Aviation Turbine Fuel Thermal Conductivity: A Predictive Approach Using Entropy Scaling-Guided Machine Learning with Experimental Validation.

ABSTRACT: Although typical aircraft fuel thermal management analysis relies upon temperature-dependent thermodynamic and transport properties of aviation turbine fuel, the variation in properties associated with compositional variation in fuels and the subsequent impacts on system performance are not well established. With this in mind, the present work aimed to develop a predictive model of aviation turbine fuel thermal conductivity which utilized only compositional (hydrocarbon) and state (temperature and pressure) inputs and had errors within the bounds of typical uncertainty of the associated test data (3%). A novel modeling approach was developed to predict thermal conductivity using pseudo-component entropy scaling techniques with a machine learning-developed intermediate step in the overall m

SUBMITTER: Malatesta WA 

PROVIDER: S-EPMC8567267 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature