254 research outputs found
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Mesoscopic simulations of hydrocarbon flow in source shales are challenging,
in part due to the heterogeneous shale pores with sizes ranging from a few
nanometers to a few micrometers. Additionally, the sub-continuum fluid-fluid
and fluid-solid interactions in nano- to micro-scale shale pores, which are
physically and chemically sophisticated, must be captured. To address those
challenges, we present a GPU-accelerated package for simulation of flow in
nano- to micro-pore networks with a many-body dissipative particle dynamics
(mDPD) mesoscale model. Based on a fully distributed parallel paradigm, the
code offloads all intensive workloads on GPUs. Other advancements, such as
smart particle packing and no-slip boundary condition in complex pore
geometries, are also implemented for the construction and the simulation of the
realistic shale pores from 3D nanometer-resolution stack images. Our code is
validated for accuracy and compared against the CPU counterpart for speedup. In
our benchmark tests, the code delivers nearly perfect strong scaling and weak
scaling (with up to 512 million particles) on up to 512 K20X GPUs on Oak Ridge
National Laboratory's (ORNL) Titan supercomputer. Moreover, a single-GPU
benchmark on ORNL's SummitDev and IBM's AC922 suggests that the host-to-device
NVLink can boost performance over PCIe by a remarkable 40\%. Lastly, we
demonstrate, through a flow simulation in realistic shale pores, that the CPU
counterpart requires 840 Power9 cores to rival the performance delivered by our
package with four V100 GPUs on ORNL's Summit architecture. This simulation
package enables quick-turnaround and high-throughput mesoscopic numerical
simulations for investigating complex flow phenomena in nano- to micro-porous
rocks with realistic pore geometries
Cell mechanics in flow: algorithms and applications
The computer simulations are pervasively used to improve the knowledge about biophysical phenomena and to quantify effects which are difficult to study experimentally. Generally, the numerical methods and models are desired to be as accurate as possible on the chosen length and time scales, but, at the same time, affordable in terms of computations. Until recently, the cell mechanics and blood flow phenomena on the sub-micron resolution could not be rigorously studied using computer simulations. However, within the last decade, advances in methods and hardware catalyzed the development of models for cells mechanics and blood flow modeling which, previously, were considered to be not feasible. In this context, a model should accurately describe a phenomenon, be computationally affordable, and be flexible to be applied to study different biophysical changes. This thesis focuses on the development of the new methods, models, and high-performance software implementation that expand the class of problems which can be studied numerically using particle-based methods. Microvascular networks have complex geometry, often without any symmetry, and to study them we need to tackle computational domains with several inlets and outlets. However, an absence of appropriate boundary conditions for particle- based methods hampers study of the blood flow in these domains. Another obstacle to model complex blood flow problems is the absence the highperformance software. This problem restricts the applicability of the of particlebased cell flow models to relatively small systems. Although there are several validated red blood cell models, to date, there are no models for suspended eukaryotic cells. The present thesis addresses these issues. We introduce new open boundary conditions for particle-based systems and apply them to study blood flow in a part of a microvascular network. We develop a software demonstrating outstanding performance on the largest supercomputers and used it to study blood flow in microfluidic devices. Finally, we present a new eukaryotic cell model which helps in quantifying the effect of sub-cellular components on the cell mechanics during deformations in microfluidic devices
Bridging the computational gap between mesoscopic and continuum modeling of red blood cells for fully resolved blood flow
We present a computational framework for the simulation of blood flow with
fully resolved red blood cells (RBCs) using a modular approach that consists of
a lattice Boltzmann solver for the blood plasma, a novel finite element based
solver for the deformable bodies and an immersed boundary method for the
fluid-solid interaction. For the RBCs, we propose a nodal projective FEM
(npFEM) solver which has theoretical advantages over the more commonly used
mass-spring systems (mesoscopic modeling), such as an unconditional stability,
versatile material expressivity, and one set of parameters to fully describe
the behavior of the body at any mesh resolution. At the same time, the method
is substantially faster than other FEM solvers proposed in this field, and has
an efficiency that is comparable to the one of mesoscopic models. At its core,
the solver uses specially defined potential energies, and builds upon them a
fast iterative procedure based on quasi-Newton techniques. For a known
material, our solver has only one free parameter that demands tuning, related
to the body viscoelasticity. In contrast, state-of-the-art solvers for
deformable bodies have more free parameters, and the calibration of the models
demands special assumptions regarding the mesh topology, which restrict their
generality and mesh independence. We propose as well a modification to the
potential energy proposed by Skalak et al. 1973 for the red blood cell
membrane, which enhances the strain hardening behavior at higher deformations.
Our viscoelastic model for the red blood cell, while simple enough and
applicable to any kind of solver as a post-convergence step, can capture
accurately the characteristic recovery time and tank-treading frequencies. The
framework is validated using experimental data, and it proves to be scalable
for multiple deformable bodies
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