Quantifying Uncertainty in Chemical Systems Modeling

This study compares two techniques for uncertainty quantification in chemistry computations, one based on sensitivity analysis and error-propagation, and the other on stochastic analysis using polynomial chaos techniques. The two constructions are studied in the context of H2-O2 ignition under supercritical-water conditions. They are compared in terms of their prediction of uncertainty in species concentrations and the sensitivity of selected species concentrations to given parameters. The formulation is extended to 1-D reacting-flow simulations. The computations are used to study sensitivities to both reaction rate preexponentials and enthalpies, and to examine how this information must be evaluated in light of known, inherent parametric uncertainties in simulation parameters. The results indicate that polynomial chaos methods provide similar firstorder information to conventional sensitivity analysis, while preserving higher-order information that is needed for accurate uncertainty quantification and for assigning confidence intervals on sensitivity coefficients. These higher-order effects can be significant, as the analysis reveals substantial uncertainties in the sensitivity coefficients themselves.