Partitioned Simulation of Fluid-Structure Interaction on Cartesian Grids

Bungartz, Hans‐Joachim; Benk, Janos; Gatzhammer, Bernhard; Mehl, Miriam; Neckel, Tobias

doi:10.1007/978-3-642-14206-2_10

Cited by 26 publications

(29 citation statements)

References 41 publications

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“…Important features desired of such mappings are accuracy, consistency, and conservation of energy and momentum. Two main classes of mapping methods can be identified: interpolation methods (Farhat et al, 1998b;de Boer et al, 2007;Jaiman et al, 2006;Bungartz et al, 2010;Scholz et al, 2006), based on geometric relations between the involved grid points, and mortar methods, in which boundary conditions are formulated in weak form by using Lagrange multipliers (Farhat et al, 1998b;Baaijens, 2001;Klöppel et al, 2011;Ross, 2006).…”

Section: Interaction Of Fluids and Structuresmentioning

confidence: 99%

Multiphysics simulations: challenges and opportunities.

Keyes¹,

McInnes²,

Woodward³

et al. 2012

153

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We consider multiphysics applications from algorithmic and architectural perspectives, where "algorithmic" includes both mathematical analysis and computational complexity and "architectural" includes both software and hardware environments. Many diverse multiphysics applications can be reduced, en route to their computational simulation, to a common algebraic coupling paradigm. Mathematical analysis of multiphysics coupling in this form is not always practical for realistic applications, but model problems representative of applications discussed herein can provide insight. A variety of software frameworks for multiphysics applications have been constructed and refined within disciplinary communities and executed on leading-edge computer systems. We examine several of these, expose some commonalities among them, and attempt to extrapolate best practices to future systems. From our study, we summarize challenges and forecast opportunities.

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Section: Interaction Of Fluids and Structuresmentioning

confidence: 99%

Multiphysics simulations: challenges and opportunities.

Keyes¹,

McInnes²,

Woodward³

et al. 2012

153

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show abstract

“…More detailed results for Peano and its flow solver can be found in [14,15,55]. Due to the sophisticated data handling based solely on streams and stacks, all applications implemented in Peano achieve a level 2 cachehit rate above 99%.…”

Section: Performance Datamentioning

confidence: 99%

“…preCICE (precice code interaction coupling environment) has been originally developed as a coupling tool for partitioned multiphysics applications [14]. The idea behind partitioned multiphysics simulations is to treat the solvers for the involved singlephysics phenomena (such as fluid flow, structural mechanics, acoustics, etc.)…”

Section: Precice Basicsmentioning

confidence: 99%

“…All geometry queries in preCICE are accelerated by spacetree algorithms (quadtrees in 2D, octrees in 3D) exploiting information inheritage from father to son cells of these spacetrees and, thus, substantially reducing the computational complexity [14]. The spacetrees are created dynamically according to the locations of incoming queries.…”

Section: Spacetree-supported Geometry Queriesmentioning

confidence: 99%

“…The new CFD solver on this basis has been implemented by Neckel [36] and applied to fluid-structure interaction scenarios [12,14] and simulations of a microscopic device for particle separation [13]. Due to the hierarchical structure of the involved computational grid, Peano is particularly suited for interactive simulations, i.e.…”

Section: Introductionmentioning

confidence: 99%

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Towards multi-phase flow simulations in the PDE framework Peano

et al. 2011

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In this work, we present recent enhancements and new functionalities of our flow solver in the partial differential equation framework Peano. We start with an introduction including an overview of the Peano development and a short description of the basic concepts of Peano and the flow solver in Peano concerning the underlying structured but adaptive Cartesian grids, the data structure and data access optimisation, and spatial and time discretisation of the flow solver. The new features cover geometry interfaces and additional application functionalities. The two geometry interfaces, a triangulation-based description supported by the tool preCI-CE and a built-in geometry using geometry primitives such as cubes, spheres, or tetrahedra allow for the efficient treatment of complex and changing geometries, an essential ingredient for most application scenarios. The new application functionality concerns a coupled heat-flow problem and two-phase flows. We present numerical examples, performance and validation results for these new functionalities.

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Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

Meng

Berzins

2013

Concurrency and Computation

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Uintah is a software framework that provides an environment for solving fluid-structure interaction problems on structured adaptive grids for large-scale science and engineering problems involving the solution of partial differential equations. Uintah uses a combination of fluid flow solvers and particle-based methods for solids, together with adaptive meshing and a novel asynchronous task-based approach with fully automated load balancing. When applying Uintah to fluid-structure interaction problems, the combination of adaptive meshing and the movement of structures through space present a formidable challenge in terms of achieving scalability on large-scale parallel computers. The Uintah approach to the growth of the number of core counts per socket together with the prospect of less memory per core is to adopt a model that uses MPI to communicate between nodes and a shared memory model on-node so as to achieve scalability on large-scale systems. For this approach to be successful, it is necessary to design data structures that large numbers of cores can simultaneously access without contention. This scalability challenge is addressed here for Uintah, by the development of new hybrid runtime and scheduling algorithms combined with novel lock-free data structures, making it possible for Uintah to achieve excellent scalability for a challenging fluid-structure problem with mesh refinement on as many as 260K cores. ‡ Titan is a parallel computer under construction at Oak Ridge National Laboratory with about 299K CPU cores now with a large number of attached GPUs to be added in 2012. § Stampede is a parallel computer under construction at Texas Advanced Computing Center with two petaflops of CPU performance and eight petaflops of accelerator performance. 1389This work will start to address some of these challenges by developing scheduling algorithms and associated software that will be used to tackle fluid-structure interaction algorithms in which mesh refinement is used to resolve the structure. The vehicle for this work is the Uintah Computational Framework [2][3][4]. The broad range and challenging nature of Uintah applications and the software itself are described in [5]. The Uintah code was designed to solve fluid-structure interaction problems arising from a number of physical scenarios. Uintah uses a combination of computational fluid dynamics (CFD) algorithms in its implicit continuous-fluid Eulerian (ICE) solver [6,7] and couples ICE to a particle-based solver for solids known as the material point method (MPM) [8,9]. The adaptive mesh refinement (AMR) approach used in Uintah is that of multiple levels of regularly refined mesh patches. This mesh is also used as a scratch pad for the MPM calculation of the movement and deformation of the solid [10,11]. Uintah has been shown to scale successfully with many fluid and solid problems with adaptive meshes [12,13]; however, the scalability of fluid-structure interaction problems has proven to be somewhat more challenging. This is at least partly because the part...

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Partitioned Simulation of Fluid-Structure Interaction on Cartesian Grids

Cited by 26 publications

References 41 publications

Multiphysics simulations: challenges and opportunities.

Multiphysics simulations: challenges and opportunities.

Towards multi-phase flow simulations in the PDE framework Peano

Scalable large‐scale fluid–structure interaction solvers in the Uintah framework via hybrid task‐based parallelism algorithms

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