Fast Boundary Element Method for acoustics with the Sparse Cardinal Sine Decomposition

Alouges, François; Aussal, Matthieu; Parolin, Emile

doi:10.1080/17797179.2017.1306832

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…TS predictions for the model have been presented from a number of modeling methods, including BEM, FMBEM, H-matrix, SCSD and ray-tracing methods. [65][66][67][68][69][70] The FMBEM is suitable for modeling the submarine hull at low-to-mid frequencies (up to a few kHz), while at higher frequencies the fast approximate methods (such as ray-tracing) are better suited, as they do not require an explicit frequency-dependent discretization of the scattering surface. Monostatic TS modeling results are presented here for the outer submarine hull at frequencies of 1 kHz and 3 kHz.…”

Section: Ts Modeling Of the Betssi II Submarine Hull Modelmentioning

confidence: 99%

A Parallel and Broadband Helmholtz FMBEM Model for Large-Scale Target Strength Modeling

Wilkes

Duncan

Marburg

2020

J. Theor. Comp. Acout.

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The Fast Multipole Boundary Element Method (FMBEM) reduces the [Formula: see text] computational and memory complexity of the conventional BEM discretized with [Formula: see text] boundary unknowns, to [Formula: see text] and [Formula: see text], respectively. A number of massively parallel FMBEM models have been developed in the last decade or so for CPU, GPU and heterogeneous architectures, which are capable of utilizing hundreds of thousands of CPU cores to treat problems with billions of degrees of freedom (dof). On the opposite end of this spectrum, small-scale parallelization of the FMBEM to run on the typical workstation computers available to many researchers allows for a number of simplifications in the parallelization strategy. In this paper, a novel parallel broadband Helmholtz FMBEM model is presented, which utilizes a simple columnwise distribution scheme, element reordering and rowwise compression of data, to parallelize all stages of the fast multipole method (FMM) algorithm with a minimal communication overhead. The sparse BEM near-field and sparse approximate inverse preconditioner are also constructed and executed in parallel, while the flexible generalized minimum residual (fGMRES) solver has been modified to apply the FMBEM matrix-vector products and corresponding minimum residual convergence within the parallel environment. The algorithmic and memory complexities of the resulting parallel FMBEM model are shown to reaffirm the above estimates for both the serial and parallel configurations. The parallel efficiency (PE) of the FMBEM matrix-vector products and fGMRES solution for the present model is shown to be satisfactory; achieving PEs up to [Formula: see text] and [Formula: see text] in the fGMRES solution using 3 and 6 CPU cores respectively, when applied to models having [Formula: see text] dof per CPU core. The PE of the precalculation stages of the FMBEM — in particular the FMM precomputation stage which is largely unparallelized — reduces the overall PE of the FMBEM model; resulting in average efficiencies of [Formula: see text] and [Formula: see text] for the 3-core and 6-core models when treating problems with [Formula: see text] dof per CPU core. The present model is able to treat large-scale acoustic scattering problems involving up to [Formula: see text] dof on a workstation computer equipped with 128[Formula: see text]GB of RAM, while acoustic target strength (TS) results calculated up to 3[Formula: see text]kHz for the BeTSSi II submarine model demonstrate its capabilities for large-scale TS modeling.

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Section: Ts Modeling Of the Betssi II Submarine Hull Modelmentioning

confidence: 99%

A Parallel and Broadband Helmholtz FMBEM Model for Large-Scale Target Strength Modeling

Wilkes

Duncan

Marburg

2020

J. Theor. Comp. Acout.

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“…the scatterer), which lowers the dimension of the object that needs to be discretized. Nevertheless, due to computer limitations, those methods may require a special compression technique such as the Fast Multipole Method (FMM) [18,15], the H-matrix storage [20] or the more recent Sparse Cardinal Sine Decomposition [2,3,4], in order to be applied to relevant problems. In terms of software packages available for the simulation with this kind of methods, and probably due to the technicality sketched above, the list is much shorter than before.…”

Section: Introductionmentioning

confidence: 99%

FEM and BEM simulations with the Gypsilab framework

Alouges¹,

Aussal²

2018

The SMAI Journal of Computational Mathematics

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Gypsilab is a Matlab framework which aims at simplifying the development of numerical methods that apply to the resolution of problems in multiphysics, in particular, those involving FEM or BEM simulations. The peculiarities of the framework, with a focus on its ease of use, are shown together with the methodology that have been followed for its development. Example codes that are short though representative enough are given both for FEM and BEM applications. A performance comparison with FreeFem++ is provided, and a particular emphasis is made on problems in acoustics and electromagnetics solved using the BEM and for which compressed H-matrices are used.

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Fast Boundary Element Method for acoustics with the Sparse Cardinal Sine Decomposition

Cited by 2 publications

References 8 publications

A Parallel and Broadband Helmholtz FMBEM Model for Large-Scale Target Strength Modeling

A Parallel and Broadband Helmholtz FMBEM Model for Large-Scale Target Strength Modeling

FEM and BEM simulations with the Gypsilab framework

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