Racoon: A parallel mesh-adaptive framework for hyperbolic conservation laws

Dreher, J.; Grauer, Rainer

doi:10.1016/j.parco.2005.04.011

Cited by 59 publications

(56 citation statements)

References 32 publications

(37 reference statements)

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“…In order to reduce the overhead, some authors have suggested a block-wise approach to SAMR [6][7][8][9][10][11], with the purpose to avoid the clustering and grid-fitting steps in the Berger-Colella approach. The initial, coarse grid is divided into a number of blocks.…”

Section: Approaches To Structured Adaptive Mesh Refinementmentioning

confidence: 99%

“…Also the Racoon framework has been parallelized in two levels but using a hybrid of MPI and POSIXPthreads [6].…”

Section: Software Framework For Samrmentioning

confidence: 99%

“…In the other variant of block-wise SAMR a block is subdivided whenever it is refined [6,10,11]. The effect is that the number of blocks grows but the number of grid points per block is constant.…”

Section: Dynamic Load Balancingmentioning

confidence: 99%

“…Since all blocks have the same number of mesh points, all level l blocks will have the same workload, and equally sized partitions of the sequence of blocks will give a balanced workload among the partitions. For an example of this approach, see [6].…”

Section: Dynamic Load Balancingmentioning

confidence: 99%

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Parallel Structured Adaptive Mesh Refinement

Rantakokko

Thuné

2009

Parallel Computing

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Parallel structured adaptive mesh refinement is a technique for efficient utilization of computational resources. It reduces the computational effort and memory requirements needed for numerical simulation of complex phenomena, described by partial differential equations. Structured adaptive mesh refinement (SAMR) is applied in simulations where the domain is divided into logically rectangular patches, where each patch is discretized with a structured mesh. The purpose of adaptive mesh refinement is to automatically adapt the mesh to the resolution required to represent important features of the simulated phenomenon in different subdomains. In a parallel computing context, an important consequence of the adaptation is that the dynamically changing resolution leads to a dynamically changing work load, data volume, and communication pattern at run-time. This calls for dynamic load balancing and has implications for data placement as well as parallelization granularity.This chapter gives an overview of structured adaptive mesh refinement approaches. After a brief introductory survey of SAMR techniques and software packages, the main part of the chapter addresses various issues related to implementation of SAMR on parallel computers. In particular programming models, data placement and load balancing are discussed, for shared memory as well as distributed memory platforms. Various approaches and algorithms are presented. The appropriate choice of dynamic load balancing algorithm, data placement strategy, programming model, etc., depends on both the application state and the computer platform. There

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Section: Approaches To Structured Adaptive Mesh Refinementmentioning

confidence: 99%

“…Also the Racoon framework has been parallelized in two levels but using a hybrid of MPI and POSIXPthreads [6].…”

Section: Software Framework For Samrmentioning

confidence: 99%

Section: Dynamic Load Balancingmentioning

confidence: 99%

Section: Dynamic Load Balancingmentioning

confidence: 99%

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Parallel Structured Adaptive Mesh Refinement

Rantakokko

Thuné

2009

Parallel Computing

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“…An overall summary of AMR for both the Euler and Navier-Stokes Equations was presented in several publications [45,46,47]. In addition to studying numerical schemes on an adaptively refined mesh, the most recent interests were focused on improving AMR parallel efficiency [48,49,50,51,52], applying AMR to practical problems [53,54,55,56] and presenting new AMR frameworks [57,58,59].…”

Section: Adaptive Mesh Refinementmentioning

confidence: 99%

Adaptive Mesh Refinement for Computational Aeroacoustics

Huang

Zhang

2005

11th AIAA/CEAS Aeroacoustics Conference

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This thesis describes a parallel block-structured adaptive mesh refinement (AMR) method that is employed to solve some computational aeroacoustic problems with the aim of improving the computational efficiency. AMR adaptively refines and coarsens a computational mesh along with sound propagation to increase grid resolution only in the area of interest.While sharing many of the same features, there is a marked difference between the current and the established AMR approaches. Rather than low-order schemes generally used in the previous approaches, a high-order spatial difference scheme is employed to improve numerical dispersion and dissipation qualities. To use a highorder scheme with AMR, a number of numerical issues associated with fine-coarse block interfaces on an adaptively refined mesh, such as interpolations, filter and artificial selective damping techniques and accuracy are addressed. In addition, the asymptotic stability and the transient behaviour of a high-order spatial scheme on an adaptively refined mesh are also studied with eigenvalue analysis and pseudospectra analysis respectively. In addition, the fundamental AMR algorithm is simplified in order to make the work of implementation more manageable.Particular emphasis has been placed on solving sound radiation from generic aero-engine bypass geometry with mean flow. The approach of AMR is extended to support a body-fitted multi-block mesh. The radiation from an intake duct is modelled by the linearised Euler equations, while the radiation from an exhaust duct is modelled by the extended acoustic perturbation equations to suppress hydrodynamic instabilities generated in a sheared mean flow. After solving the near-field sound solution, the associated far-field sound directivity is estimated by solving the Ffowcs Williams-Hawkings equation. The overall results demonstrate the accuracy and the efficiency of the presented AMR method, but also reveal some limitations. The possible methods to avoid these limitations are given at the end of this thesis.

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