Dielectric Constant Predictions for Jet-Range Hydrocarbons: Evaluating the Clausius–Mossotti Relation and Correcting for Molecular Dipole Moments

Abstract

The dielectric constant of aviation turbine fuel is leveraged by aircraft fuel quantity indicator systems (FQISs) using the Clausius–Mossotti relation, which assumes no significant dipole moments. For fossil-derived jet fuel containing relatively consistent fractions of polar aromatic molecules, this is appropriate. However, the interest in sustainable aviation fuels (SAFs) to reduce the carbon intensity of commercial aviation has brought attention to the uncertainty of the FQISs’ compatibility for low- or zero-aromatic fuels exhibiting dielectric constant values outside of the conventional Jet-A range. A dielectric constant model accounting for the varying dipole moments of both aromatic and non-aromatic jet-range hydrocarbons improves the community’s understanding of fuel composition on FQISs’ operability and provides a tool suitable for fuel performance property optimizations while maintaining compatibility with current aircraft systems. Here, the Clausius–Mossotti relation is first evaluated against a training dataset of 240 dielectric constant and density measurements (48 neat hydrocarbons each measured at five temperatures). Then, the dipole moment is calculated for each species of interest using open-source computational chemistry software, and a second-degree binomial regression is performed over the training data to correct for the error in the Clausius–Mossotti relation. The Clausius–Mossotti relation exhibited an R2 value of 0.54, which increased to 0.92 when terms for the dipole correction were added to the model. The improved accuracy from this model establishes a computationally inexpensive framework for modeling theoretical fuel compositions that demonstrate improved performance characteristics (sooting propensity, thermal management, aircraft efficiency, etc.) while remaining within key limiting property constraints, such as the dielectric constant.

Keywords

• sustainable aviation fuel (SAF)
• dielectric constant
• property predictions
• Clausius–Mossotti
• computational chemistry
• fuel-composition modeling