Evaluation of the Uncertainty in JP-7 Kinetics Models Applied to Scramjets

One of the challenges of designing and flying a scramjet-powered vehicle is the difficulty of preflight testing. Ground tests at realistic flight conditions introduce several sources of uncertainty to the flow that must be addressed. For example, the scales of the available facilities limit the size of vehicles that can be tested and so performance metrics for larger flight vehicles must be extrapolated from ground tests at smaller scales. To create the correct flow enthalpy for higher Mach number flows, most tunnels use a heater that introduces vitiates into the flow. At these conditions, the effects of the vitiates on the combustion process is of particular interest to the engine designer, where the ground test results must be extrapolated to flight conditions. In this paper, the uncertainty of the cracked JP-7 chemical kinetics used in the modeling of a hydrocarbon-fueled scramjet was investigated. The factors that were identified as contributing to uncertainty in the combustion process were the level of flow vitiation, the uncertainty of the kinetic model coefficients and the variation of flow properties between ground testing and flight. The method employed was to run simulations of small, unit problems and identify which variables were the principal sources of uncertainty for the mixture temperature. Then using this resulting subset of all the variables, the effects of the uncertainty caused by the chemical kinetics on a representative scramjet flow-path for both vitiated (ground) and nonvitiated (flight) flows were investigated. The simulations showed that only a few of the kinetic rate equations contribute to the uncertainty in the unit problem results, and when applied to the representative scramjet flowpath, the resulting temperature variability was on the order of 100 K. Both the vitiated and clean air results showed very similar levels of uncertainty, and the difference between the mean properties were generally within the range of uncertainty predicted.