A novel laminar flame speed equation for quasi-dimensional combustion model refinement in advanced, ultra-lean gasoline spark-ignited engines. (1st February 2023) - Sok, Ratnak; Kataoka, Hidefumi; Kusaka, Jin; Miyoshi, Akira; Reitz, Rolf D.
Highlights: A laminar flame speed (LFS) equation of a 5-component gasoline surrogate is proposed. LFS equation is validated with measured data under stoichiometric to the ultra-lean mixture. The combustion and performance of two advanced, ultra-lean engines are well predicted. The LFS equation can be used in combustion modeling. Abstract: In model-based combustion simulations, laminar flame speed (LFS) is a vital parameter for predicting spark-ignited (SI) turbulent combustion, which is unstable under ultra-lean mixtures. Accurate predictions of ultra-lean combustion require a proper LFS correlation. This work proposes a novel LFS equation for a 5-component gasoline surrogate to refine a quasi-dimensional (QD) combustion model predictivity in two highly efficient gasoline engines dedicated to next-generation hybridized vehicles. First, micro-gravity constant volume vessel experiments are performed to measure the LFS of the 5-component gasoline surrogate for equation validations (equivalence ratio ϕ  = 0.55 – 1.0 at elevated temperature and pressure). Calculated data using conventional (LFS_conv), refined (LFS_ref), and novel (LFS_nov) functions are compared with the measured, literature, and 1D kinetics data. LFS_conv computes zero or negative flame speeds under ultra-lean and ultra-rich mixtures, and over-predicted LFS values are obtained for the entire range. Computed data of LFS_ref and LFS_nov are properly validated with measured, literature, and 1D kinetics values. The refined and novel LFS correlations are embedded into the QD combustion model. The combustion model fidelities are compared using the three LFS equations in predicting the combustion and performance characteristics of the engines (conventional-port engine A: ϕ  = 1.0 to lean-limit, strong-tumble engine B: ϕ  = 0.5 – 1.0). For engine A, predicted combustion and performance using LFS_conv are obtained with average relative errors δ ¯ conv up to 47.6 %. Maximum δ ¯ ref  = 18.2 % and δ ¯ nov  = 12.8 % are obtained using LFS_conv and LFS_ref, respectively. For the long-stroke engine B model, LFS_nov produces the highest prediction accuracy with δ ¯ nov ≤ ± 6 %. Using LFS_nov, a sensitivity analysis of the combustion model and the method to predict lean combustion under cycle-to-cycle variations are also proposed for engine B.