Shear Thickening of Dense Suspensions: The Role of Friction

Shear thickening of particle suspensions is characterized by a transition between lubricated and frictional contacts between the particles. Using 3D numerical simulations, we study how the inter-particle friction coefficient influences the effective macroscopic friction coefficient and hence the microstructure and rheology of dense shear thickening suspensions. We propose expressions for effective friction coefficient in terms of distance to jamming for varying shear stresses and particle friction coefficient values. We find effective friction coefficient to be rather insensitive to interparticle friction, which is perhaps surprising but agrees with recent theory and experiments

Mesoscopic Methods in Engineering and Science

(First paragraph) Matter, conceptually classified into fluids and solids, can be completely described by the microscopic physics of its constituent atoms or molecules. However, for most engineering applications a macroscopic or continuum description has usually been sufficient, because of the large disparity between the spatial and temporal scales relevant to these applications and the scales of the underlying molecular dynamics. In this case, the microscopic physics merely determines material properties such as the viscosity of a fluid or the elastic constants of a solid. These material properties cannot be derived within the macroscopic framework, but the qualitative nature of the macroscopic dynamics is usually insensitive to the details of the underlying microscopic interactions

Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots

Parametric reduced-order modelling often serves as a surrogate method for hemodynamics simulations to improve the computational efficiency in many-query scenarios or to perform real-time simulations. However, the snapshots of the method require to be collected from the same discretisation, which is a straightforward process for physical parameters, but becomes challenging for geometrical problems, especially for those domains featuring unparameterised and unique shapes, e.g. patient-specific geometries. In this work, a data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. The proposed surrogate model leverages group surface registration to parameterise those shapes and formulates corresponding hemodynamics information into geometry-informed snapshots by the diffeomorphisms constructed between a reference domain and original domains. A non-intrusive reduced-order model for geometrical parameters is subsequently constructed using proper orthogonal decomposition, and a radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model based on compressed geometrical parameters of the shape. Two examples of blood flowing through a stenosis and a bifurcation are presented and analysed. The proposed surrogate model demonstrates its accuracy and efficiency in hemodynamics prediction and shows its potential application toward real-time simulation or uncertainty quantification for complex patient-specific scenarios

Cellular Level In-silico Modeling of Blood Rheology with An Improved Material Model for Red Blood Cells

Many of the intriguing properties of blood originate from its cellular nature. Therefore, accurate modeling of blood flow related phenomena requires a description of the dynamics at the level of individual cells. This, however, presents several computational challenges that can only be addressed by high performance computing. We present Hemocell, a parallel computing framework which implements validated mechanical models for red blood cells and is capable of reproducing the emergent transport characteristics of such a complex cellular system. It is computationally capable of handling large domain sizes, thus it is able to bridge the cell-based micro-scale and macroscopic domains. We introduce a new material model for resolving the mechanical responses of red blood cell membranes under various flow conditions and compare it with a well established model. Our new constitutive model has similar accuracy under relaxed flow conditions, however, it performs better for shear rates over 1,500 s−1. We also introduce a new method to generate randomized initial conditions for dense mixtures of different cell types free of initial positioning artifacts

Another face of Lorenz-Mie scattering: monodisperse distributions of spheres produce Lissajous-like patterns

The complete scattering matrix S of spheres was measured with a flow cytometer. The experimental equipment allows simultaneous detection of two scattering-matrix elements for every sphere in the distribution. Two-parameter scatterplots withx andy coordinates determined by the Sll + Sij and S11 - Sij values are measured. Samples of spheres with very narrow size distributions (< 1%) were analyzed with a FlowCytometer, and they produced unexpected two-parameter scatterplots. Instead of compact distributions we observed Lissajous-like loops. Simulation of the scatterplots, using Lorenz-Mie theory, shows that these loops are due not to experimental errors but to true Lorenz-Mie scattering. It is shown that the loops originate from the sensitivity of the scattered field on the radius of the spheres. This paper demonstrates that the interpretation of rare events and hidden features in flow cytometry needs reconsideration