Investigation of Dataset Construction Parameters and their Impact on Reaction Model Optimization using PrIMe

Abstract

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

Data Ecosystems for Scientific Experiments: Managing Combustion Experiments and Simulation Analyses in Chemical Engineering

“… Allan et al (2012) ) are an example of a framework inside EOSC to visualize and manage data but lacks a systematic methodology for handling scientific experiments together with predictive models in order to extract value from them automatically. As stated in Mirzayeva et al (2018) , “Methods for determining whether or not the model predictions are consistent with experimental data have been of great interest in combustion research over decades.” PrIMe was one of the first attempts to build a systematic repository of experiments in the combustion domain (cfr. Frenklach (2007) ).…”

“… Frenklach (2007) ). PrIMe includes a database, that can be used to perform selective analyses of experiments to validate reaction model parameters as discussed in Mirzayeva et al (2018) . In particular, research has focused on analyzing the data features, with emphasis on uncertainty of experimental data.…”