Bulletin of the American Physical Society
74th Annual Meeting of the APS Division of Fluid Dynamics
Volume 66, Number 17
Sunday–Tuesday, November 21–23, 2021; Phoenix Convention Center, Phoenix, Arizona
Session A14: NonNewtonian Flows: Rheology 
Hide Abstracts 
Chair: Irmgard Bischofberger, MIT Room: North 128 AB 
Sunday, November 21, 2021 8:00AM  8:13AM 
A14.00001: Viscosity measurements of glycerol reveal the misalignment in parallelplate rheometers Jesse T Ault, Sangwoo Shin, Allan Garcia, Antonio Perazzo, Howard A Stone We consider the viscosity measurement of pure glycerol in a parallelplate rheometer over time. Due to the hygroscopic nature of glycerol, it spontaneously absorbs water vapor from the atmosphere through the outer edge of the rheometer, leading to a transient decrease in the measured viscosity. A combination of diffusion and advection drive the transport of the dissolved water through the rheometer, resulting in regions of lessviscous fluid. The measured viscosity then represents an integration of the shear stresses on the plate device. We find that the rate of decrease of the measured viscosity is a complex function of the gap thickness and angular velocity, as well as the vapor flux at the outer edge. The decrease in viscosity is a nonlinear function of the angular velocity. For small angular velocities the dynamics are diffusive, and the water only slowly proceeds inward from the outer edge of the glycerol, resulting in high concentrations in the outermost region. Because this low viscosity fluid is confined to a relatively small region, the decrease in measured viscosity is slow for this case. For cases with modest angular velocities, the recirculating advection acts to pull some of the water inwards from the outer boundary, steepening the gradient of concentration at the outer edge and increasing the flux of water vapor into the system, leading to a faster decrease in viscosity up to a point. For very fast angular velocities, much of the water is pulled all the way to the center of the rheometer, where the effect on the measured viscosity is relatively less (since the majority of the torque develops at the outer edge). Nevertheless, we see a deviation from these behaviors for very small gap heights (which should be purely diffusive), in which the measured viscosity drops much faster than expected. We propose that this is due to plate misalignment of the rheometer, and we use computational fluid dynamics simulations to support this hypothesis. 
Sunday, November 21, 2021 8:13AM  8:26AM 
A14.00002: Multifidelity modeling to predict the rheological properties of fiber suspensions Miad Boodaghidizaji, Monsurul Islam Khan, Arezoo M Ardekani Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications like biofuel production. The 3D numerical simulations of the suspension of fibers are often computationally expensive and timeconsuming. Machine learning methods such as neural networks can simplify the prediction of rheological behavior; however, they require a relatively large training data set. Multifidelity models, which combine highfidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified physical equations, can lead to optimized predictions. Here, we focus on a neural network with two levels of fidelity, i.e., high and low fidelity networks. To produce highfidelity data, we perform direct numerical simulations to model the fibers as onedimensional inextensible slender bodies that obey the Euler Bernoulli beam equation. The NavierStokes equations govern the suspended fluid, and an immersed boundary method is used to couple the fluid and solid motion. The lowfidelity data is produced by using constitutive equations. Noticeable improvements have been observed in the accuracy of predicting the rheological behavior when a multifidelity network is used compared to the singlefidelity network. 
