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Generating energy from combustion is prone to pollutant formation. In energy systems working under non-premixed combustion mode, rapid mixing is required to increase the heat release rates. However, local extinction and re-ignition may occur, resulting from strong turbulence–chemistry interaction, especially when rates of mixing exceed combustion rates, causing harmful emissions and flame instability. Since the physical mechanisms for such processes are not well understood, there are not yet combustion models in large eddy simulation (LES) context capable of accurately predicting them. In the present study, finite-rate scale similarity (SS) combustion models were applied to evaluate both heat release and combustion rates. The performance of three SS models was a priori assessed based on the direct numerical simulation of a temporally evolving syngas jet flame experiencing high level of local extinction and re-ignition. The results show that SS models following the Bardina’s “grid filtering” approach (A and B) have lower errors than the model based on the Germano’s “test filtering” approach (C), in terms of mean, root mean square (RMS), and local errors. In mean, both Bardina’s based models capture well the filtered combustion and heat release rates. Locally, Model A captures better major species, while Model B retrieves radicals more accurately.

In this work, the performances of two recently developed finite-rate dynamic scale similarity (SS) sub-grid scale (SGS) combustion models (named DB and DC) for non-premixed turbulent combustion are a priori assessed based on three Direct Numerical Simulation (DNS) databases. These numerical experiments feature temporally evolving syngas jet flames with different Reynolds (Re) numbers (2510, 4487 and 9079), experiencing a high level of local extinction. For comparison purposes, the predicting capability of these models is compared with three classical non-dynamic SS models, namely the scale similarity resolved reaction rate model (SSRRRM or A), the scale similarity filtered reaction rate model (SSFRRM or B), and a SS model derived by the "test filtering" approach (C), as well as an existing dynamic version of SSRRRM (DA). Improvements in the prediction of heat release rates using a new dynamic model DC are observed in high Re flame case. By decreasing Re, dynamic procedures produce results roughly similar to their non-dynamic counterparts. In the lowest Re, the dynamic methods lead to higher errors. Keywords Dynamic scale similarity combustion model • Finite-rate SGS combustion model • A priori DNS analysis • LES • Non-premixed jet flame • Extinction re-ignition Electronic supplementary material The online version of this article (