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Multi-scale Coupling for Predictive Injector Simulations

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High-Performance Scientific Computing (JHPCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10164))

Abstract

Predictive simulations of full fuel injection systems for e.g. diesel engines could be very important for reducing emissions of current engines but are still rare. Beside the numerical issues arising from discontinuities across the liquid-gas-interface, different scales relevant for the nozzle internal flow, primary breakup in the vicinity of the nozzle, and secondary breakup and evaporation further downstream make efficient simulation of the full injection system challenging. This paper introduces a multi-scale coupling approach for overcoming this issue leading to efficient and predictive injector simulations. After a brief description of the numerical methods used in this study, the coupling among nozzle internal flow, primary breakup, and secondary breakup with evaporation is introduced and analyzed with respect to computing efficiency and physical accuracy. Finally, the simulation framework is applied to the “Spray A” case of the Engine Combustion Network.

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Acknowledgment

The authors gratefully acknowledge funding by Honda R&D and the Cluster of Excellence “Tailor-Made Fuels from Biomass”, which is funded by the Excellence Initiative of the German federal and state governments to promote science and research at German universities. Also the authors gratefully acknowledge computing time granted for the project JHPC18 by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputer JUQUEEN [22] at Forschungszentrum Jülich and under grant 2013092005 of the Partnership for Advanced Computing in Europe (PRACE). Experimental data for validation has been provided by Alan Kastengren, Argonne National Laboratory, and are gratefully acknowledged, too.

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Correspondence to Mathis Bode .

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Bode, M., Davidovic, M., Pitsch, H. (2017). Multi-scale Coupling for Predictive Injector Simulations. In: Di Napoli, E., Hermanns, MA., Iliev, H., Lintermann, A., Peyser, A. (eds) High-Performance Scientific Computing. JHPCS 2016. Lecture Notes in Computer Science(), vol 10164. Springer, Cham. https://doi.org/10.1007/978-3-319-53862-4_9

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  • DOI: https://doi.org/10.1007/978-3-319-53862-4_9

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