Experimental-Based Laminar Flame Speed Approximation Formulas of Efficiency-Optimized Biofuels for SI-Engine Modeling

The transition towards sustainable mobility encourages research into biofuels for use in internal combustion engines. For these alternative energy carriers, high-fidelity experimental data of flame speeds influenced by pressure, temperature, and air-fuel equivalence ratio under engine-relevant conditions are required to support the development of robust combustion models for spark-ignition engines. E.g., physicochemical-based approximation formulas adjusted to the fuel provide similar accuracy as high fidelity chemical kinetic model calculations at a fraction of the computational cost and can be easily adopted in engine simulation codes. In the present study, a workflow to enable predictive combustion engine modeling is applied first for a gasoline reference fuel and two biofuel blends recently proposed by Dahmen and Marquardt [Energy Fuels, 2017]. They identified one promising high-octane rating biofuel blend, expected to be optimized for SI combustion engines, and one promising low carbon high energy density blend with an optimized production pathway. The first blend consists of ethanol, 2-butanone, cyclopentane, and cyclopentanone, and the second blend consists of 1-butanol, ethanol, and cyclopentane. In the present study, the reference fuel RON95 E10 and both biofuel blends were experimentally examined for their flame speed in RWTH-ITV’s closed combustion chamber at 423 K and 2.5 bar, with equivalence ratios (Φ) ranging from 0.8 to 1.3. Then, pressure (1 atm and 5 bar) and temperature variations (398 K and 450 K) were conducted for the blends at Φ = 1.1. Due to its good agreement with the experimental results, a detailed kinetic mechanism was selected and used for comprehensive flame speed calculations at engine conditions. The approximation formula was parametrized in the next step, showing good agreement with the detailed calculations. Finally, the flame speed model is adopted for engine simulations, and the 0-2% burn duration of gasoline is used as a benchmark against engine data, showing the improved predictability of the newly derived approximation compared to a standard correlation. The biofuels’ burn durations indicate slight improvements due to higher flame speeds.