Polymer solutions are frequently used in enhanced oil recovery and groundwater remediation to improve the recovery of trapped nonaqueous fluids. However, applications are limited by an incomplete understanding of the flow in porous media. The tortuous pore structure imposes both shear and extension, which elongates polymers; moreover, the flow is often at large Weissenberg numbers, Wi, at which polymer elasticity in turn strongly alters the flow. This dynamic elongation can even produce flow instabilities with strong spatial and temporal fluctuations despite the low Reynolds number, Re. Unfortunately, macroscopic approaches are limited in their ability to characterize the pore‐scale flow. Thus, understanding how polymer conformations, flow dynamics, and pore geometry together determine these nontrivial flow patterns and impact macroscopic transport remains an outstanding challenge. This review describes how microfluidic tools can shed light on the physics underlying the flow of polymer solutions in porous media at high Wi and low Re. Specifically, microfluidic studies elucidate how steady and unsteady flow behavior depends on pore geometry and solution properties, and how polymer‐induced effects impact nonaqueous fluid recovery. This work thus provides new insights for polymer dynamics, non‐Newtonian fluid mechanics, and applications such as enhanced oil recovery and groundwater remediation.
Soft hydraulics: from Newtonian to complex fluid flows through compliant conduits
Abstract Microfluidic devices manufactured from soft polymeric materials have emerged as a paradigm for cheap, disposable and easy-to-prototype fluidic platforms for integrating chemical and biological assays and analyses. The interplay between the flow forces and the inherently compliant conduits of such microfluidic devices requires careful consideration. While mechanical compliance was initially a side-effect of the manufacturing process and materials used, compliance has now become a paradigm, enabling new approaches to microrheological measurements, new modalities of micromixing, and improved sieving of micro- and nano-particles, to name a few applications. This topical review provides an introduction to the physics of these systems. Specifically, the goal of this review is to summarize the recent progress towards a mechanistic understanding of the interaction between non-Newtonian (complex) fluid flows and their deformable confining boundaries. In this context, key experimental results and relevant applications are also explored, hand-in-hand with the fundamental principles for their physics-based modeling. The key topics covered include shear-dependent viscosity of non-Newtonian fluids, hydrodynamic pressure gradients during flow, the elastic response (deformation and bulging) of soft conduits due to flow within, the effect of cross-sectional conduit geometry on the resulting fluid–structure interaction, and key dimensionless groups describing the coupled physics. Open problems and future directions in this nascent field of soft hydraulics, at the intersection of non-Newtonian fluid mechanics, soft matter physics, and microfluidics, are noted.
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- Award ID(s):
- 1705637
- NSF-PAR ID:
- 10303122
- Date Published:
- Journal Name:
- Journal of Physics: Condensed Matter
- Volume:
- 34
- Issue:
- 6
- ISSN:
- 0953-8984
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
BACKGROUND Optical sensing devices measure the rich physical properties of an incident light beam, such as its power, polarization state, spectrum, and intensity distribution. Most conventional sensors, such as power meters, polarimeters, spectrometers, and cameras, are monofunctional and bulky. For example, classical Fourier-transform infrared spectrometers and polarimeters, which characterize the optical spectrum in the infrared and the polarization state of light, respectively, can occupy a considerable portion of an optical table. Over the past decade, the development of integrated sensing solutions by using miniaturized devices together with advanced machine-learning algorithms has accelerated rapidly, and optical sensing research has evolved into a highly interdisciplinary field that encompasses devices and materials engineering, condensed matter physics, and machine learning. To this end, future optical sensing technologies will benefit from innovations in device architecture, discoveries of new quantum materials, demonstrations of previously uncharacterized optical and optoelectronic phenomena, and rapid advances in the development of tailored machine-learning algorithms. ADVANCES Recently, a number of sensing and imaging demonstrations have emerged that differ substantially from conventional sensing schemes in the way that optical information is detected. A typical example is computational spectroscopy. In this new paradigm, a compact spectrometer first collectively captures the comprehensive spectral information of an incident light beam using multiple elements or a single element under different operational states and generates a high-dimensional photoresponse vector. An advanced algorithm then interprets the vector to achieve reconstruction of the spectrum. This scheme shifts the physical complexity of conventional grating- or interference-based spectrometers to computation. Moreover, many of the recent developments go well beyond optical spectroscopy, and we discuss them within a common framework, dubbed “geometric deep optical sensing.” The term “geometric” is intended to emphasize that in this sensing scheme, the physical properties of an unknown light beam and the corresponding photoresponses can be regarded as points in two respective high-dimensional vector spaces and that the sensing process can be considered to be a mapping from one vector space to the other. The mapping can be linear, nonlinear, or highly entangled; for the latter two cases, deep artificial neural networks represent a natural choice for the encoding and/or decoding processes, from which the term “deep” is derived. In addition to this classical geometric view, the quantum geometry of Bloch electrons in Hilbert space, such as Berry curvature and quantum metrics, is essential for the determination of the polarization-dependent photoresponses in some optical sensors. In this Review, we first present a general perspective of this sensing scheme from the viewpoint of information theory, in which the photoresponse measurement and the extraction of light properties are deemed as information-encoding and -decoding processes, respectively. We then discuss demonstrations in which a reconfigurable sensor (or an array thereof), enabled by device reconfigurability and the implementation of neural networks, can detect the power, polarization state, wavelength, and spatial features of an incident light