Modeling RP-1 Fuel Advanced Distillation Data using Comprehensive Two-Dimensional Gas Chromatography Coupled with Time-of-Flight Mass Spectrometry and Partial Least Squares Analysis
AIR FORCE RESEARCH LAB EDWARDS AFB CA AEROSPACE SYSTEMS DIRECTORATE
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Recent efforts in predicting rocket propulsion RP-1 fuel performance through modeling put greater emphasis on obtaining detailed and accurate fuel properties, as well as to elucidate the relationships between fuel composition and their properties. Herein, we study multidimensional chromatographic data obtained utilizing the instrumental platform that included comprehensive two-dimensional gas chromatography combined with time-of-flight mass spectrometry GC x GC -TOFMS to analyze RP-1 fuels. For GC x GC separations, RTX-wax polar stationary phase and RTX-1 non-polar stationary phase columns were implemented for the primary and secondary dimensions, respectively, to separate the chemical compound classes alkanes, cycloalkanes, aromatics, etc, providing a significant level of chemical compositional information. The GC x GC -TOFMS data were analyzed using partial least-squares regression PLS chemometric analysis, specifically to model and predict advanced distillation curve ADC data for ten RP-1 fuels that were previously analyzed using the ADC method. The PLS modeling provides insight into the chemical species that impact the observed changes in the previously collected ADC data. The PLS modeling correlates compositional information found in the GC x GC - TOFMS chromatograms of each RP-1 fuel, and their respective ADC, and allows prediction of the ADC for each RP-1 fuel with good precision and accuracy. The predictive power of the overall method via PLS modeling was assessed using leave-one-out cross-validation LOOCV yielding root-mean-square error of cross-validation RMSECV with low values, typically below 2.0 deg C, at each distilled measurement point during the ADC analysis.
- Rocket Engines