Calculation of Laminar Flame Speed and Autoignition Delay at High Temperature and Pressures

Abstract

Recent developments in emissions regulations, costs of conventional fuels, and new gas extraction drilling technologies have resulted in an increased emphasis in gaseous fueled spark ignited engine development. However the composition of gaseous fuels can vary greatly. Homogenous Charge Spark Ignited (HCSI) engine performance is heavily dependent upon fuel properties, and robust engine design to utilize gaseous fuels must accommodate these fuel property variances. Accurate prediction of fuel energy release characteristics and knock tendency is critical in the process of HCSI engine development. Combustion characteristics, such as Laminar Flame Speed (LFS) and Autoignition Interval (AI), are used to characterize performance of various gaseous fuels in HCSI engine applications. Combustion duration is related to the LFS. The likelihood of Knock is related to the AI. Overall engine performance is estimated by appropriately incorporating these parameters into cycle simulation software. Experimental data of LFS is often at low temperature and low pressure and thus does not represent the high temperature and pressure conditions typically prevalent in HCSI engine combustion chambers at the time of ignition. Lack of reliable LFS data at high temperature and pressures represents a major opportunity of development for better engine performance simulations [1]. In this paper, the commercially available chemical kinetics solver Chemkin Pro using an appropriate mechanism was employed to compute LFS and AI at typical HCSI engine in-cylinder conditions. It is challenging to compute LFS at such extreme conditions mainly because of autoignition as a competing process. This paper describes development of a robust methodology to compute LFS over a wide range of Temperature (up to 1300 K), Pressure (up to 250 bar), Relative Humidity, and Lambda for Methane. A regression for LFS with Pressure, Temperature, Lambda, and Relative Humidity as independent variables was generated for Methane. Methodology robustness was suggested with similar LFS calculations using other fuels. The form of the regression is similar for all of the fuels investigated.