This work describes simulations of a red blood cell (RBC) in simple shear flow, focusing on the dependence of the cell dynamics on the spontaneous curvature of the membrane. The results show that an oblate spheroidal spontaneous curvature maintains the dimple of the RBC during tank-treading dynamics as well as exhibits off-shear-plane tumbling consistent with the experimental observations of Dupire et al. [J. Dupire, M. Socol, and A. Viallat, Proc. Natl. Acad. Sci. USA 109, 20808 (2012)] and their hypothesis of an inhomogeneous spontaneous shape. As the flow strength (capillary number Ca) is increased at a particular viscosity ratio between inner and outer fluid, the dynamics undergo transitions in the following sequence: tumbling, kayaking or rolling, tilted tank-treading, oscillating-swinging, swinging, and tank-treading. The tilted tank-treading (or spinning frisbee) regime has been previously observed in experiments but not in simulations. Two distinct classes of regime are identified: a membrane reorientation regime, where the part of membrane that is at the dimple at rest moves to the rim and vice versa, is observed in motions at high Ca such as tilted tank-treading, oscillating-swinging, swinging, and tank-treading, and a nonreorientation regime, where the part of the membrane starting from the dimple stays at the dimple, is observed in motions at low Ca such as rolling, tumbling, kayaking, and flip-flopping.

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We present detailed simulations and theory for flow-induced segregation in suspensions of deformable fluid-filled capsules with different shapes during simple shear flow in a planar slit. This system is an idealized model for transport for blood cells and/or drug carriers in the microcirculation or in microfluidic devices. For the simulations, an accelerated implementation of the boundary integral method was employed. We studied the binary mixtures of spherical and ellipsoidal capsules, varying the aspect ratio κ of the ellipsoid while keeping constant either (a) equatorial radius or (b) volume. Effects of a variety of parameters was studied, including κ, volume fraction and number fraction of the spherical capsules in the mixture. In suspensions where the ellipsoids have the same equatorial radius as the spheres, capsules with lower κ marginate. In suspension where the ellipsoids have the same volume as the spheres, ellipsoidal (both oblate and prolate) capsules are seen to demarginate in a mixture of primarily spherical capsules. To understand these results, a mechanistic framework based on the competition between wall-induced migration and shear-induced collisions is presented. A simplified drift-diffusion theory based on this framework shows excellent qualitative agreement with simulation results.

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A mechanistic theory is developed to describe segregation in confined multicomponent suspensions such as blood. It incorporates the two key phenomena arising in these systems at low Reynolds number: hydrodynamic pair collisions and wall-induced migration. In simple shear flow, several regimes of segregation arise, depending on the value of a "margination parameter" M. Most importantly, there is a critical value of M below which a sharp "drainage transition" occurs: one component is completely depleted from the bulk flow to the vicinity of the walls. Direct simulations also exhibit this transition as the size or flexibility ratio of the components changes.Introduction. Flow-induced segregation is ubiquitous in multicomponent suspensions and granular materials, including systems as disparate as hard macroscopic particles in air [1], polydisperse droplet suspensions [2], foams [3], and blood. During blood flow, the focus of the present work, both the leukocytes and platelets segregate near the vessel walls, a phenomenon known as margination, while the red blood cells (RBCs) tend to be depleted in the near-wall region, forming a so-called cell-free or depletion layer [4]. Engineering the margination process has been proposed for microfluidic cell separations in blood (e.g. [5]) as well as for enhanced drug delivery to the vasculature [6].Direct simulations of flowing multicomponent suspensions -models of blood -can capture margination phenomena [7][8][9][10][11][12][13][14][15][16], but developing a fundamental understanding of underlying mechanisms and parameterdependence from simulations is difficult. It is thus important to have a simple yet mechanistic mathematical model, ideally one with closed form solutions that reveal parameter-dependence, that can distill out the essential phenomena that drive segregation and capture the key effects and transitions. We present such a model here.Theory. We consider a dilute suspension containing N s types of deformable particles with total volume fraction φ undergoing flow in a slit bounded by no-slip walls at y = 0 and y = 2H and unbounded in x and z. Quantities referring to a specific component α in the mixture will have subscript α: for example n α is the number density of component α. We consider here only simple shear (plane Couette) flow and, consistent with the diluteness assumption, take the shear rateγ to be independent of the local number densities and thus independent of position. In a dilute suspension of particles, where φ ≪ 1, the particle-particle interactions can be treated as a sequence of uncorrelated pair collisions [17][18][19]. For the moment, we neglect molecular diffusion of the particles. This issue is further addressed below. Since the particles are deformable, they migrate away from the wall during flow with velocity v αm (y) [20,21]. The evolution of the particle number density distributions can be idealized by a kinetic master equation that captures the migration

