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An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model−data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. The initial H 2 /CO reaction model, assembled from 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.

This study initiates the gradual upgrade of the DLR reaction database. The upgrade plan has two main steps: an optimisation of the C 1-C 4 oxidation chemistry and a revision of the polyaromatic hydrocarbon (PAH) formation sub-mechanism based thereupon. The present paper reports the main principles applied to model improvements and results obtained for the acetylene (C 2 H 2) oxidation sub-mechanisms. The principle acetylene oxidation reactions have been revised as well as the detailed chemistry of important intermediates, i.e. methylene, ethynyl, vinylperoxy radical and also diacetylene, vinylacetylene and higher diacetylenes, important for PAH formation. The uncertainty intervals of the studied reactions were statistically evaluated, providing general bounds for the performed modifications to reaction rate coefficients. The first stage of the presented update was performed through revision of the thermochemical data and model optimisation on ignition delay data and laminar flame speed data, since they exhibit lower uncertainty in comparison to species profile data. The final model optimization was obtained through simulations of concentration profiles measured in shock tubes and laminar flames for improvement of the reaction paths and rate coefficients related to acetylene pyrolysis and PAH precursor formation. Approximately 500 data points were analysed. The updated reaction mechanism predicts all simulated experimental data, also not included in the optimisation loop data prom plug flow and jet-stirred reactors, either with good or satisfactory agreement. It was found that the vinylperoxy radical formation and consumption dictate the reaction progress at low temperatures. The performed study clearly determined that acetylene combustion proceeds through the strongly coupled reaction paths of fuel oxidation and PAH precursor formation; the same species are involved in these parallel processes. Therefore, the self-consistent reaction model for acetylene combustion could be obtained only by an optimisation performed on the experimental dataset encompassing both processes.

The current paper presents a continuation of the development of a modern methodology for the construction of uncertainty-quantified chemical reaction models on the base of the Bound-to-Bound Data Collaboration (B2BDC) module of the automated data-centric infrastructure PrIMe. Some problems, postulated in the recent studies, are in the focus of the present investigation. The question of targets amount (experimental data, Quantities of Interest (QoI)) selected for the analysis has been studied. To investigate this, the PrIMe dataset is augmented. The influence of dataset extension on the dataset consistency, feasible parameter set, and model optimization is studied and an algorithm for the selection of QoI in each experimental set is postulated. The approach of combined methods of scalar consistency measure, SCM, and vector consistency measure, VCM, for consistency analysis are adapted and successfully implemented. Predictions of the LS-optimized mechanism are compared against a wide range of experimental data of laminar premixed flames and shock tube ignition delay times. Good agreement of model predictions with the experimental measurements is obtained. Nomenclature ϕ = equivalence ratio P 5 = pressure behind reflected shock waves in shock-tube experiments QoI = quantity of interest T 5 = temperature behind reflected shock waves in shock-tube experiments T 0 = initial temperature in laminar flame experiments UB = uncertainty bounds 1 PhD Student, Chemical Kinetics Department, Aziza.Mirzayeva@dlr.de

A module of PrIMe automated data-centric infrastructure, Bound-to-Bound Data Collaboration (B2BDC), was used for the analysis of systematic uncertainty and data consistency of the H2/CO reaction model (73/17). In order to achieve this purpose, a dataset of 167 experimental targets (ignition delay time and laminar flame speed) and 55 active model parameters (pre-exponent factors in the Arrhenius form of the reaction rate coefficients) was constructed. Consistency analysis of experimental data from the composed dataset revealed disagreement between models and data. Two consistency measures were applied to identify the quality of experimental targets (Quantities of Interest, QoI): scalar consistency measure, which quantifies the tightening index of the constraints while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds; and a newly-developed method of computing the vector consistency measure (VCM), which determines the minimal bound changes for QoIs initially identified as inconsistent, each bound by its own extent, while still ensuring the existence of a set of the model parameter values whose associated modeling output predicts the experimental QoIs within the uncertainty bounds. The consistency analysis suggested that elimination of 45 experimental targets, 8 of which were self- inconsistent, would lead to a consistent dataset. After that the feasible parameter set was constructed through decrease uncertainty parameters for several reaction rate coefficients. This dataset was subjected for the B2BDC framework model optimization and analysis on. Forth methods of parameter optimization were applied, including those unique in the B2BDC framework. The optimized models showed improved agreement with experimental values, as compared to the initially-assembled model. Moreover, predictions for experiments not included in the initial dataset were investigated. The results demonstrate benefits of applying the B2BDC methodology for development of predictive kinetic models.