Impact of scalar mixing uncertainty on the predictions of reactor-based closures: Application to a lifted methane/air jet flame

This work is devoted to quantify the predictive uncertainty in RANS simulation of a non-premixed lifted flame due to uncertainty in the model parameters of the scalar dissipation rate transport equation. The un-certainty propagation and the global sensitivity analysis of the effect of such parameters on the quantities of interest (QoIs) is performed employing Polynomial Chaos Expansions as surrogate models of the uncertain response. This approach is applied on a lifted methane-air jet flame in vitiated coflow, already experimentally investigated by Cabra et al [1]. The results show the effectiveness of the approach to provide predictions with estimates of uncertainty. It is shown that the the uncertainty in the mixture fraction and temperature is neg-ligible as long as only pure mixing happens, then it becomes significant in the regions where ignition begins, starting from z/D = 30 . Of the four parameters considered, i.e., CD1, CD2, CP1 and CP2, main and total effect sensitivity indices show that the largest contribution to the uncertainty in the flame temperature is given by the two dissipation parameters CD1 and CD2, while the production parameter CP2 has almost negligible im-pact on the predictions. Lastly, the surrogate models are used to determine an optimum set of parameters that minimizes the distance with the experimental measures, leading to improved predictions of the QoIs. & COPY; 2022 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Automated summary

This work focuses on quantifying the predictive uncertainty in RANS simulation of a non-premixed lifted flame by considering the uncertainty in the model parameters of the scalar dissipation rate transport equation. Polynomial Chaos Expansions are used as surrogate models to analyze the uncertainty propagation and global sensitivity of these parameters on the quantities of interest (QoIs). The study demonstrates the effectiveness of this approach in providing predictions with estimates of uncertainty and identifies the significant role of certain parameters in affecting the flame temperature predictions.