Finite-rate oxidation model for carbon surfaces from molecular beam experiments

Savio Poovathingal, Thomas E. Schwartzentruber, Vanessa J. Murray, Timothy K. Minton, Graham V. Candler

Research output: Contribution to journalArticle

12 Scopus citations

Abstract

An oxidation model for carbon surfaces has been developed where the gas-surface reaction mechanisms and corresponding rate parameters are based solely on observations from recent molecular beam experiments. In the experiments, a high-energy molecular beam containing atomic and molecular oxygen (93% atoms and 7% molecules) was directed at a high-temperature carbon surface. The measurements revealed that carbon monoxide (CO) is the dominant reaction product and that its formation required a high surface coverage of oxygen atoms. As the temperature of the carbon sample was increased during the experiment, the surface coverage reduced and the production of CO was diminished. Most importantly, the measured time-of-flight distributions of surface reaction products indicated that CO and carbon dioxide (CO2) are predominately formed through thermal reaction mechanisms as opposed to direct abstraction mechanisms. These observations have enabled the formulation of a finite-rate oxidation model that includes surface-coverage dependence, similar to existing finite-rate models used in computational fluid dynamics simulations. However, each reaction mechanism and all rate parameters of the new model are determined individually based on the molecular beam data. The new model is compared to existing models using both zero-dimensional gas-surface simulations and computational fluid dynamics simulations of hypersonic flow over an ablating surface. The new model predicts similar overall mass loss rates compared to existing models; however, the individual species production rates are completely different. The most notable difference is that the new model (based on molecular beam data) predicts CO as the oxidation reaction product with virtually no CO2 production, whereas existing models predict the exact opposite trend.

Original languageEnglish (US)
Pages (from-to)1644-1658
Number of pages15
JournalAIAA journal
Volume55
Issue number5
DOIs
StatePublished - Jan 1 2017

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