Numerical Modeling of Transcritical and Supercritical Fuel Injections Using a Multi-Component Two-Phase Flow Model
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ArticleAuthors
Ningegowda, Bittagowdanahalli ManjegowdaRahantamialisoa, Faniry Nadia Zazaravaka
Pandal, Adrian

Jasak, Hrvoje
Im, Hong G.

Battistoni, Michele

KAUST Department
Physical Science and Engineering (PSE) DivisionMechanical Engineering Program
Clean Combustion Research Center
KAUST Grant Number
OSR-2017-CRG6-3409.03Date
2020-10-30Submitted Date
2020-09-24Permanent link to this record
http://hdl.handle.net/10754/667546
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In 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.Citation
Ningegowda, 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/en13215676Sponsors
This 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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MDPI AGJournal
EnergiesAdditional Links
https://www.mdpi.com/1996-1073/13/21/5676ae974a485f413a2113503eed53cd6c53
10.3390/en13215676
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