Accounting for uncertainty in RCCE species selection

A framework is presented to quantify, based on Bayesian evidence, the relative plausibility of species selection options in rate-controlled constrained equilibrium (RCCE) reduced chemical models, accounting for uncertainty in the kinetic parameters and experimental data used to refine them. This approach balances the joint goals of matching available data and avoiding overfitting, which is well-understood to limit extrapolative capacity for true prediction. The methodology is applied to homogeneous autoignition, where predictions are known to be particularly sensitive to chemical model details, specially at low temperatures. It is first introduced for hydrogen–air autoignition using an established mechanism, then demonstrated in two applications of methane–air autoignition using the larger GRI-1.2 mechanism. This larger mechanism significantly increases the computational cost of model selection (though not of the subsequent application in predictions), which is alleviated with a time-scale-guided pre-sorting strategy. Uses and extensions of this new formulation are discussed.