Modeling of Homogeneous Mixture Formation and Combustion in GDI Engine with Negative Valve Overlap

Lalith, M. K.; Dinesh, Akshay; Unnikrishnan, S.; Radhakrishnan, Akhil; Srihari, Sargur N.; Velamati, Ratna Kishore

doi:10.1155/2013/521375

Cited by 2 publications

(1 citation statement)

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“…Trapped residuals are the carrier between consecutive combustion cycles [9][10][11]. Furthermore, due to the limited time for mixing trapped residuals with fresh air, the in-cylinder mixture is not perfectly homogeneous [12] and, consequently, uneven in the cyclic domain. If fuel is injected during the NVO period, fuel-exhaust reactions introduce further complexities into cycle-by-cycle couplings [13].…”

Section: Introductionmentioning

confidence: 99%

On Cyclic Variability in a Residual Effected HCCI Engine with Direct Gasoline Injection during Negative Valve Overlap

Hunicz¹

2014

Mathematical Problems in Engineering

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This study contributes towards describing the nature of cycle-by-cycle variability in homogeneous charge compression ignition (HCCI) engines. Experimental measurements were performed using a single cylinder research engine operated in the negative valve overlap (NVO) mode and fuelled with direct gasoline injection. Both stoichiometric and lean mixtures were applied in order to distinguish between different exhaust-fuel reactions during the NVO period and their propagation into the main event combustion. The experimental results show that the mode of cycle-by-cycle variability depends on the NVO phenomena. Under stoichiometric mixture conditions, neither variability in the main event indicated mean effective pressure (IMEP) nor the combustion timing was affected by the NVO phenomena; however, long period oscillations in IMEP were observed. In contrast, for lean mixture, where fuel oxidation during the NVO period took place, distinctive correlations between NVO phenomena and the main event combustion parameters were observed. A wavelet analysis revealed the presence of both long-term and short-term oscillations in IMEP, in accordance with the extent of NVO phenomena. Characteristic patterns in IMEP were recognized using an in-house algorithm.

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