An automated target species selection method for dynamic adaptive chemistry simulations

TL;DR

This paper introduces an automated method based on the relative importance index (RII) for selecting target species in dynamic adaptive chemistry simulations, improving accuracy and reducing user expertise requirements.

Contribution

The RII method for target species selection is developed and validated, enabling accurate, adaptive, and less user-dependent chemistry simulations for different fuels.

Findings

RII maintains accuracy where static sets fail.

RII outperforms static sets in engine simulations.

RII reduces computational overhead at higher thresholds.

Abstract

The relative importance index (RII) method for determining appropriate target species for dynamic adaptive chemistry (DAC) simulations using the DRGEP method is developed. The accuracy and effectiveness of this RII method is validated for two fuels: n-heptane and isopentanol. The RII method determines appropriate DRGEP target species solely from the local thermo-chemical state of the simulation, ensuring that accuracy will be maintained. Further, the RII method reduces the expertise required of users to select DRGEP target species sets appropriate to the combustion phenomena under consideration. Constant volume autoignition simulations run over a wide range of initial condi- tions using detailed reaction mechanisms for n-heptane and isopentanol show that the RII method is able to maintain accuracy even when traditional static target species sets fail, and are even more accurate than expert-selected target species sets. Additionally, the accuracy and efficiency of the RII method are compared to those of static target species sets in single-cell engine simulations under homogeneous charge compression ignition conditions. For simulations using more stringent DRGEP thresholds, the RII method performs similarly to that of the static target species sets. With a larger DRGEP threshold, the RII method is significantly more accurate than the static target species sets without imposing significant computational overhead. Furthermore, the applicability of the RII method to a DRG-based DAC scheme is discussed.