beam. OUTLOOK As increasingly more computing resources become available, optical sensing is becoming more computational, with device reconfigurability playing a key role. On the one hand, advanced algorithms, including deep neural networks, will enable effective decoding of high-dimensional photoresponse vectors, which reduces the physical complexity of sensors. Therefore, it will be important to integrate memory cells near or within sensors to enable efficient processing and interpretation of a large amount of photoresponse data. On the other hand, analog computation based on neural networks can be performed with an array of reconfigurable devices, which enables direct multiplexing of sensing and computing functions. We anticipate that these two directions will become the engineering frontier of future deep sensing research. On the scientific frontier, exploring quantum geometric and topological properties of new quantum materials in both linear and nonlinear light-matter interactions will enrich the information-encoding pathways for deep optical sensing. In addition, deep sensing schemes will continue to benefit from the latest developments in machine learning. Future highly compact, multifunctional, reconfigurable, and intelligent sensors and imagers will find applications in medical imaging, environmental monitoring, infrared astronomy, and many other areas of our daily lives, especially in the mobile domain and the internet of things. Schematic of deep optical sensing. The n -dimensional unknown information ( w ) is encoded into an m -dimensional photoresponse vector ( x ) by a reconfigurable sensor (or an array thereof), from which w′ is reconstructed by a trained neural network ( n ′ = n and w′ ≈ w ). Alternatively, x may be directly deciphered to capture certain properties of w . Here, w , x , and w′ can be regarded as points in their respective high-dimensional vector spaces ℛ n , ℛ m , and ℛ n ′ .more » « less
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Summary In this paper, a three‐dimensional numerical solver is developed for suspensions of rigid and soft particles and droplets in viscoelastic and elastoviscoplastic (EVP) fluids. The presented algorithm is designed to allow for the first time three‐dimensional simulations of inertial and turbulent EVP fluids with a large number particles and droplets. This is achieved by combining fast and highly scalable methods such as an FFT‐based pressure solver, with the evolution equation for non‐Newtonian (including EVP) stresses. In this flexible computational framework, the fluid can be modeled by either Oldroyd‐B, neo‐Hookean, FENE‐P, or Saramito EVP models, and the additional equations for the non‐Newtonian stresses are fully coupled with the flow. The rigid particles are discretized on a moving Lagrangian grid, whereas the flow equations are solved on a fixed Eulerian grid. The solid particles are represented by an immersed boundary method with a computationally efficient direct forcing method, allowing simulations of a large numbers of particles. The immersed boundary force is computed at the particle surface and then included in the momentum equations as a body force. The droplets and soft particles on the other hand are simulated in a fully Eulerian framework, the former with a level‐set method to capture the moving interface and the latter with an indicator function. The solver is first validated for various benchmark single‐phase and two‐phase EVP flow problems through comparison with data from the literature. Finally, we present new results on the dynamics of a buoyancy‐driven drop in an EVP fluid.
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SUMMARY The occurrence of plate tectonics on Earth is rooted in the physics of lithospheric ductile weakening and shear-localization. The pervasiveness of mylonites at lithospheric shear zones is a key piece of evidence that localization correlates with reduction in mineral grain size. Most lithospheric mylonites are polymineralic and the interaction between mineral phases, such as olivine and pyroxene, especially through Zener pinning, impedes normal grain growth while possibly enhancing grain damage, both of which facilitate grain size reduction and weakening, as evident in lab experiments and field observations. The efficacy of pinning, however, relies on the mineral phases being mixed and dispersed at the grain scale, where well-mixed states lead to greater mylonitization. To model grain mixing between different phases at the continuum scale, we previously developed a theory treating grain-scale processes as diffusion between phases, but driven by imposed compressive stresses acting on the boundary between phases. Here we present a new model for shearing rock that combines our theory for diffusive grain mixing, 2-D non-Newtonian flow and two-phase grain damage. The model geometry is designed specifically for comparison to torsional shear-deformation experiments. Deformation is either forced by constant velocity or constant stress boundary conditions. As the layer is deformed, mixing zones between different mineralogical units undergo enhanced grain size reduction and weakening, especially at high strains. For constant velocity boundary experiments, stress drops towards an initial piezometric plateau by a strain of around 4; this is also typical of monophase experiments for which this initial plateau is the final steady state stress. However, polyphase experiments can undergo a second large stress drop at strains of 10–20, and which is associated with enhanced phase mixing and resultant grain size reduction and weakening. Model calculations for polyphase media with grain mixing and damage capture the experimental behaviour when damage to the interface between phases is moderately slower or less efficient than damage to the grain boundaries. Other factors such as distribution and bulk fraction of the secondary phase, as well as grain-mixing diffusivity also influence the timing of the second stress drop. For constant stress boundary conditions, the strain rate increases during weakening and localization. For a monophase medium, there is theoretically one increase in strain rate to a piezometric steady state. But for the polyphase model, the strain rate undergoes a second abrupt increase, the timing for which is again controlled by interface damage and grain mixing. The evolution of heterogeneity through mixing and deformation, and that of grain size distributions also compare well to experimental observations. In total, the comparison of theory to deformation experiments provides a framework for guiding future experiments, scaling microstructural physics to geodynamic applications and demonstrates the importance of grain mixing and damage for the formation of plate tectonic boundaries.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1361-648X/ac327d
