill-posed datasets to accurately determine spatially-varying diffusion coefficients is of great value across a large range of disciplines. Here, we developed an inverse solver that uses physics informed neural networks (PINNs) to calculate spatially-varying diffusion coefficients from numerical and experimental image data in varying biological and engineering applications. The residual of the transient diffusion equation for a concentration field is minimized to find the diffusion coefficient. The robustness of the method as an inverse solver was tested using both numerical and experimental datasets. The predictions show good agreement with both the numerical and experimental benchmarks; an error of less than 6.31% was obtained against all numerical benchmarks, while the diffusion coefficient calculated in experimental datasets matches the appropriate ranges of other reported literature values. Our work demonstrates the potential of using PINNs to resolve spatially-varying diffusion coefficients, which may aid a wide-range of applications, such as enabling better-designed drug-eluting implants for regenerative medicine or oncology fields.

q-bio.QM↗

arXiv2023

Stress and stretching regulate dispersion in viscoelastic porous media flows

In this work, we study the role of viscoelastic instability in the mechanical dispersion of fluid flow through porous media at high Peclet numbers. Using microfluidic experiments and numerical simulations, we show that viscoelastic instability in flow through a hexagonally ordered (staggered) medium strongly enhances dispersion transverse to the mean flow direction with increasing Weissenberg number (Wi). In contrast, preferential flow paths can quench the elastic instability in disordered media, which has two important consequences for transport: First, the lack of chaotic velocity fluctuations reduces transverse dispersion relative to unstable flows. Second, the amplification of flow along preferential paths with increasing Wi causes strongly-correlated stream-wise flow that enhances longitudinal dispersion. Finally, we illustrate how the observed dispersion phenomena can be understood through the lens of Lagrangian stretching manifolds, which act as advective transport barriers and coincide with high stress regions in these viscoelastic porous media flows.

physics.flu-dyn↗

arXiv2022

Rheology of dense fiber suspensions: Origin of yield stress, shear thinning and normal stress differences

We explain the origins of yield stress, shear-thinning, and normal stress differences in rigid fiber suspensions. We investigate the interplay between the hydrodynamic, colloidal attractive and repulsive, and inter-fiber contact interactions. The shear-thinning viscosity and finite yield stress obtained from the computational model are in quantitative agreement with experiential results from the literature. In this study, we show that attractive interactions result in yield stress and shear-thinning rheology in the suspensions of rigid fibers. This is an important finding, given the ongoing discussion regarding the origin of the yield stress for suspensions of fibers. The ability of the proposed model to quantitatively predict the rheology is not limited to only shear thinning and yield stress but also extends to normal stresses.

physics.flu-dyn↗

arXiv2022

A constitutive model for viscosity of dense fiber suspension

We propose a constitutive model to predict the viscosity of fiber suspensions, which undergoes shear thinning, at various volume fractions, aspect ratios, and shear stresses/rates. We calibrate the model using the data from direct numerical simulation and prove the accuracy by predicting experimental measurements from the literature. We use a friction coefficient decreasing with the normal load between the fibers to quantitatively reproduce the experimentally observed shear thinning in fiber suspensions. In this model, the effective normal contact force, which is directly proportional to the bulk shear stress, determines the effective friction coefficient. A rise in the shear stress reduces the effective friction coefficient in the suspension. As a result, the jamming volume fraction increases with the shear stress, resulting in a shear thinning in the suspension viscosity. Moreover, we extend the model to quantify the effects of fiber volume fraction and aspect ratio in the suspension. We calibrate this model using the data from numerical simulations for the rate-controlled shear flow. Once calibrated, we show that the model can be used to predict the relative viscosity for different volume fractions, shear stresses, and aspect ratios. The model predictions are in excellent agreement with the available experimental measurements from the literature. The findings of this study can potentially be used to tune the fiber size and volume fraction for designing the suspension rheology in various applications.

physics.flu-dyn↗

arXiv2022

Lagrangian stretching reveals stress topology in viscoelastic flows

Viscoelastic flows are pervasive in a host of natural and industrial processes, where the emergence of nonlinear and time-dependent dynamics regulate flow resistance, energy consumption, and particulate dispersal. Polymeric stress induced by the advection and stretching of suspended polymers feeds back on the underlying fluid flow, which ultimately dictates the dynamics, instability, and transport properties of viscoelastic fluids. However, direct experimental quantification of the stress field is challenging, and a fundamental understanding of how Lagrangian flow structure regulates the distribution of polymeric stress is lacking. In this work, we show that the topology of the polymeric stress field precisely mirrors the Lagrangian stretching field, where the latter depends solely on flow kinematics. We develop a general analytical expression that directly relates the polymeric stress and stretching in weakly viscoelastic fluids for both nonlinear and unsteady flows, which is also extended to special cases characterized by strong kinematics. Furthermore, numerical simulations reveal a clear correlation between the stress and stretching field topologies for unstable viscoelastic flows across a broad range of geometries. Ultimately, our results establish a connection between the Eulerian stress field and the Lagrangian structure of viscoelastic flows. This work provides a simple framework to determine the topology of polymeric stress directly from readily measurable flow field data and lays the foundation for directly linking the polymeric stress to flow transport properties.