Sunday, November 21, 2021 8:26AM  8:39AM 
A14.00003: Investigating the applicability of physicsbased machine learning algorithms to metamodeling of complex fluids Mohammadamin Mahmoudabadbozchelou, Safa Jamali We briefly present three types of physicsbased machine learning frameworks, to model, describe, and predict the behavior of complex fluids. In the area of datadriven constitutive metamodeling, we present RheologyInformed Neural Networks (RhINNs) and MultiFidelity Neural Network (MFNN), in which the physical intuition is being included explicitly and implicitly, respectively. We used RhINNs as an alternative solver for systems of Ordinary Differential Equations (ODEs) in a direct approach, and to learn the model/material parameters using a series of limited experimental data in an inverse platform. MFNN is also used as a datadriven constitutive metamodeling of complex fluids and compares its rheological predictions with those of a simple Deep NN and experimental measurements. Generation of the lowfidelity data points is done using the underlying rheological constitutive models, while the highfidelity network is trained on a limited number of experimentally measured data. We will discuss the MFNN predictions of a set of flow curves for a multicomponent colloidal and wormlike micelle solution with respect to temperature, salinity, and aging of the mixture. With regards to complex fluid flow modeling, nonNewtonian physicsinformed neural network (nnPINNs) is introduced for solving systems of coupled PDEs. nnPINNs are then employed to solve the constitutive models in conjunction with conservation of momentum while avoiding the mesh generation step, followed by validating for a number of different complex fluids with various constitutive models. These include a range of Generalized Newtonian Fluids empirical constitutive models and some phenomenological models with memory effects and thixotropic timescales, and for several flow protocols. We finally discuss the outlook and opportunities, and more importantly the limitations of sciencebased ML platforms in rheology and nonNewtonian fluid mechanics. 
Sunday, November 21, 2021 8:39AM  8:52AM 
A14.00004: Lagrangian and Eulerian signatures of inertioelastic instabilities and turbulence in polymer jets Sami Yamanidouzisorkhabi, Yashasvi Raj, Tamer A Zaki, Gareth H McKinley, Irmgard Bischofberger We report on the spatiotemporal evolution of flow structures in a jet of dilute polymer solution entering a quiescent bath of Newtonian fluid. Highspeed digital Schlieren imaging and laser Doppler velocimetry are used to study the Lagrangian and Eulerian signatures of the onset of inertioelastic instabilities and the subsequent transition to a turbulent flow state. Applying Dynamic Mode Decomposition to the Schlieren images reveals the dominant inertioelastic instability modes and their dependence on elasticity number and polymer extensibility. The advective growth of these inertioelastic modes results in the jet transitioning to a state of elastoinertial turbulence (EIT). Velocity measurements at fixed Eulerian locations along the center line of the jet show that EIT is characterized by a dramatic decrease in turbulence intensity with a distinctly different powerlaw spectrum as compared to a turbulent Newtonian jet. 
Sunday, November 21, 2021 8:52AM  9:05AM 
A14.00005: Torsional fracture of viscoelastic liquid bridges San To Chan, Frank P van Berlo, Hammad A Faizi, Atsushi Matsumotoa, Simon J Haward, Patrick D Anderson, Amy Q Shen Short liquid bridges are stable under the action of surface ten sion. In applications like electronic packaging, food engineering, and additive manufacturing, this poses challenges to the clean and fast dispensing of viscoelastic fluids. Here, we investigate how viscoelastic liquid bridges can be destabilized by torsion. By combining highspeed imaging and numerical simulation, we show that concave surfaces of liquid bridges can localize shear, in turn localizing normal stresses and making the surface more con cave. Such positive feedback creates an indent, which propagates toward the center and leads to breakup of the liquid bridge. The indent formation mechanism closely resembles edge fracture, an often undesired viscoelastic flow instability characterized by the sudden indentation of the fluid's free surface when the fluid is subjected to shear. By applying torsion, even short, capillary sta ble liquid bridges can be broken in the order of 1 s. This may lead to the development of dispensing protocols that reduce substrate contamination by the satellite droplets and long capillary tails formed by capillary retraction, which is the current mainstream industrial method for destabilizing viscoelastic liquid bridges. 
