Bulletin of the American Physical Society
77th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 24–26, 2024; Salt Lake City, Utah
Session X19: Non-Newtonian Flows IV: Rheology |
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Chair: Pranay Nagrani, Purdue University Room: 250 C |
Tuesday, November 26, 2024 8:00AM - 8:13AM |
X19.00001: Using interfacial rheology to understand the mechanism of Alzheimer’s disease Monica Feijo Naccache, Henrique Uchôa, Jorge Peixinho, Boris Leon, Eliana M Castaño, Priscilla R Varges, Xavier Colin, Nicolás A Rey, Paulo R de Souza Mendes In Alzheimer's disease, amyloid plaques and, predominantly, extracellular Amyloid-beta (A-beta) oligomers are important byomarkers associated with neuronal cells loss and dysfunction. Some experiments in the literature demonstrates that A-beta peptides induce significative changes in membrane organization, reducing stiffness and ultimately forming pores that contribute to the damage and death of neuronal cells. Understanding the interaction between A-beta and neuronal lipid membranes is crucial for the study of Alzheimer’s disease and drug development. This work aims to quantify the interactions, binding mechanisms and insertion of A-beta peptides into lipid membranes, using interfacial rheology and microscopy techniques. Initially, we used a Langmuir trough to measure the surface pressure at the interface of a model lipid layer (DPPC) at the air-water interface. Shear interfacial rheology employing a double wall ring was also used to obtain the surface viscosity, while cryo-SEM visualizations are performed to examine the microstructure of the compounds and interfaces formed. Upon introducing A-beta into the water phase system, a decrease in surface pressure, and a weekening effect on interfacial viscosity were observed. The study includes experiments with varying concentrations of A-beta and different temperatures. Additionally, we investigate the effect of a novel organic compound at the interface to assess its potential for restoring the mechanical properties of the interface. |
Tuesday, November 26, 2024 8:13AM - 8:26AM |
X19.00002: The critical Plasto-Capillary number and minimum feature size in embedded 3D printing Mohammad Tanver Hossain, Wonsik Eom, Vidush Parasramka, Douglas Fudge, Sameh H Tawfick, Randy H Ewoldt Embedded 3D printing enables freeform manufacturing of intricate components by suspending an extruded ink material in a non-Newtonian yield-stress fluid bath, enabling fabrication of small, delicate, and soft structures not achievable by other means. While it is known that interfacial capillary effects should limit the minimum feature size, there is persistent disagreement in the published literature between the theoretical plasto-capillary length d = 2Γ/σy, set by a balance between interfacial tension Γ and bath yield stress σy, and the experimentally observed minimum feature sizes. Although this has been rationalized by adjusting the apparent value of interfacial tension Γ, here we introduce and experimentally test the hypothesis that the critical diameter is set by the dimensionless Plasto-Capaillary number, Pl = σyd/(2Γ), having a non-trivial critical value different than one, and therefore there is no need to adjust Γ. We study several Newtonian inks (uncured polydimethylsiloxane (PDMS), highly refined mineral oil, silicone oil) extruded into a wide range of non-Newtonian viscoplastic bath materials (polyacrylic acid microgels, polysaccharide microgels, nanoclay gel, and micro-organogels). Across this wide parameter space, we observe a critical value of Pl = σyd/(2Γ) = 0.21±0.03. We explain this being less than one by analogy to other critical dimensionless groups with yield stress fluids, such as the gravitational stability of a suspended sphere or bubble where the effective area is larger than the naïve estimate set only by diameter d. These results provide a new way to determine the minimum feature size based on the rheological properties involved in embedded 3D printing, as d = 0.42 (Γ/σy). |
Tuesday, November 26, 2024 8:26AM - 8:39AM |
X19.00003: Abstract Withdrawn
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Tuesday, November 26, 2024 8:39AM - 8:52AM |
X19.00004: Viscoelastic thin film lubrication in finite width channels Luca Biancofiore, Humayun Ahmed Polymer enhanced lubricants exhibit elasticity, enabling them to stretch over long distances. In thin film lubrication, their relaxation time approaches the short residence time scales within the thin channel, the ratio referred to as the Deborah number (De), leading to the onset of viscoelasticity. The polymer’s elasticity generates finite streamwise normal stresses (zero for a Newtonian lubricant) that can potentially increase the film’s load bearing capability. Using reduced-order models, we observe in sliding contacts with infinite width (neglecting then all spanwise variation in pressure, velocity and stress) a noticeable increase in the load and change in the pressure distribution as De increases, owing to these finite normal stresses. Here, we consider thin lubricated channels, having a finite length-to-width or aspect ratio (a). Using the Oldroyd-B constitutive relation to model the viscoelastic stresses, we predict the pressure distribution along the contact and the maximum load carrying capacity of the film (F), varying the Deborah number and the aspect ratio. We find the same beneficial increase observed in the infinite-width case. Interestingly, F, that is zero for a Newtonian lubricant due to asymmetric pressure profile along the channel, varies strongly versus the aspect ratio for each Deborah number. |
Tuesday, November 26, 2024 8:52AM - 9:05AM |
X19.00005: Abstract Withdrawn
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Tuesday, November 26, 2024 9:05AM - 9:18AM |
