评估模型参数差异对自燃模型性能的影响：使用丁醇的案例研究
Assessing impacts of discrepancies in model parameters on autoignition model performance: a case study using butanol
摘要：
并排比较详细的动力学模型，使用一种新的工具，以帮助识别物种结构揭示了显着的差异，在许多反应和热化学的许多物种的公布率。我们提出了第一个自动化评估的影响，这些不同的参数上可观察到的数量的利益-在这种情况下，自燃延迟-使用文献实验数据。将丁醇异构体的最新动力学模型导入通用数据库。个别反应速率和热力学参数的物种不同，使用最近的文献中的燃烧模型中遇到的值。考虑了1600多个替代参数的影响。另外，从最近的出版物中收集实验数据，并将其转换为标准的基于YAML的ChemKED格式。利用基于Cantera的模型验证工具PyTeCK，利用生成的模型和实验数据自动模拟自燃过程，判断模型的性能。单独考虑，大多数参数替代对整体模型性能的影响很小，尽管少数有相当大的影响，并进行了更彻底的研究。此外，同时改变多个参数的模型使用遗传算法进行进化，以给出最快和最慢的自燃延迟时间，表明点火延迟时间超过10倍的变化是可能的，只接受，公布的参数的樱桃采摘。本研究中使用的所有数据和软件都是公开的。
英文摘要：
Side-by-side comparison of detailed kinetic models using a new tool to aid recognition of species structures reveals significant discrepancies in the published rates of many reactions and thermochemistry of many species. We present a first automated assessment of the impact of these varying parameters on observable quantities of interest---in this case, autoignition delay---using literature experimental data. A recent kinetic model for the isomers of butanol was imported into a common database. Individual reaction rate and thermodynamic parameters of species were varied using values encountered in combustion models from recent literature. The effects of over 1600 alternative parameters were considered. Separately, experimental data were collected from recent publications and converted into the standard YAML-based ChemKED format. The Cantera-based model validation tool, PyTeCK, was used to automatically simulate autoignition using the generated models and experimental data, to judge the performance of the models. Taken individually, most of the parameter substitutions have little effect on the overall model performance, although a handful have quite large effects, and are investigated more thoroughly. Additionally, models varying multiple parameters simultaneously were evolved using a genetic algorithm to give fastest and slowest autoignition delay times, showing that changes exceeding a factor of 10 in ignition delay time are possible by cherry-picking from only accepted, published parameters. All data and software used in this study are available openly.
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