Multiscale parareal algorithm for long-time mesoscopic simulations of microvascular blood flow in zebrafish

Blumers, Ansel L.; Yin, Minglang; Nakajima, Hiroyuki; Hasegawa, Yosuke; Li, Zhen; Karniadakis, George Em

doi:10.1007/s00466-021-02062-w

Cited by 9 publications

(5 citation statements)

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“…Detailed parallelisation strategies for particle-laden LB simulations have been published previously [288,312,314,[319][320][321][322]. Recent breakthroughs in the parallelisation of dissipative particle dynamics codes [323][324][325][326][327] also provide insights for further improving the parallel performance of existing LB codes in simulating particle-laden flows. Particularly noteworthy is lbmpy [328], a metaprogramming system for automatic code generation for parallel LB simulations.…”

Section: Parallelisation and Grid Refinement Strategiesmentioning

confidence: 99%

Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review

Owen

Kechagidis

Bazaz

et al. 2023

Preprint

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Inertial particle microfluidics (IPMF) is an emerging technology for the manipulation and separation of microparticles and biological cells. Since the flow physics of IPMF is complex and experimental studies are often time-consuming or costly, computer simulations can offer complementary insights. In this tutorial review, we provide a guide for researchers who are exploring the potential of the lattice-Boltzmann (LB) method for simulating IPMF applications. We first review the existing literature to establish the state of the art of LB-based IPMF modelling. After summarising the physics of IPMF, we then present related methods used in LB models for IPMF and show several case studies of LB simulations for a range of IPMF scenarios. Finally, we conclude with an outlook and several proposed research directions.

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Section: Parallelisation and Grid Refinement Strategiesmentioning

confidence: 99%

Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review

Owen

Kechagidis

Bazaz

et al. 2023

Preprint

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“…Emerging cell-resolved models of blood flow using advanced mesoscopic methods [4,[17][18][19][20] have been extensively applied since the late 2010s to simulate multiscale haemodynamics in synthetic or realistic vascular networks. These simulations have greatly improved our understanding of microscopic processes in the blood stream mediated by the flowing RBCs within, e.g.…”

Section: Introductionmentioning

confidence: 99%

Red blood cell dynamics in extravascular biological tissues modelled as canonical disordered porous media

Zhou¹,

Schirrmann

Doman

et al. 2022

Interface Focus.

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The dynamics of blood flow in the smallest vessels and passages of the human body, where the cellular character of blood becomes prominent, plays a dominant role in the transport and exchange of solutes. Recent studies have revealed that the microhaemodynamics of a vascular network is underpinned by its interconnected structure, and certain structural alterations such as capillary dilation and blockage can substantially change blood flow patterns. However, for extravascular media with disordered microstructure (e.g. the porous intervillous space in the placenta), it remains unclear how the medium’s structure affects the haemodynamics. Here, we simulate cellular blood flow in simple models of canonical porous media representative of extravascular biological tissue, with corroborative microfluidic experiments performed for validation purposes. For the media considered here, we observe three main effects: first, the relative apparent viscosity of blood increases with the structural disorder of the medium; second, the presence of red blood cells (RBCs) dynamically alters the flow distribution in the medium; third, symmetry breaking introduced by moderate structural disorder can promote more homogeneous distribution of RBCs. Our findings contribute to a better understanding of the cell-scale haemodynamics that mediates the relationship linking the function of certain biological tissues to their microstructure.

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“…Following this domain-decomposition strategy, several works that combine continuum and microscopic models have been proposed [18,37,38,47,58,59,74,76,85] to model multiscale systems. Another approach focuses on developing a fast surrogate as the fine-scale model represented by homogenization [7,9,22,82,90,96]. For example, [53] considered an approximation model of a partial differential equation (PDE) that contains small-scale oscillations in its coefficients, in essence, replacing these coefficients in the model with effective properties so that the resulting solutions can adequately approximate the solutions of the original problem.…”

Section: Introductionmentioning

confidence: 99%

Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems

Yin¹,

Zhang²,

Yu³

et al. 2022

Preprint

Self Cite

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Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are coupled together for predicting the system's response. The solver with lower fidelity (coarse) is responsible for simulating domains with homogeneous features, whereas the expensive high-fidelity (fine) model describes microscopic features with refined discretization, often making the overall cost prohibitively high, especially for timedependent problems. In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver. DeepONet is trained offline using data acquired from the fine solver for learning the underlying and possibly unknown finescale dynamics. It is then coupled with standard PDE solvers for predicting the multiscale systems with new boundary/initial conditions in the coupling stage. The proposed framework significantly reduces the computational cost of multiscale simulations since the DeepONet inference cost is negligible, facilitating readily the incorporation of a plurality of interface conditions and coupling schemes. We present various benchmarks to assess accuracy and speedup, and in particular we develop a coupling algorithm for a timedependent problem, and we also demonstrate coupling of a continuum model (finite element methods, FEM) with a neural operator representation of a particle system (Smoothed Particle Hydrodynamics, SPH) for a uniaxial tension problem with hyperelastic material. What makes this approach unique is that a well-trained over-parametrized DeepONet can generalize well and make predictions at a negligible cost.

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Multiscale parareal algorithm for long-time mesoscopic simulations of microvascular blood flow in zebrafish

Cited by 9 publications

References 50 publications

Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review

Lattice-Boltzmann Modelling for Inertial Particle Microfluidics Applications — A Tutorial Review

Red blood cell dynamics in extravascular biological tissues modelled as canonical disordered porous media

Interfacing Finite Elements with Deep Neural Operators for Fast Multiscale Modeling of Mechanics Problems

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