The oxidation of hydrocarbon fuels proceeds through the attack of small radicals such as H and CH3 on large molecules. These radicals abstract H atoms from the large molecules, which then usually proceed by β-scission to form C2H4 and C3H6. Quantifying these rates is critical to the development of chemical models for the oxidation of hydrocarbons. Study of this reaction system is confounded by the rapid dissociation of the intermediate radicals, which produces both additional H and additional CH3, making it difficult to separate the behavior of the two radical species under many conditions. In this work, we propose an experimental design algorithm that will be applied to measuring H and CH3 attack rates on n-butane using a single-pulse shock tube. This design algorithm is based on the Method of Uncertainty Minimization using Polynomial Chaos Expansions (Sheen & Wang, Combust Flame 158, pp 2258-2374, 2011). We generate a set of proposed measurements covering a wide range of initial reactant concentrations, temperatures, and species concentration measurements, for a total of 160 proposed measurements. To simulate the proposed experiments, we use the Jet Surrogate Fuel model as a candidate model. We then use a machine-learning algorithm to identify the best subset of experiments to perform. Seven are elected as the best set. We compare the algorithms performance against an expert-recommended set of experimental measurements. The machine-generated experimental set performs better than the expert-generated experimental set. Therefore, the machine learning algorithm is therefore a suitable surrogate for an experts evaluation of a set of experiments, and can be applied to many other database analysis and constraint problems.
Journal of Physical Chemistry A
shock tube, kinetics, methyl radicals, H atoms, butane, uncertainty analysis, reaction networks