INCORPORATION OF PARAMETRIC UNCERTAINTY INTO COMPLEX KINETIC MECHANISMS - APPLICATION TO HYDROGEN OXIDATION IN SUPERCRITICAL WATER

In this study, uncertainty analysis is applied to a supercritical wate r hydrogen oxidation mechanism to determine the effect of uncertaintie s in reaction rate constants and species thermochemistry on predicted species concentrations. Forward rate constants and species thermochemi stry are assumed to be the sole contributors to uncertainty in the rea ction model with all other model parameters and inputs treated as dete rministic quantities. The analysis is conducted by treating the model parameters as random variables, assigning each a suitable probability density function, and propagating the parametric uncertainties through to the predicted species concentrations. Uncertainty propagation is p erformed using traditional Monte Carlo (MC) simulation and a new, more computationally efficient, probabilistic collocation method called th e Deterministic Equivalent Modeling Method (DEMM). Both methods predic t virtually identical probability distributions for the resulting spec ies concentrations as a function of time, with DEMM requiring approxim ately two orders of magnitude less computation time than the correspon ding MC simulation. The results of both analyses show that there is co nsiderable uncertainty in all predicted species concentrations. The pr edicted H-2 and O-2 concentrations vary +/- 70% from their median valu es. Similarly, the HO2 concentration ranges from +90 to -70% of its me dian, while the H2O2 concentration varies by +180 to -80%. In addition , the DEMM methodology identified two key model parameters, the standa rd-state heat of formation of HO2 radical and the forward rate constan t for H2O2 dissociation, as the largest contributors to the uncertaint y in the predicted hydrogen and oxygen species concentrations. The ana lyses further show that the change in model predictions due to the inc lusion of real-gas effects, which are potentially important for SCWO p rocess modeling, is small relative to the uncertainty introduced by th e model parameters themselves. (C) 1998 by The Combustion Institute.