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  4. A hybrid solver based on efficient BEM-potential and LBM-NS models: recent BEM developments and applications to naval hydrodynamics
 
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A hybrid solver based on efficient BEM-potential and LBM-NS models: recent BEM developments and applications to naval hydrodynamics

Publikationstyp
Conference Paper
Date Issued
2017
Sprache
English
Author(s)
Mivehchi, Amin  
Harris, Jeffrey  
Grilli, Stephan T.  
Dahl, Jason M.  
O'Reilly, Christopher  
Kuznetsov, Konstantin  
Janßen, Christian Friedrich  orcid-logo
Institut
Fluiddynamik und Schiffstheorie M-8  
TORE-URI
http://hdl.handle.net/11420/4745
Start Page
721
End Page
728
Citation
Proceedings of the International Offshore and Polar Engineering Conference: 721-728 (2017)
Contribution to Conference
27th International Ocean and Polar Engineering Conference, ISOPE 2017  
Publisher
International Society of Ocean and Polar Engineers (ISOPE)
We report on recent developments of a 3D hybrid model for naval hydrodynamics based on a perturbation method, in which velocity and pressure are decomposed as the sum of an inviscid flow and a viscous perturbation. The far-to near-field inviscid flows are solved with a Boundary Element Method (BEM), based on fully nonlinear potential flow theory, accelerated with a fast multipole method (FMM), and the near-field perturbation flow is solved with a Navier-Stokes (NS) model based on a Lattice Boltzmann Method (LBM) with a LES modeling of turbulent properties. The BEM model is efficiently parallelized on CPU clusters and the LBM model on massively parallel GPGPU co-processors. The hybrid model formulation and its latest developments and implementation, in particular, regarding the improvement and validation of the model for naval hydrodynamics applications, are presented in a companion paper by O'Reilly et. al (2017), in this conference. In this paper, we concentrate on the BEM model aspects and show that the BEM-FMM can accurately solve a variety of problems while providing a nearly linear scaling with the number of unknowns (up to millions of nodes) and a speed-up with the number of processors of 35-50%, for small (e.g., 24 cores) to large (e.g., hundreds of cores) CPU clusters.
DDC Class
600: Technik
More Funding Information
Grants N000141310687 and N000141612970 of the Office of Naval Research (PM Kelly Cooper).
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