CFD Study of Heat Transfer Reduction Using Multiple Injectors in a DCEE Concept

Gustav Nyrenstedt, Tariq Lutfallah Mohammed Alturkestani, Hong G. Im, Bengt Johansson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Earlier studies on efficiency improvement in CI engines have suggested that heat transfer losses contribute largely to the total energy losses. Fuel impingement on the cylinder walls is typically associated with high heat transfer. This study proposes a two-injector concept to reduce heat losses and thereby improve efficiency. The two injectors are placed at the rim of the bowl to change the spray pattern. Computational simulations based on the Reynolds-Averaged Navier-Stokes approach have been performed for four different fuel injection timings in order to quantify the reduction in heat losses for the proposed concept. Two-injector concepts were compared to reference cases using only one centrally mounted injector. All simulations were performed in a double compression expansion engine (DCEE) concept using the Volvo D13 single-cylinder engine. In the DCEE, a large portion of the exhaust energy is re-used in the second expansion, thus increasing the thermodynamic efficiency. To isolate the heat losses associated with the changed spray pattern of the two-injector concept, effects of the heat release are excluded during the analysis. Results showed that the optimal injection strategy allows a decrease in the temperature close to the walls, leading to heat loss reduction up to 13 % or 2 % of the fuel energy. The residual exhaust energy was increased by 1.5 %-points with the two-injector concept when compared to the reference case. This proved the advantage of the two-injector concept compared to conventional single injector case for the DCEE application.
Original languageEnglish (US)
Title of host publicationSAE Technical Paper Series
PublisherSAE International
StatePublished - Jan 15 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: 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 postprocessing, Dr. Arne Andersson at Volvo Trucks Sweden for helpful comments, and Mr. Nhut Lam at Lund University for providing the experimental data for model validation.


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