Development of a Eulerian Multi-Fluid Solver for Dense Spray Applications in OpenFOAM
KAUST DepartmentMechanical Engineering
Physical Science and Engineering (PSE) Division
Mechanical Engineering Program
Clean Combustion Research Center
KAUST Grant NumberOSR-2017-CRG6-3409.03
Permanent link to this recordhttp://hdl.handle.net/10754/665150
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AbstractThe new generation of internal combustion engines is facing various research challenges which often include modern fuels and different operating modes. A robust modeling framework is essential for predicting the dynamic behavior of such complex phenomena. In this article, the implementation, verification, and validation of a Eulerian multi-fluid model for spray applications within the OpenFOAM toolbox are presented. Due to its open-source nature and broad-spectrum of available libraries and solvers, OpenFOAM is an ideal platform for academic research. The proposed work utilizes advanced interfacial momentum transfer models to capture the behavior of deforming droplets at a high phase fraction. Furthermore, the WAVE breakup model is employed for the transfer of mass from larger to smaller droplet classes. The work gives detailed instructions regarding the numerical implementation, with a dedicated section dealing with the implementation of the breakup model within the Eulerian multi-fluid formulation. During the verification analysis, the model proved to give stable and consistent results in terms of the selected number of droplet classes and the selected spatial and temporal resolution. In the validation section, the capability of the developed model to predict the dynamic behavior of non-evaporating sprays is presented. It was confirmed that the developed framework could be used as a stable foundation for future fuel spray modeling.
CitationKeser, R., Ceschin, A., Battistoni, M., Im, H. G., & Jasak, H. (2020). Development of a Eulerian Multi-Fluid Solver for Dense Spray Applications in OpenFOAM. Energies, 13(18), 4740. doi:10.3390/en13184740
SponsorsThis work was supported by the King Abdullah University of Science and Technology within the OSR-2017-CRG6-3409.03 research grant, and the Croatian Science Foundation (project number DOK-01-2018).
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