The Importance of Relative Reaction Rates in the Optimization of Detailed Kinetic Models

Abstract

Numerous mathematical tools intended to adjust rate constants employed in complex detailed kinetic models to make them consistent with multiple sets of experimental data have been reported in the literature. Application of such model optimization methods typically begins with the assignment of uncertainties in the absolute rate constants in a starting model, followed by variation of the rate constants within these uncertainty bounds to tune rate parameters to match model outputs to experimental observations. The present work examines the impact of including information on relative reaction rates in the optimization strategy, which is not typically done in current implementations. It is shown that where such rate constant data are available, the available parameter space changes dramatically due to the correlations inherent in such measurements. Relative rate constants are typically measured with greater relative accuracy than corresponding absolute rate constant measurements. This greater accuracy further reduces the available parameter space, which significantly affects the uncertainty in the model outcomes as a result of kinetic parameter uncertainties. We demonstrate this effect by considering a simple example case emulating an ignition event and show that use of relative rate measurements leads to a significantly smaller uncertainty in the output ignition delay time in comparison with results based on absolute measurements. This is true even though the same range of absolute rate constants is sampled in each case. Implications of the results with respect to the maintenance of physically realistic kinetics in optimized models are discussed, and suggestions are made for the path forward in the refinement of detailed kinetic models. Published 2016. This article is a U.S. Government work and is in the public domain in the USA. Int J Chem Kinet 48: [358][359][360][361][362][363][364][365][366] 2016

The role of correlations in uncertainty quantification of transportation relevant fuel models

“…However, they did not perform a detailed evaluation of this assumption, which is one of the objectives of the present work. In addition, they did not consider other interdependencies, such as relative correlations arising between competing reactions explored within the present work and that of Manion et al [10]. The authors of this latter study suggested that ignoring such information in the forward propagation of uncertainty and model optimization schemes can lead to physically unrealistic results.…”

“…Perhaps for this reason not much attention has been paid to the effects of correlations among reactions. However, from the few studies that have examined these relationships, the effects of neglecting correlations can be non-negligible [6][7][8][9][10]. This phenomenon is also well established in the broader UQ literature for cases where uncertainties are large and correlations are strong between important variables [11].…”

“…While kinetic rates for two reactions cannot be exactly equal, it should be expected that a high degree of correlation exists among very similar reactions. Strong correlations between reactions with multiple branches can arise in cases when the total rate constant of a reaction may be known better than the branching ratios [8], or vice versa [10], attributed to experimental or theoretical limitations. Proper treatment of correlations arising from the use of rate rules in forward propagation of uncertainty was left as an open question in [3], and this serves as part of the motivation of the present study.…”

“…Very recently, in a similarly motivated study using a small representative model, Manion et al [10] showed that for UQ where very precise relative rate constant measurements are available, e.g., branching ratios, treating reactions as independent variables can grossly overestimate the extent of uncertainty in the predicted ignition delays. Their observations are consistent with the present simulation results for Case 5 in Fig.…”

Double Exponential Evaluation under Non-Pseudo-First-Order Conditions: A Mixed Second-Order Process Followed by a First-Order Reaction

“…The deduction of chemical models from experimental observations is a central problem of current chemical kinetics [1][2][3][4][5][6][7][8][9]. In practice, elementary reactions can seldom be investigated without the interference of other processes as kinetic coupling is a very common phenomenon.…”