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.

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先进生物燃料燃烧的自动机制生成：乙醚案例研究

先进的生物燃料有潜力取代大部分传统液体化石燃料。然而，根据所选原料和生产工艺，潜在化合物的范围可能很广。对于许多此类燃料的发动机相关行为知之甚少，特别是在复杂混合物中使用时。模拟工具可能有助于探索此类混合物的燃烧行为，但依赖于强大的化学机制，可以准确预测大范围热化学空间的性能目标。自动机制生成（AMG）等工具可以促进合适机制的生成。此类工具通常用于生成描述非氧化、非芳香烃氧化的机制，但由于含氧官能团的存在，生物燃料的出现增加了新的挑战。本研究以乙醚 (DEE) 作为案例研究，研究了 AMG 工具反应机制生成器执行此类任务的能力。提出了一种生成先进生物燃料机制的方法，并根据文献来源的实验测量对所产生的机制进行了评估，包括φ  = 0.5–2.0、P  = 1的条件下的点火延迟时间、喷射搅拌反应器物种浓度和火焰速度–100 bar，T  = 298–1850 K。结果表明，AMG 工具能够快速生成先进生物燃料组件的精确模型，尽管需要大量的前期输入。需要高质量的燃料特定反应速率和含氧物质的热化学，以及种子机制、热化学库和反应族数据库的扩展以包括含氧化合物的训练数据。最终的 DEE 机制包含 146 个物种和 4392 个反应，总的来说，与跨研究目标数据的文献来源机制相比，提供了更准确或可比较的预测。其他潜在的先进生物燃料成分的燃烧机制的生成可以轻松地利用这些数据库更新，减少未来用户干预的需要。