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Feasibility of Employing Negative Valve Overlap for Enhanced Charge Homogeneity in PCCI Diesel Engine Using 1D Thermodynamic Simulation and 3D CFD Study
ISSN: 0148-7191, e-ISSN: 2688-3627
Published January 09, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
There have been strong demands for complete combustion and eco-friendly engine for meeting the stringent emission norms. Homogenous charge formations within the cylinder is important to achieve complete combustion. This work deals with new cam shaft design of “NEGATIVE VALVE OVERLAP” (NVO) for achieving homogenous charge preparation within the cylinder. NVO study involves the early closure of exhaust valve which keeps delay between intake valve opening and exhaust valve closing, this allows more residual gas inside the cylinder. Based on the early closure of exhaust valve, two cam shaft profiles, 0° NVO and 10° NVO is selected. This study includes the 1D and 3D computational thermodynamic and fluid dynamic simulation of NVO in 3 cylinder, common rail direct injection (CRDI), turbo-charged intercooled diesel engine. The first part of the work involves the 1D thermodynamic analysis of NVO in which full throttle performance cases are simulated with the 1D computational model. The results show that, trapped exhaust gas increases the in cylinder temperature by 25°C and pressure by 2.5 bar at 150° BTDC. Increased temperature and pressure have good potential to improve the fuel evaporation and mixing. The second part of the work involves multi-dimensional CFD study for visualizing in cylinder charge preparation. Lambda ratio distribution over the cylinder is compared with 0° and 10° NVO cases. Results show improved mixing in the NVO cases.
CitationKinkhabwala, B., S, S., Raaj, A., Sadagopan, K. et al., "Feasibility of Employing Negative Valve Overlap for Enhanced Charge Homogeneity in PCCI Diesel Engine Using 1D Thermodynamic Simulation and 3D CFD Study," SAE Technical Paper 2019-26-0135, 2019, https://doi.org/10.4271/2019-26-0135.
Data Sets - Support Documents
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