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.
Bound-to-Bound Data Collaboration (B2BDC) provides a natural framework for addressing both forward and inverse uncertainty quantification problems. In this approach, QOI (quantity of interest) models are constrained by related experimental observations with interval uncertainty. A collection of such models and observations is termed a dataset and carves out a feasible region in the parameter space. If a dataset has a nonempty feasible set, it is said to be consistent. In real-world applications, it is often the case that collections of models and observations are inconsistent. Revealing the source of this inconsistency, i.e., identifying which models and/or observations are problematic, is essential before a dataset can be used for prediction. To address this issue, we introduce a constraint relaxation-based approach, entitled the vector consistency measure, for investigating datasets with numerous sources of inconsistency. The benefits of this vector consistency measure over a previous method of consistency analysis is demonstrated in two realistic gas combustion examples.
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
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