Abstract
Numerical studies investigated how multiple injectors can reduce the high heat losses associated with swirl, as a further attempt to enhance thermal efficiencies of high-pressure combustion engines. Computational fluid dynamics simulations employed the Reynolds-averaged Navier-Stokes approach for one, two- and three injector configurations. High and medium load conditions were simulated at different swirl ratios. In general, an increased swirl ratio reduced engine efficiency. However, for all swirl ratios, three injectors provided higher efficiency. Two injectors decreased the heat losses for all swirl ratios, and injection against the swirl with multiple injectors provided high efficiencies. In combination with a waste heat recovery system, the two-injector case delivered an efficiency increase of 2.2%-points for the medium load case. Three injectors delivered high efficiencies at all swirl ratios as an effect of a high flow rate and low heat losses. The multiple injector configurations evaluated in this study proved non-beneficial for the high load case. Spray-to-spray interactions lowered the combustion — and indicated efficiencies. However, the three injector case showed potential for delivering high indicated efficiency, from an increased flow rate, at high loads.
Original language | English (US) |
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Title of host publication | ASME 2020 Internal Combustion Engine Division Fall Technical Conference |
Publisher | American Society of Mechanical Engineers |
ISBN (Print) | 9780791884034 |
DOIs | |
State | Published - Dec 14 2020 |
Bibliographical note
KAUST Repository Item: Exported on 2021-02-25Acknowledgements: This work was sponsored by King Abdullah University of Science and Technology (KAUST). The simulations in this work were performed with the computing resources at the KAUST Supercomputing Laboratories. The authors would like to thank Dr. Georgios Markomanolis at KAUST Supercomputing Laboratory for helpful guidance in post-processing, and Mr. Nhut Lam at Lund University for providing the experimental
data for model validation.