Predicting the autoignition behaviour of tailorable advanced biofuel blends using automatically generated mechanisms

Emerging processes such as biomass alcoholysis have the potential to provide tailorable, advanced biofuels to replace conventional fossil fuels. Knowledge of the engine-relevant behaviour for such fuels is evolving but is currently limited. Simulation tools may assist in the exploration of this behaviour but are reliant on the availability of robust, detailed kinetic mechanisms to produce accurate predictions. Automatic mechanism generation (AMG) techniques may be applied to facilitate the production of such mechanisms, as long as the utilised databases contain high-quality kinetic and thermochemical information of relevance to the functional groups of interest. To model the combustion characteristics of complex fuel blends, Reaction Mechanism Generator (RMG) is applied in this work to produce detailed ethyl (ethyl levulinate, diethyl ether, ethanol) and butyl (n‑butyl levulinate, di-n‑butyl ether, n-butanol) kinetic mechanisms. The predictive capabilities of these mechanisms are evaluated against a combination of literature data for individual components and new experimental measurements of ethyl and butyl blends. Stoichiometric blend ignition delay times are measured in a rapid compression machine at a compressed pressure of 20 bar and compressed temperatures of 645–960 K. The investigated blends are formulated to achieve a desired research octane number (blends ELV1 and BLV1) or to match physical property limits of diesel fuels (blends ELV2 and BLV2). Ethyl blend measurements show clear examples of complex low temperature oxidation behaviour, but this is not observable in the butyl cases, as the high boiling point of butyl levulinate necessitates the use of significant dilution in ignition delay experiments. The generated models show a high degree of accuracy when compared to measurements at thermodynamic conditions of relevance to engine technologies. The blending behaviour shown by the experimental measurements is also well predicted. This work highlights the importance of database additions/modifications based on low uncertainty, high quality, literature data when using AMG methods.