Numerical Modeling of Transcritical and Supercritical Fuel Injections Using a Multi-Component Two-Phase Flow Model
AuthorsNingegowda, Bittagowdanahalli Manjegowda
Rahantamialisoa, Faniry Nadia Zazaravaka
Im, Hong G.
KAUST DepartmentClean Combustion Research Center
Computational Reacting Flow Laboratory (CRFL)
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant NumberOSR-2017-CRG6-3409.03
Permanent link to this recordhttp://hdl.handle.net/10754/667546
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AbstractIn the present numerical study, implicit large eddy simulations (LES) of non-reacting multi-components mixing processes of cryogenic nitrogen and n-dodecane fuel injections under transcritical and supercritical conditions are carried out, using a modified reacting flow solver, originally available in the open source software OpenFOAM®. To this end, the Peng-Robinson (PR) cubic equation of state (EOS) is considered and the solver is modified to account for the real-fluid thermodynamics. At high pressure conditions, the variable transport properties such as dynamic viscosity and thermal conductivity are accurately computed using the Chung transport model. To deal with the multicomponent species mixing, molar averaged homogeneous classical mixing rules are used. For the velocity-pressure coupling, a PIMPLE based compressible algorithm is employed. For both cryogenic and non-cryogenic fuel injections, qualitative and quantitative analyses are performed, and the results show significant effects of the chamber pressure on the mixing processes and entrainment rates. The capability of the proposed numerical model to handle multicomponent species mixing with real-fluid thermophysical properties is demonstrated, in both supercritical and transcritical regimes.
CitationNingegowda, B. M., Rahantamialisoa, F. N. Z., Pandal, A., Jasak, H., Im, H. G., & Battistoni, M. (2020). Numerical Modeling of Transcritical and Supercritical Fuel Injections Using a Multi-Component Two-Phase Flow Model. Energies, 13(21), 5676. doi:10.3390/en13215676
SponsorsThis research was funded by the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, under the CRG grant OSR-2017-CRG6-3409.03.
Authors gratefully acknowledge the SHAHEEN HPC facilities provided by KAUST.
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