physics.flu-dyn↗

arXiv2022

Implementing contact angle boundary conditions for second-order Phase-Field models of wall-bounded multiphase flows

In the present work, a general formulation is proposed to implement the contact angle boundary conditions for the second-order Phase-Field models, which is applicable to $N$-phase $(N \geqslant 2)$ moving contact line problems. To remedy the issue of mass change due to the contact angle boundary condition, a source term or Lagrange multiplier is added to the original second-order Phase-Field models, which is determined by the consistent and conservative volume distribution algorithm so that the summation of the order parameters and the \textit{consistency of reduction} are not influenced. To physically couple the proposed formulation to the hydrodynamics, especially for large-density-ratio problems, the consistent formulation is employed. The reduction-consistent conservative Allen-Cahn models are chosen as examples to illustrate the application of the proposed formulation. The numerical scheme that preserves the consistency and conservation of the proposed formulation is employed to demonstrate its effectiveness. Results produced by the proposed formulation are in good agreement with the exact and/or asymptotic solutions. The proposed method captures complex dynamics of moving contact line problems having large density ratios.

physics.flu-dyn↗

arXiv2022

A consistent and conservative Phase-Field method for multiphase incompressible flows

A consistent and conservative Phase-Field method, including both the model and scheme, is developed for multiphase flows with an arbitrary number of immiscible and incompressible fluid phases. The consistency of mass conservation and the consistency of mass and momentum transport are implemented to address the issue of physically coupling the Phase-Field equation, which locates different phases, to the hydrodynamics. These two consistency conditions provide the ``optimal'' coupling because (i) the new momentum equation resulting from them is Galilean invariant and implies the kinetic energy conservation, regardless of the details of the Phase-Field equation, and (ii) failures of satisfying the second law of thermodynamics or the consistency of reduction of the multiphase flow model only result from the same failures of the Phase-Field equation but are not due to the new momentum equation. Physical interpretation of the consistency conditions and their formulations are first provided, and general formulations that are obtained from the consistency conditions and independent of the interpretation of the velocity are summarized. Several novel techniques are developed to inherit the physical properties of the multiphase flows after discretization, including the gradient-based phase selection procedure, the momentum conservative method for the surface force, and the general theorems to preserve the consistency conditions on the discrete level. Equipped with those novel techniques, a consistent and conservative scheme for the present multiphase flow model is developed and analyzed. Numerical applications demonstrate that the present model and scheme are robust and effective in studying complicated multiphase dynamics, especially for those with large-density ratios.

physics.comp-ph↗

arXiv2021

A consistent and conservative Phase-Field model for thermo-gas-liquid-solid flows including liquid-solid phase change

In the present study, a consistent and conservative Phase-Field model is developed to study thermo-gas-liquid-solid flows with liquid-solid phase change. The proposed model is derived with the help of the consistency conditions and exactly reduces to the consistent and conservative Phase-Field method for incompressible two-phase flows, the fictitious domain Brinkman penalization (FD/BP) method for fluid-structure interactions, and the Phase-Field model of solidification of pure material. It honors the mass conservation, defines the volume fractions of individual phases unambiguously, and therefore captures the volume change due to phase change. The momentum is conserved when the solid phase is absent, but it changes when the solid phase appears due to the no-slip condition at the solid boundary. The proposed model also conserves the energy, preserves the temperature equilibrium, and is Galilean invariant. A novel continuous surface tension force to confine its contribution at the gas-liquid interface and a drag force modified from the Carman-Kozeny equation to reduce solid velocity to zero are proposed. The issue of initiating phase change in the original Phase-Field model of solidification is addressed by physically modifying the interpolation function. The corresponding consistent scheme is developed to solve the model, and the numerical results agree well with the analytical solutions and the existing experimental and numerical data. Two challenging problems having a wide range of material properties and complex dynamics are conducted to demonstrate the capability of the proposed model.

physics.comp-ph↗

arXiv2021

Microswimming in viscoelastic fluids

The locomotion of microorganisms and spermatozoa in complex viscoelastic fluids is of critical importance in many biological processes such as fertilization, infection, and biofilm formation. Depending on their propulsion mechanisms, microswimmers display various responses to a complex fluid environment: increasing or decreasing their swimming speed and efficiency, modifying their propulsion kinematics and swimming gaits, and experiencing different hydrodynamic interactions with their surroundings. In this article, we review the fundamental physics of locomotion of biological and synthetic microswimmers in complex viscoelastic fluids. Starting from a continuum framework, we describe the main theoretical approaches developed to model microswimming in viscoelastic fluids, which typically rely on asymptotically small dimensionless parameters. We then summarise recent progress on the mobility of single cells propelled by cilia, waving flagella and rotating helical flagella in unbounded viscoelastic fluids. We next briefly discuss the impact of other physical factors, including the micro-scale heterogeneity of complex biological fluids, the role of Brownian fluctuations of the microswimmers, the effect of polymer entanglement and the influence of shear-thinning viscosity. In particular, for solutions of long polymer chains whose sizes are comparable to the radius of flagella, continuum models cannot be used and instead Brownian Dynamics for the polymers can predict the swimming dynamics. Finally, we discuss the effect of viscoelasticity on the dynamics of microswimmers in the presence of surfaces or external flows and its impact on collective cellular behavior.

cond-mat.soft↗