Sunday, November 21, 2021 9:05AM  9:18AM 
A14.00006: Coronavirus Pleomorphism and Rotational Diffusivity Mona Kanso, Alan Jeffrey Giacomin, Marwa Naime, Vikash Chaurasia, Eliot Fried The coronavirus is always idealized as a spherical capsid with radially protruding spikes. However, histologically, in the tissues of infected patients, capsids in cross section are elliptical, and only sometimes spherical. This capsid ellipticity implies that coronaviruses are oblate or prolate or both. We call this diversity of shapes, pleomorphism. Recently, the rotational diffusivity of the coronavirus in suspension was calculated, from first principles, using general rigid beadrod theory [M.A. Kanso, Phys Fluids, 32, 113101 (2020)]. We did so by beading the capsid, and then also by replacing each of its bulbous spikes with a single bead. We use energy minimization for the spreading of the spikes, charged identically, over the oblate or prolate capsids. We use general rigid beadrod theory to explore the role of coronavirus ellipticity on its rotational diffusivity, the transport property around which its cell attachment revolves. We learn that coronavirus ellipticity decreases its rotational diffusivity for both oblate and prolate ellipsoids. 
Sunday, November 21, 2021 9:18AM  9:31AM 
A14.00007: Experiments in nonNewtonian fluids: SaffmanTaylor instability pooja jangir, Ratan Mohan, Paresh Chokshi Viscous fingering instability can be controlled by adding polymers to either displacing or displaced fluid as polymers alter the fluid viscosity, the main cause of viscous fingering. Most of the polymeric solutions exhibit shearrate dependent viscosity and elasticity, simultaneously. The fluid rheology can affect the growth of fingers due to the nonuniform viscosity distribution and the presence of normal stresses. To investigate the role of rheological properties on the instability, experiments are performed using HeleShaw cell when one of the fluids is polymeric fluid. The aqueous solutions of polyethylene oxide (PEO) of different concentrations and molecular weights are used. The evolution of the fingers shows that displacement of PEO solutions of high concentration or high molecular weight leads to more complex and fractallike patterns involving tipsplitting and sidebranching mechanisms. The displacement when the displaced fluid is PEO solution shows more intensified patterns than the displacing PEO solution for similar viscosity contrast and rheological properties. It is observed that the shearthinning behavior always strengthens the shielding behavior whereas the fluid elasticity always leads to tipspitting, irrespective of the flow arrangement. 
Sunday, November 21, 2021 9:31AM  9:44AM 
A14.00008: Spreading of complex fluids with a soft blade. Marion Krapez, Anais Gauthier, JeanBaptiste Boitte, Christophe Kusina, Odile Aubrun, JeanFrançois Joanny, Annie Colin The spreading of complex fluids is a part of our everyday life: we spread jam, cosmetics, paint, etc. It is also central in industry, mostly to make paper or protective coatings. Here, we consider the spreading of polymer solutions with an elastic blade that is soft enough to deform during the spreading (similarly to a brush, a finger or rubber squeegees). We compare fluids with identical shearthinning rheology, but which generates – or not – normal stresses when flowing. This allows us to disentangle the effects of viscosity, shearthinning and normal stresses on the spreading process. We thus reveal two counterintuitive results: first, shearthinning does not significantly modify the film thickness compared with an equivalent Newtonian fluid, but increases the force that has to be applied to spread the liquid. In addition, the geometry of the experiment can make normal stresses negligible compared with the viscous forces even if they are dominant over the shear stress. We combine experiments, a numerical model and a scaling analysis to evidence and explain these observations. 
Sunday, November 21, 2021 9:44AM  9:57AM 
A14.00009: Experimental characterization of elastoinertial turbulence Sarath S. Suresh, George H Choueiri, Atul Varshney, Bjoern Hof

Follow Us 
Engage
Become an APS Member 
My APS
Renew Membership 
Information for 
About APSThe American Physical Society (APS) is a nonprofit membership organization working to advance the knowledge of physics. 
© 2023 American Physical Society
 All rights reserved  Terms of Use
 Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 207403844
(301) 2093200
Editorial Office
1 Research Road, Ridge, NY 119612701
(631) 5914000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 200452001
(202) 6628700