X19.00006: Experimental measurement of 2D strain hardening reveals local yield stress distribution of a Carbopol gel Frederic Blanc, Guillaume Ovarlez, Romain Mari, Kirsten Martens, Adam Trigui Yield stress fluids (YSFs) are ubiquitous in everyday life, such as gels, fresh cement, food emulsions, and clays. They flow only above their yield threshold. The complexity of these materials lies in their combination of elastic and plastic properties of solids with the viscous properties of fluids. While steady-state features are reasonably well understood, the response to complex flow histories, common in applications like pumping, mixing, or under vibration, is less known. In these complex deformation protocols, it is crucial to consider the full tensorial character of the mechanical response. |
Tuesday, November 26, 2024 9:18AM - 9:31AM |
X19.00007: Recent Advances In Polymer Viscoelasticity From General Rigid Bead-Rod Theory Alan Jeffrey Giacomin One good way to explain the elasticity of a polymeric liquid, is to just consider the |
Tuesday, November 26, 2024 9:31AM - 9:44AM |
X19.00008: Rheology of mRNA Loaded Lipid Nanodumbbells Mona Kanso, Shalini Singh, Alan Jeffrey Giacomin, Alan Jeffrey Giacomin, Richard Dean Braatz In one important chemical engineering unit operation of messenger ribonucleic acid (mRNA) vaccine manufacture, the precious mRNA payload is encapsulated in lipid nanoparticles. Recent elegant cryogenic-transmission electron microscopy [Biophys J 120, 2766 (2021)] reveals these lipid nanoparticles take the form of dumbbell suspensions. When encapsulating their mRNA payloads, these dumbbells can be both lopsided and interpenetrating, with the smaller of the two beads carrying the payload. In this work, we arrive at analytical expressions for these suspensions of lopsided lipid nanoparticle dumbbells encapsulating mRNA payloads. For this, we exploit rigid dumbbell theory [Appl Sci Res, 30, 268 (1975)], which relies on the orientation distributions of the lopsided dumbbells to predict the suspension rheology, and specifically to predict how this departs from Newtonian behavior. Our results include analytical expressions for the relaxation time, rotational diffusivity, zero-shear viscosity, shear stress relaxation function, steady-shear viscosity and both the viscous part and minus the elastic part of the complex viscosity. |
Tuesday, November 26, 2024 9:44AM - 9:57AM |
X19.00009: Deep ensembles assisted multiscale modeling of rheologically complex fluids Bhargav Sriram Siddani, Weiqun Zhang, Andrew J Nonaka, Ishan Srivastava Continuum modeling of complex fluid dynamics without an explicitly-known constitutive rheology, such as the relationship between viscosity and local fluid flow state, relies on a multiscale methodology wherein particle-based simulations are used to either precompute the entire rheological space, or inform the local rheology for every fluid cell at each time step of the continuum fluid simulation. Both these approaches can quickly become computationally intractable, particularly since the rheological space to sample can be very large and is often a priori unknown in most scenarios. This study leverages ensemble deep learning to obtain an uncertainty quantified approximation of the intrinsic constitutive rheology. Our multiscale method reduces the need for numerous expensive particle-based simulations by adaptively focusing on the under-sampled rheological spaces with high-variance. Hence, reducing the computational expense and making the multiscale problem tractable. |
Tuesday, November 26, 2024 9:57AM - 10:10AM |
X19.00010: Differentiable simulations of viscoelastic fluids for developing digital rheometers Alp M Sunol, Mohammed Alhashim, Kaylie Hausknecht, Henry S Bae, James V Roggeveen, Joseph L Holey, Michael P Brenner Viscoelastic flows are widely encountered in a diverse array of industrial and biophysical processes. However, accurately modeling these flows via constitutive models remains a significant challenge. The advent of differentiable simulation methods leveraging automatic differentiation offers a promising avenue for improved computational models and addressing inverse design problems in viscoelastic fluids. In this work, we introduce a fully-differentiable computational framework incorporating a viscoelastic fluid solver implemented with JAX. This framework enables data-driven parametrization of known constitutive equations, the discovery of optimal models, and the identification of additional corrective terms to existing constitutive relations. Our approach accommodates arbitrary flow conditions and is not limited to specific experimental setups or model choices. By embedding the training of the model within the fluid solver, we can incorporate data from a multitude of flow conditions, allowing us to characterize the intrinsic fluid properties that generalize across various scenarios. |
Tuesday, November 26, 2024 10:10AM - 10:23AM |
X19.00011: Uncertainty quantification of rheological properties of soft materials under shear Pranay P Nagrani, Akash Mattupalli, Akshay J Thomas, Ivan C. Christov 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. From a microscopic point of view, these rheological behaviors arise from the particles' inhomogeneous mixing or separation/settling over time. Previous literature has used deterministic approaches to extract values of the rheological parameters of such complex soft solids, assuming a particular model for stress evolution. Specifically, stress relaxation and buildup in sheared commercial thermal greases were successfully captured using a thixotropic-elasto-visco-plastic (TEVP) and a nonlinear elasto-visco-plastic (NEVP) model, respectively. However, the previous model calibration methods ignored parameter and model uncertainty arising from epistemic and aleatoric sources. Therefore, in this study, we use statistical methods, specifically a state-of-the-art hierarchical Bayesian inference methodology, to obtain distributions of the parameters in the rheological models. We further propagate these uncertainties through the rheological models to obtain uncertainties within the transient shear stress distributions at different imposed shear rates. Our approach provides a systematic way to understand model identifiability and, therefore, provides a more robust way of empirical model selection for thixotropic soft solids of unknown microstructure. |
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