Knock is an abnormal combustion phenomena capable of causing serious damage to spark ignition engines, and is a constraint to reach the maximum potential of the engine, since strategies to increase power output and improve efficiency such as turbocharging, increased compression ratio and the advancement of spark timing, also increase the possibility of knock occurrence. Therefore, it is crucial to take into account the limits imposed by knock in the design and operating conditions of the engine when using an engine computational model. In this article a zero-dimensional two-zone engine model, coupled with a chemical kinetic model for knock detection through end-gas auto-ignition is developed and validated, for a natural gas engine. Given the importance of an accurate knock prediction, five heat transfer coefficient correlations are compared to find the most suitable to predict the knock occurrence, through calculation of a knock criterion. Correlations from Sitkei and Annand were the most suitable to predict this knock criterion for the experimental data used, and the Sitkei correlation was later tested in a parametric study to predict the effect of spark timing, compression ratio, equivalence ratio and inlet temperature in knock occurrence and intensity. Results were in accordance with real engine behaviour when knock occurs. Keywords Zero-D model• Engine knock model • Natural gas • Detailed kinetics • SI engine • Heat transfer correlation List of symbols A R Flow area of the valve (m 2 ) A s Heat transfer area (m) B Cylinder bore (m) C D Discharge coefficient CR Compression ratio c p Specific heat capacity at constant pressure (J/kg K) c v Specific heat capacity at constant volume (J/kg K) D V Valve diameter (m) d Q Gas-wall heat transfer (J/CAD) d Q chem Rate of energy released in combustion reactions (J/CAD) h Specific Enthalpy (J/kg) h g Gas-wall heat transfer coefficient (W/m 2 K) B Andrés Felipe Sierra Parra
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.