THERMODYNAMIC AND TRANSPORT PROPERTIES OF AVIATION TURBINE FUEL: PREDICTIVE APPROACHES USING ENTROPY SCALING GUIDED MACHINE LEARNING WITH EXPERIMENTAL VALIDATION

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dc.contributor.advisorYang, Baoen_US
dc.contributor.authorMalatesta, William Anthonyen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2022-06-15T05:34:12Z
dc.date.available2022-06-15T05:34:12Z
dc.date.issued2022en_US
dc.description.abstractWith issues such as increasing power generation densities, design restrictions on heat rejection, and finite heat sink capacity, fighter aircraft face significant thermal management challenges which are driving research from component to system level technology regimes. As aviation turbine fuel often represents half of the take-off weight of aircraft, it is an integral piece of the thermal management puzzle and generally regarded as the primary internal heat sink for fighter aircraft. Though typical thermal performance analysis requires temperature dependent transport and thermodynamic properties of fuel, the variation in properties associated with the fact that fuels are mixtures with varying composition is not well understood. As such, the present work aimed to define bounds of density, viscosity, thermal conductivity, and specific heat of aviation turbine fuel as functions of composition and temperature by developing numerical models which were validated against test data. Data collected for this work included 96 samples with measured composition and viscosity at a single temperature (54 F-24, 26 JP-8, 11 Jet A, 5 Jet A-1), and four samples (3 JP-5 and 1 F-24) which underwent compositional and temperature dependent property testing. The novel modeling approaches to predict viscosity and thermal conductivity of jet fuels employed pseudo component entropy scaling techniques with artificial neural networks occupying an intermediate step in the overall model. Simple hyper-parameter optimization techniques were developed to promote model stability, computational efficiency, and long-term repeatability of the approach. Additionally, a model for predicting temperature dependent isobaric specific heats of liquids based on atomic density was developed for well-defined hydrocarbon mixtures. Model performance against test data showed average deviations of 0.1%, 1%, and -2% for viscosity, thermal conductivity, and specific heat respectively. Utilizing the compositional data collected, the models were then used to estimate bounds of these properties. Analysis of Prandtl numbers calculated using the modeled property ranges suggests that the observed variation in properties should be considered during a thorough aircraft thermal management design or performance analysis effort.en_US
dc.identifierhttps://doi.org/10.13016/ee5g-1qxj
dc.identifier.urihttp://hdl.handle.net/1903/28709
dc.language.isoenen_US
dc.subject.pqcontrolledThermodynamicsen_US
dc.subject.pquncontrolledAviation Turbine Fuelen_US
dc.subject.pquncontrolledEntropy Scalingen_US
dc.subject.pquncontrolledFluid Propertiesen_US
dc.subject.pquncontrolledMachine Learningen_US
dc.titleTHERMODYNAMIC AND TRANSPORT PROPERTIES OF AVIATION TURBINE FUEL: PREDICTIVE APPROACHES USING ENTROPY SCALING GUIDED MACHINE LEARNING WITH EXPERIMENTAL VALIDATIONen_US
dc.typeDissertationen_US

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