Automatic mechanism generation for the combustion of advanced biofuels: A case study for diethyl ether

Abstract

Advanced biofuels have the potential to supplant significant fractions of conventional liquid fossil fuels. However, the range of potential compounds could be wide depending on selected feedstocks and production processes. Not enough is known about the engine relevant behavior of many of these fuels, particularly when used within complex blends. Simulation tools may help to explore the combustion behavior of such blends but rely on robust chemical mechanisms providing accurate predictions of performance targets over large regions of thermochemical space. Tools such as automatic mechanism generation (AMG) may facilitate the generation of suitable mechanisms. Such tools have been commonly applied for the generation of mechanisms describing the oxidation of non-oxygenated, non-aromatic hydrocarbons, but the emergence of biofuels adds new challenges due to the presence of functional groups containing oxygen. This study investigates the capabilities of the AMG tool Reaction Mechanism Generator for such a task, using diethyl ether (DEE) as a case study. A methodology for the generation of advanced biofuel mechanisms is proposed and the resultant mechanism is evaluated against literature sourced experimental measurements for ignition delay times, jet-stirred reactor species concentrations, and flame speeds, over conditions covering φ = 0.5–2.0, P = 1–100 bar, and T = 298–1850 K. The results suggest that AMG tools are capable of rapidly producing accurate models for advanced biofuel components, although considerable upfront input was required. High-quality fuel specific reaction rates and thermochemistry for oxygenated species were required, as well as a seed mechanism, a thermochemistry library, and an expansion of the reaction family database to include training data for oxygenated compounds. The final DEE mechanism contains 146 species and 4392 reactions and in general, provides more accurate or comparable predictions when compared to literature sourced mechanisms across the investigated target data. The generation of combustion mechanisms for other potential advanced biofuel components could easily capitalize on these database updates reducing the need for future user interventions.