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Volume 26, Issue 3
Fast Distance Fields for Fluid Dynamics Mesh Generation on Graphics Hardware

Alo Roosing, Oliver Strickson & Nikos Nikiforakis

DOI: 10.4208/cicp.OA-2018-013

Commun. Comput. Phys., 26 (2019), pp. 654-680.

Published online: 2019-04

[An open-access article; the PDF is free to any online user.]

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  • Abstract

We present a CUDA accelerated implementation of the Characteristic/Scan Conversion algorithm to generate narrow band signed distance fields in logically Cartesian grids. We outline an approach of task and data management on GPUs based on an input of a closed triangulated surface with the aim of reducing pre-processing and mesh-generation times. The work demonstrates a fast signed distance field generation of triangulated surfaces with tens of thousands to several million features in high resolution domains. We present improvements to the robustness of the original algorithm and an overview of handling geometric data.

  • Keywords

Signed distance field, GPU, CUDA, mesh generation, fluid dynamics

  • AMS Subject Headings

68U05, 68U20

  • Copyright

COPYRIGHT: © Global Science Press

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  • TXT
@Article{CiCP-26-654, author = {Alo Roosing, Oliver Strickson and Nikos Nikiforakis}, title = {Fast Distance Fields for Fluid Dynamics Mesh Generation on Graphics Hardware}, journal = {Communications in Computational Physics}, year = {2019}, volume = {26}, number = {3}, pages = {654--680}, abstract = {

We present a CUDA accelerated implementation of the Characteristic/Scan Conversion algorithm to generate narrow band signed distance fields in logically Cartesian grids. We outline an approach of task and data management on GPUs based on an input of a closed triangulated surface with the aim of reducing pre-processing and mesh-generation times. The work demonstrates a fast signed distance field generation of triangulated surfaces with tens of thousands to several million features in high resolution domains. We present improvements to the robustness of the original algorithm and an overview of handling geometric data.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-013}, url = {http://global-sci.org/intro/article_detail/cicp/13141.html} }
TY - JOUR T1 - Fast Distance Fields for Fluid Dynamics Mesh Generation on Graphics Hardware AU - Alo Roosing, Oliver Strickson & Nikos Nikiforakis JO - Communications in Computational Physics VL - 3 SP - 654 EP - 680 PY - 2019 DA - 2019/04 SN - 26 DO - http://doi.org/10.4208/cicp.OA-2018-013 UR - https://global-sci.org/intro/article_detail/cicp/13141.html KW - Signed distance field, GPU, CUDA, mesh generation, fluid dynamics AB -

We present a CUDA accelerated implementation of the Characteristic/Scan Conversion algorithm to generate narrow band signed distance fields in logically Cartesian grids. We outline an approach of task and data management on GPUs based on an input of a closed triangulated surface with the aim of reducing pre-processing and mesh-generation times. The work demonstrates a fast signed distance field generation of triangulated surfaces with tens of thousands to several million features in high resolution domains. We present improvements to the robustness of the original algorithm and an overview of handling geometric data.

Alo Roosing, Oliver Strickson and Nikos Nikiforakis. (2019). Fast Distance Fields for Fluid Dynamics Mesh Generation on Graphics Hardware. Communications in Computational Physics. 26 (3). 654-680. doi:10.4208/cicp.OA-2018-013
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