Smoothed Particle Hydrodynamics for Numerical Predictions of Primary Atomization
Samuel Braun (),
Rainer Koch () and
Hans-Jörg Bauer ()
Additional contact information
Samuel Braun: Karlsruher Institut für Technologie, Institut für Thermische Strömungsmaschinen
Rainer Koch: Karlsruher Institut für Technologie, Institut für Thermische Strömungsmaschinen
Hans-Jörg Bauer: Karlsruher Institut für Technologie, Institut für Thermische Strömungsmaschinen
A chapter in High Performance Computing in Science and Engineering ´16, 2016, pp 321-336 from Springer
Abstract:
Abstract A code framework based on the Smoothed Particle Hydrodynamics (SPH) method has been used to investigate the liquid disintegration processes of an air-assisted atomizer. As the flow physics includes spatial and temporal scales which cover at least 4 orders of magnitude, the use of HPC resources is indispensable. The application of the SPH method is rather new to computational fluid dynamics (CFD). We therefore compare our in-house code to established CFD tools in order to assess the computational performance as well as the quality the physical results. It can be shown, that SPH is able to outperform commonly used grid based methods concerning the scalability behavior as well as the absolute computing speed. The three dimensional test case to be presented consists of 1.2 billion particles. The simulation has been run on the ForHLR I cluster, where 2560 cores have been used for 60 days. The simulation is the most detailed numerical investigation of a prefilmer based atomizer and one of the largest SPH multi-phase flow simulations ever. It did capture the experimentally observed bag breakup regime with good agreement of the spatial liquid disintegration and the breakup time scales.
Date: 2016
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-47066-5_22
Ordering information: This item can be ordered from
http://www.springer.com/9783319470665
DOI: 10.1007/978-3-319-47066-5_22
Access Statistics for this chapter
More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().