Automatic mechanism generation for the combustion of advanced biofuels: A case study for diethyl ether
Automatic mechanism generation for the combustion of advanced biofuels: A case study for diethyl ether
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DOI：
10.1002/kin.21705
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发表时间：
2023-12-17
影响因子：
1.5
通讯作者：
Tomlin，Alison S.
中科院分区：
文献类型：
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作者：
Michelbach，Christian A.;Tomlin，Alison S.
先进的生物燃料有可能取代传统液体化石燃料的很大一部分。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。对许多这些燃料的发动机相关行为的了解还不够，特别是在复杂混合物中使用时。模拟工具可能有助于探索这种混合物的燃烧行为，但依赖于强大的化学机制，在大区域的热化学空间提供准确的预测性能目标。自动机制生成（AMG）等工具可以促进合适机制的生成。这些工具通常用于生成描述非氧化非芳烃氧化的机制，但生物燃料的出现由于含氧官能团的存在而增加了新的挑战。本研究调查的AMG工具反应机制发生器这样一个任务的能力，使用乙醚（DEE）作为案例研究。提出了一种生成先进生物燃料机理的方法，并根据文献来源的点火延迟时间、喷射搅拌反应器物种浓度和火焰速度的实验测量结果对所生成的机理进行了评估，条件涵盖φ= 0.5- 2.0，P = 1-100 bar，T = 298-1850 K。结果表明，AMG工具能够快速生成先进生物燃料成分的精确模型，尽管需要大量的前期投入。需要高质量的燃料比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种物质和4392个反应，一般来说，与文献来源的机制相比，在研究的目标数据中提供了更准确或可比的预测。生成其他潜在的先进生物燃料成分的燃烧机制可以很容易地利用这些数据库更新，减少未来用户干预的需要。
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 coveringφ= 0.5–2.0,P= 1–100 bar, andT= 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.