PNNL

Abstract

There is an increased need to more fully assess and control the composition of kerosene based rocket propulsion fuels, namely RP-1 and RP-2. In particular, it is crucial to be able to make better quantitative connections between the following three attributes: (a) fuel performance, (b) fuel properties (flash point, density, kinematic viscosity, net heat of combustion, hydrogen content, etc) and (c) the chemical composition of a given fuel (i.e., specific chemical compounds and compound classes present as a result of feedstock blending and processing). Indeed, recent efforts in predicting fuel performance through modeling put greater emphasis on detailed and accurate fuel properties and fuel compositional information. In this regard, advanced distillation curve (ADC) metrology provides improved data relative to classical boiling point and volatility curve techniques. Using ADC metrology, data obtained from RP-1 and RP-2 fuels provides compositional variation information that is directly relevant to predictive modeling of fuel performance. Often, in such studies, one-dimensional gas chromatography (GC) combined with mass spectrometry (MS) is typically employed to provide chemical composition information. Building on approaches using GC-MS, but to glean substantially more chemical composition information from these complex fuels, we have recently studied the use of comprehensive two dimensional gas chromatography combined with time-of-flight mass spectrometry (GC × GC - TOFMS) to provide chemical composition data that is significantly richer than that provided by GC-MS methods. In this report, by applying multivariate data analysis techniques, referred to as chemometrics, we are able to readily model (correlate) the chemical compositional information from RP-1 and RP-2 fuels provided using GC × GC - TOFMS, to the fuel property information such as that provided by the ADC method and other specification properties. We anticipate that this new chemical analysis strategy will have broad implications for the development of high fidelity composition-property models, leading to an optimized approach to fuel formulation and specification for advanced engine cycles.