Transient simulations of dynamic systems, using physics-based scientific computing tools, are practically limited by availability of computational resources and power. While the promise of machine learning has been explored in a variety of scientific disciplines, its application in creation of a framework for computationally expensive transient models has not been fully explored. Here, we present an ensemble approach where one such computationally expensive tool, discrete element method, is combined with time-series forecasting via auto regressive integrated moving average and machine learning methods to simulate a complex pharmaceutical problem: development of an agitation protocol in an agitated filter dryer to ensure uniform solid bed mixing. This ensemble approach leads to a significant reduction in the computational burden, while retaining model accuracy and performance, practically rendering simulations possible. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing. Machine learning has emerged as one of the most promising technologies in the past decade due to its capability to provide valuable insights 1 into vast amounts of data generated during the Internet era. Rapid democratization of machine learning tools has allowed for the successful adoption of the technology in a wide range of fields including robotics, computer vision 2 , speech and natural language processing 3 , autonomous driving 4 , neuroscience, drug-discovery 5 and in fundamental sciences 6. However, its application to computational sciences, and applied computational physics in general, has been limited. Prior efforts to apply machine learning to computational sciences have primarily focused on steady state problems which are more tractable. However, applications of machine learning to time-variant problems are rare. Over the past decade, a tremendous growth in computational power, easily accessed through cloud computing platforms, has been observed. Even then, simulations based on first-principles models of natural systems and, in particular, time-variant problems of these systems remain prohibitively expensive for most practical applications. First-principles models refers to the models that are based on the physical laws such as Newton's laws of motion and are not merely data-driven. Many of these models, such as molecular dynamics (MD) 7 used for enhancing understanding of molecular arrangements, computational fluid dynamics (CFD) 8 used for understanding flow patterns for both gas and liquid phase, density functional theory (DFT) 9 used for understanding electronic (or nuclear) structure, discrete element methods (DEM) 10 used for understanding motion of particulate systems and, last but not the least, finite element method (FEM) 11 used to measure the structural strength of materials, have immense potential to accelerate research and ultimately change the world around us. Advances in the field of ML and artificial intelligenc...

Precompetitive collaborations on new enabling technologies for research and development are becoming popular among pharmaceutical companies. The Enabling Technologies Consortium (ETC), a precompetitive collaboration of leading innovative pharmaceutical companies, identifies and executes projects, often with third-party collaborators, to develop new tools and technologies of mutual interest. Here, we report the results of one of the first ETC projects: the development of a user-friendly population balance model (PBM)-based crystallization simulator software. This project required the development of PBM software with integrated experimental data handling, kinetic parameter regression, interactive process simulation, visualization, and optimization capabilities incorporated in a computationally efficient and robust software platform. Inputs from a team of experienced scientists at 10 ETC member companies helped define a set of software features that guided a team of crystallization modelers to develop software incorporating these features. Communication, continuous testing, and feedback between the ETC and the academic team facilitated the software development. The product of this project, a software tool called CrySiV, an acronym for Crystallization Simulation and Visualization, is reported herein. Currently, CrySiV can be used for cooling, antisolvent, and combined cooling and antisolvent crystallization processes, with primary and secondary nucleation, growth, dissolution, agglomeration, and breakage of crystals. This paper describes the features and the numerical methods of the software and presents two case studies demonstrating its use for parameter estimation. In the first case study, a simulated data set is used to demonstrate the capabilities of the software to find kinetic parameters and its goodness of fit to a known solution. In the second case study, the kinetics of an antisolvent crystallization of indomethacin from a ternary solvent system are estimated, providing a practical example of the tool.

Crystal engineering has advanced the strategies of design and synthesis of organic solids with the main focus being on customising the properties of the materials.