Thermal Conductivity from Entropy Scaling: A Group-Contribution Method

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

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 model. Simple hyper-parameter optimization techniques were developed to promote model stability, computational efficiency, and long-term repeatability of the novel architecture. Validation data were gathered which included four fuel samples (3 JP-5 and 1 F-24), which underwent two-dimensional gas chromatography compositional testing and temperature-dependent density, viscosity, thermal conductivity, and specific heat testing. Model performance on the validation data set assembled from the literature data and present efforts showed an average deviation of 1% and an absolute average deviation of 2.5%. Model outputs outside the validation range are well-behaved and are expected to perform well on a large range of liquid hydrocarbon mixtures with the overall process expected to be well suited to prediction of other properties.

Entropy Scaling of Viscosity - II: Predictive Scheme for Normal Alkanes

In this work, a residual entropy value 6/10 of the way between the critical point and a value of -2/3 of Boltzmann's constant is shown to collapse the scaled viscosity for the family of normal alkanes. Based on this approach, a nearly universal correlation is proposed that can reproduce 95% of the experimental data for normal alkanes within ±18% (without removal of clearly erroneous data). This universal correlation has no new fluid-specific empirical parameters and is based on experimentally accessible values. This collapse is shown to be valid to a residual entropy half way between the critical point and the triple point, beyond which the macroscopically-scaled viscosity has a super-exponential dependence on residual entropy, terminating at the triple point. A key outcome of this study is a better understanding of entropy scaling for fluids with intramolecular degrees of freedom. A study of the transport and thermodynamic properties at the triple point rounds out the analysis.

Residual entropy model for predicting the viscosities of dense fluid mixtures

In this work, we have investigated the mono-variant relationship between the reduced viscosity and residual entropy in pure fluids and in binary mixtures of hydrocarbons and hydrocarbons with dissolved carbon dioxide. The mixtures considered were octane + dodecane, decane + carbon dioxide, and 1,3-dimethylbenzene (m-xylene) + carbon dioxide. The reduced viscosity was calculated according to the definition of Bell, while the residual entropy was calculated from accurate multi-parameter Helmholtz-energy equations of state and, for mixtures, the multi-fluid Helmholtz energy approximation. The mono-variant dependence of reduced viscosity upon residual molar entropy was observed for the pure fluids investigated, and by incorporating two scaling factors (one for reduced viscosity and the other for residual molar entropy), the data were represented by a single universal curve. To apply this method to mixtures, the scaling factors were determined from a mole-fraction weighted sum of the pure-component values. This simple model was found to work well for the systems investigated. The average absolute relative deviation (AARD) was observed to be between 1% and 2% for pure components and a mixture of similar hydrocarbons. Larger deviations, with AARDs of up to 15%, were observed for the asymmetric mixtures, but this compares favorably with other methods for predicting the viscosity of such systems. We conclude that the residual-entropy concept can be used to estimate the viscosity of mixtures of similar molecules with high reliability and that it offers a useful engineering approximation even for asymmetric mixtures.