Multi‑physics bi‑directional evolutionary topology optimization on GPU‑architecture

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dc.contributor.authorMunk, David J.
dc.contributor.authorKipouros, Timoleon
dc.contributor.authorVio, Gareth A.
dc.date.accessioned2018-10-25T14:18:12Z
dc.date.available2018-10-25T14:18:12Z
dc.date.issued2018-10-16
dc.description.abstractTopology optimization has proven to be viable for use in the preliminary phases of real world design problems. Ultimately, the restricting factor is the computational expense since a multitude of designs need to be considered. This is especially imperative in such fields as aerospace, automotive and biomedical, where the problems involve multiple physical models, typically fluids and structures, requiring excessive computational calculations. One possible solution to this is to implement codes on massively parallel computer architectures, such as graphics processing units (GPUs). The present work investigates the feasibility of a GPU-implemented lattice Boltzmann method for multi-physics topology optimization for the first time. Noticeable differences between the GPU implementation and a central processing unit (CPU) version of the code are observed and the challenges associated with finding feasible solutions in a computational efficient manner are discussed and solved here, for the first time on a multi-physics topology optimization problem. The main goal of this paper is to speed up the topology optimization process for multi-physics problems without restricting the design domain, or sacrificing considerable performance in the objectives. Examples are compared with both standard CPU and various levels of numerical precision GPU codes to better illustrate the advantages and disadvantages of this implementation. A structural and fluid objective topology optimization problem is solved to vary the dependence of the algorithm on the GPU, extending on the previous literature that has only considered structural objectives of non-design dependent load problems. The results of this work indicate some discrepancies between GPU and CPU implementations that have not been seen before in the literature and are imperative to the speed-up of multi-physics topology optimization algorithms using GPUs.en_UK
dc.identifier.citationDavid J. Munk, Timoleon Kipouros and Gareth A. Vio. Multi‑physics bi‑directional evolutionary topology optimization on GPU‑architecture. Engineering with Computers, Volume 35, July 2019, pp. 1059-1079en_UK
dc.identifier.cris21851678
dc.identifier.issn0177-0667
dc.identifier.urihttps://doi.org/10.1007/s00366-018-0651-1
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/13566
dc.language.isoenen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectLattice Boltzmann methoden_UK
dc.subjectGraphics processing unitsen_UK
dc.subjectReal world applicationsen_UK
dc.titleMulti‑physics bi‑directional evolutionary topology optimization on GPU‑architectureen_UK
dc.typeArticleen_UK

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