Parallel uniform mesh multiplication applied to a Navier–Stokes solver

Houzeaux, Guillaume; Cruz, R. de la; Owen, Herbert; Vázquez, Mariano

doi:10.1016/j.compfluid.2012.04.017

Cited by 48 publications

(51 citation statements)

References 8 publications

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“…The original mesh is made of a bit more than 6M elements. As in the other two examples, the original mesh goes through two and three subdivision cycles following [13], reaching 427 millions and 3.4 billions tetrahedra respectively. Figure 13 shows the strong scalability and efficiency for the cardiac electromechanical model.…”

Section: The Electro-mechamical Cardiac Model -Explicit Non-linear Somentioning

confidence: 98%

“…In this problem, [13], four different levels of mesh subdivision have been considered, referred to here as h = 1, 1/2, 1/4, 1/8, from the coarsest to the finest, respectively. The numbers of elements are 8.25M , 66.0M , 528M and 4.22B, respectively.…”

Section: The Kiln Furnace -Implicit Incompressible Navier-stokes Chementioning

confidence: 99%

“…In all cases we start with meshes in the range of a few million elements, which are progressively subdivided in parallel using the algorithm described in [13], in order to produce sufficiently large meshes to feed a large count of cores. As noted also in other works [23,16], an automatic local mesh refinement tool should be a key feature in any engineering simulation tool.…”

Section: Sparse Matrix-vector Productmentioning

confidence: 99%

“…In particularly, the latter is a key tool for large-scale simulations [13]. Alya is organized in a modular way: kernel, services and modules, which can be separately compiled and linked.…”

mentioning

confidence: 99%

“…Alya general view. Alya (see for instance [30,15,12,5,17]) is a simulation code developed at Barcelona Supercomputing Center (BSC-CNS) since 2004, whose main architects are authors GH and MV. Alya is not a born-sequential simulation code which was parallelized afterwards.…”

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confidence: 99%

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Alya: Multiphysics engineering simulation toward exascale

Vázquez

Houzeaux

Korić

et al. 2016

Journal of Computational Science

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188

111

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Abstract. Alya is a multi-physics simulation code developed at Barcelona Supercomputing Center (BSC). From its inception Alya code is designed using advanced High Performance Computing programming techniques to solve coupled problems on supercomputers efficiently. The target domain is engineering, with all its particular features: complex geometries and unstructured meshes, coupled multi-physics with exotic coupling schemes and physical models, ill-posed problems, flexibility needs for rapidly including new models, etc. Since its beginnings in 2004, Alya has scaled well in an increasing number of processors when solving single-physics problems such as fluid mechanics, solid mechanics, acoustics, etc. Over time, we have made a concerted effort to maintain and even improve scalability for multi-physics problems. This poses challenges on multiple fronts, including: numerical models, parallel implementation, physical coupling models, algorithms and solution schemes, meshing process, etc. In this paper, we introduce Alya's main features and focus particularly on its solvers. We present Alya's performance up to 100.000 processors in Blue Waters, the NCSA supercomputer with selected multi-physics tests that are representative of the engineering world. The tests are incompressible flow in a human respiratory system, low Mach combustion problem in a kiln furnace, and coupled electro-mechanical contraction of the heart. We show scalability plots for all cases and discuss all aspects of such simulations, including solver convergence.Key words. Multi-physics coupling, Parallelisation, Computational Mechanics 1. Introduction. Across a range of engineering fields, the use of computational models is pervasive in the whole design and manufacturing process. In complex systems, High Performance Computing (HPC) plays an essential role in simulation and modelling. Researchers and manufacturing teams depend on HPC to create safe cars and energy-efficient aircraft as well as effective communication systems and efficient supply chain models. Availability of advanced HPC technologies has also fundamentally altered the investigative paradigm in the field of biomechanics. But paradoxically, for many engineers and researchers, the existing hardware and software cannot be used to solve their problems. There are many reasons why this happens, but we focus here in only two. On one hand, current HPC systems lack the computational power, network bandwidth and data storage needed for solving tomorrow's real-world engineering challenges. On the other hand, while emerging peta-scale computing is already a strategic enabler of large-scale simulations in many scientific areas (such as astronomy, biology and chemistry), even the most powerful hardware will fail to deliver on its full potential unless matched with simulation software designed specifically for such environments.Several papers describe the effort of performing large-scale simulations on supercomputers, covering key areas: molecular dynamics [26], mantle convection in solid earth dynami...

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Section: The Electro-mechamical Cardiac Model -Explicit Non-linear Somentioning

confidence: 98%

Section: The Kiln Furnace -Implicit Incompressible Navier-stokes Chementioning

confidence: 99%

Section: Sparse Matrix-vector Productmentioning

confidence: 99%

“…In particularly, the latter is a key tool for large-scale simulations [13]. Alya is organized in a modular way: kernel, services and modules, which can be separately compiled and linked.…”

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Alya: Multiphysics engineering simulation toward exascale

Vázquez

Houzeaux

Korić

et al. 2016

Journal of Computational Science

Self Cite

188

111

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Fully coupled fluid‐electro‐mechanical model of the human heart for supercomputers

Santiago

Aguado-Sierra

Zavala-Aké

et al. 2018

Numer Methods Biomed Eng

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106

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In this work, we present a fully coupled fluid-electro-mechanical model of a 50th percentile human heart. The model is implemented on Alya, the BSC multi-physics parallel code, capable of running efficiently in supercomputers. Blood in the cardiac cavities is modeled by the incompressible Navier-Stokes equations and an arbitrary Lagrangian-Eulerian (ALE) scheme. Electrophysiology is modeled with a monodomain scheme and the O'Hara-Rudy cell model. Solid mechanics is modeled with a total Lagrangian formulation for discrete strains using the Holzapfel-Ogden cardiac tissue material model. The three problems are simultaneously and bidirectionally coupled through an electromechanical feedback and a fluid-structure interaction scheme. In this paper, we present the scheme in detail and propose it as a computational cardiac workbench.

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Some methods of training radial basis neural networks in solving the Navier‐Stokes equations

Sinchev¹,

Sibanbayeva

Mukhanova

et al. 2017

Numerical Methods in Fluids

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Summary The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving 2‐dimensional Navier‐Stokes equations. The authors developed an algorithm for solving hydrodynamic equations with representation of their solution by the method of weighted residuals upon the general neural network approximation throughout the entire computational domain. The article deals with testing of the developed algorithm through solving the 2‐dimensional Navier‐Stokes equations. Artificial neural networks are widely used for solving problems of mathematical physics; however, their use for modeling of hydrodynamic problems is very limited. At the same time, the problem of hydrodynamic modeling can be solved through neural network modeling, and our study demonstrates an example of its solution. The choice of neural networks based on radial basis functions is due to the ease of implementation and organization of the training process, the accuracy of the approximations, and smoothness of solutions. Radial basis neural networks in the solution of differential equations in partial derivatives allow obtaining a sufficiently accurate solution with a relatively small size of the neural network model. The authors propose to consider the neural network as an approximation of the unknown solution of the equation. The Gaussian distribution is used as the activation function.

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Parallel uniform mesh multiplication applied to a Navier–Stokes solver

Cited by 48 publications

References 8 publications

Alya: Multiphysics engineering simulation toward exascale

Alya: Multiphysics engineering simulation toward exascale

Fully coupled fluid‐electro‐mechanical model of the human heart for supercomputers

Some methods of training radial basis neural networks in solving the Navier‐Stokes equations

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