Investigation of the Engine Combustion Network Spray a Characteristics using Eulerian and Lagrangian Models
dc.contributor.author | Liu, Xinlei | |
dc.contributor.author | Al-lehaibi, Moaz | |
dc.contributor.author | Im, Hong G. | |
dc.date.accessioned | 2022-04-21T08:11:44Z | |
dc.date.available | 2022-04-21T08:11:44Z | |
dc.date.issued | 2022-03-29 | |
dc.identifier.citation | Liu, X., Allehaibi, M., & Im, H. G. (2022). Investigation of the Engine Combustion Network Spray a Characteristics using Eulerian and Lagrangian Models. SAE Technical Paper Series. https://doi.org/10.4271/2022-01-0507 | |
dc.identifier.issn | 0148-7191 | |
dc.identifier.doi | 10.4271/2022-01-0507 | |
dc.identifier.uri | http://hdl.handle.net/10754/676402 | |
dc.description.abstract | This work presents a numerical study of the Spray A (n-dodecane) characteristics using Eulerian and Lagrangian models in a finite-volume framework. The standard k-? turbulence model was applied for the spray simulations. For Eulerian simulations, the X-ray measured injector geometries from the Engine Combustion Network (ECN) were employed. The High-Resolution Interface Capturing (HRIC) scheme coupled with a cavitation model was utilized to track the fluid-gas interface. Simulations under both the cool and hot ambient conditions were performed. The effects of various grid sizes, turbulence constants, nozzle geometries, and initial gas volume within the injector sac on the modeling results were evaluated. As indicated by the Eulerian simulation results, no cavitation was observed for the Spray A injector; a minimum mesh size of 15.6 µm could achieve a reasonably convergent criterion; the nominal nozzle geometry predicted similar results to the X-ray measured nozzle geometry. For both the Eulerian and Lagrangian simulations, the higher C?1 value of the turbulence model resulted in the lower turbulent kinetic energy, longer jet penetration, and spray cone angle. Since the Eulerian-Lagrangian coupled method has the advantage over spray distribution at the nozzle exit, it predicted a significantly better near-nozzle mixture distribution compared to the conventional Lagrangian model at a non-vaporizing condition. By employing an initial gas volume fraction of 30#x00025; within the injector sac as recommended by the Engine Combustion Network committee, the Eulerian-Lagrangian coupled method could well reproduce the experimental rate of injection profile, fuel mixture distributions, and spray penetrations at a vaporizing condition. Furthermore, the higher injection pressure promoted the vapor penetration, but it had limited effects on the liquid penetration owing to the competitive relationship between the higher spray momentum and evaporation rate. The higher ambient temperature reduced the liquid penetration for the higher evaporation rate, but it had limited effects on the vapor penetration since the spray momentum and ambient density were kept unchanged. | |
dc.description.sponsorship | This work was sponsored by King Abdullah University of Science and Technology. The computational simulations utilized the clusters at KAUST Supercomputing Laboratory. The authors thank Convergent Science Inc. for providing the CONVERGE license. | |
dc.publisher | SAE International | |
dc.relation.url | https://www.sae.org/content/2022-01-0507/ | |
dc.rights | Archived with thanks to SAE International | |
dc.title | Investigation of the Engine Combustion Network Spray a Characteristics using Eulerian and Lagrangian Models | |
dc.type | Conference Paper | |
dc.contributor.department | Computational Reacting Flow Laboratory (CRFL) | |
dc.contributor.department | Clean Combustion Research Center | |
dc.contributor.department | Mechanical Engineering Program | |
dc.contributor.department | Physical Science and Engineering (PSE) Division | |
dc.rights.embargodate | 2022-09-29 | |
dc.conference.date | 2022-04-05 to 2022-04-07 | |
dc.conference.name | SAE 2022 Annual World Congress Experience, WCX 2022 | |
dc.conference.location | Virtual, Online, USA | |
dc.eprint.version | Post-print | |
dc.contributor.institution | Umm Al-Qura University | |
dc.identifier.issue | 2022 | |
kaust.person | Liu, Xinlei | |
kaust.person | Al-lehaibi, Moaz | |
kaust.person | Im, Hong G. | |
dc.identifier.eid | 2-s2.0-85128056591 | |
kaust.acknowledged.supportUnit | KAUST Supercomputing Laboratory | |
kaust.acknowledged.supportUnit | Supercomputing Laboratory. |
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Clean Combustion Research Center