Modeling of Deposition Process in Liquid Fuels

Abstract

The thermal stability of jet fuels results from a complex set of chemical reactions, and the deposition process is further complicated by physical mechanisms such as agglomeration and solvation. Although a vast amount of experimental data has been obtained by several researchers, the specific mechanisms responsible for thermal degradation of fuels and the consequent deposit-formation process are still largely unknown. This is primarily due to the fact that the fluid flow and the heat transfer which influence the deposition process vary significantly from experiment to experiment. It is thought that Computational Fluid Dynamics with Chemistry (CFDC) models can be used to correlate the data obtained from a number of experiments and, thereby, explore this large data base to aid the understanding of the deposition phenomenon. The success of this approach depends on the accuracy of the global-chemistry models used in the CFDC codes. Recent experiments on blended fuel prepared by mixing a hydrotreated fuel with a non-hydrotreated one suggest that the thermal stability of the blend cannot be linearly extrapolated from the thermal-stability characteristics of the neat fuels. The global-chemistry models developed previously are found to be insufficient for the simultaneous prediction of deposition and oxidation rates associated with the blended fuel; however, a nine-step model developed most recently appears to yield qualitatively correct results. To improve the predictive capabilities of this model, the rate expressions for the bulk-fuel reactions are modified by taking the antioxidant behavior in jet fuels into account. The resulting modified nine-step global-chemistry model yielded the experimentally observed thermal stability characteristics for the blended fuels. Predictions made for higher flow-rate and temperature conditions also matched well with the experimental data. Overall, the modified nine-step global-chemistry model is found not only to improve the accuracy in predicting quantities such as oxygen consumption and surface deposits but also to provide additional capabilities for predicting quantities such as hydroperoxides and bulk insolubles.