Assessing impacts of discrepancies in model parameters on autoignition model performance: a case study using butanol

TL;DR

This study evaluates how discrepancies in kinetic model parameters affect autoignition predictions for butanol, using automated tools and experimental data to identify influential parameters and demonstrate the potential for large variations in ignition delay times.

Contribution

It introduces an automated assessment framework combining literature data, model variation, and experimental validation to analyze parameter impacts on autoignition models.

Findings

Most parameter variations have minimal impact on model performance.

Certain parameters significantly influence autoignition delay.

Large variations in ignition delay times are achievable through parameter selection.

Abstract

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.