2016
DOI: 10.1016/j.camwa.2015.12.043
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Adjoint Lattice Boltzmann for topology optimization on multi-GPU architecture

Abstract: In this paper we present a topology optimization technique applicable to a broad range of flow design problems. We propose also a discrete adjoint formulation effective for a wide class of Lattice Boltzmann Methods (LBM). This adjoint formulation is used to calculate sensitivity of the LBM solution to several type of parameters, both global and local. The numerical scheme for solving the adjoint problem has many properties of the original system, including locality and explicit time-stepping. Thus it is possib… Show more

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Cited by 73 publications
(32 citation statements)
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“…Next, Yaji et al [118] presented a topology optimisation method using the Lattice Boltzmann Method (LBM) incorporating a special sensitivity analysis based on the discrete velocity Boltzmann equation. Łaniewski Wołłk and Rokicki [119] treated large three-dimensional problems using a discrete adjoint formulation for the LBM implemented for multi-GPU architectures. Qian and Dede [120] introduced a constraint on the tangential thermal gradient around discrete heat sources with the goal of reducing thermal stress due to non-uniform expansion.…”
Section: Forced Convectionmentioning
confidence: 99%
“…Next, Yaji et al [118] presented a topology optimisation method using the Lattice Boltzmann Method (LBM) incorporating a special sensitivity analysis based on the discrete velocity Boltzmann equation. Łaniewski Wołłk and Rokicki [119] treated large three-dimensional problems using a discrete adjoint formulation for the LBM implemented for multi-GPU architectures. Qian and Dede [120] introduced a constraint on the tangential thermal gradient around discrete heat sources with the goal of reducing thermal stress due to non-uniform expansion.…”
Section: Forced Convectionmentioning
confidence: 99%
“…This is in contrast to 'weak' scaling, where the size of the mesh is kept proportional to the number of processors. The simulations were completed using the open-source TCLB solver [52] on the Prometheus cluster at Cyfronet, Krakow. This is equipped with CPU nodes fitted with two 12-core Intel Xeon E5-2680 v3 processors and eight additional GPU nodes with two nVidia Tesla K40 cards on each.…”
Section: Computational Efficiencymentioning
confidence: 99%
“…Conjugate heat transfer was originally treated in [23,24] and is a very active field of research today [25,26,27,28,29]. Most work focuses on forced convection, where the fluid flow is induced by a fan, pump or pressure-gradient.…”
Section: Introductionmentioning
confidence: 99%