Efficient Combustion Modelling in RCCI Engine with Detailed Chemistry. (May 2017)

• Record Type: Journal Article
• Authors: Zhou, Dezhi; Yang, Wenming; Li, Jing; Tay, Kun Lin; Chou, Siaw Kiang; Kraft, Markus
• Is Part Of: Energy procedia. Volume 105(2017)
• Page Start: 1582
• Page End: 1587
• Publication Date: 2017-05
• Subjects: Combusiton modeling -- HCCI -- Internal combusiton engine; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2017.03.504
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Low temperature combustion (LTC) has been considered as a promising combustion technology in internal combustion engine due to its higher thermal efficiency and lower emission than the conventional combustion engines. Among LTC engines, reactivity controlled compression ignition (RCCI) engine draws tremendous attention of engines researchers because of its super high efficiency and near-zero emissions. Featuring by high fuel variety and high combustion phase controllability, RCCI engine needs to be designed and tested by computational fluid dynamics (CFD) models to rapidly and inexpensively achieve combustion control. Hence, computational time is one of major concerns. Instead of the most commonly used CHEMKIN solver, this study proposed a faster hybrid model for the combustion modelling in RCCI engine. In the light of the basic idea of characteristic time-scale (CTC) of achieving species equilibrium in high temperature, this model basically uses CTC in high temperature combustion and CHEMKIN in low-intermediate temperature combustion. A CEQ solver for species equilibrium calculation at certain temperature, pressure was integrated with CTC for detailed chemistry calculation. Then this combustion model was coupled in KIVA4 and validated in a RCCI engine. The predicted in-cylinder pressure and heat release rate (HRR) show a good consistency with both the data from experiment and CHEMKIN. More importantly, it is observed that this model could save about 20% computational time compared with CHEMKIN due to less stiff ordinary differential equations (ODEs) computation.