Discussion on: “Robust Fault Detection Observer and Fault Estimation Filter Design for LTI Systems Based on GKYP Lemma”

Abstract

For the calculation of thermophysical properties of fluids with molecular dynamics (MD) simulations, effective force fields (FFs) which are optimized against experimental vapor-liquid equilibria are often used. Examples for this FF category are TraPPE, OPLS, and AMBER. An alternative are simplified pair-specific, ab initio-based FFs (AI-FFs), which are derived from quantum-chemical calculations in the limit of zero-density in combination with the kinetic theory of gases, and, thus, feature a predictive character. In the present study, we show with the help of selected binary fluid mixtures that the predictive power of MD simulations in calculating Fick diffusion coefficients can be improved using simplified pair-specific AI-FFs based on corresponding FFs for the pure substances. To evaluate the performance of the new AI-FFs in comparison with the TraPPE FFs, binary mixtures consisting of methane, carbon dioxide, and propane were investigated from the superheated vapor to the gas state and supercritical region up to the compressed liquid state. For the determination of the Fick diffusion coefficient at pressures between (0.1 and 12) MPa, temperatures between (293 and 355) K, and mole fractions between 0.05 and 0.95, separate simulations for the analysis of the Maxwell–Stefan diffusion coefficient and the thermodynamic factor were performed considering system size effects. With the exception of the compressed liquid state and regions in vicinity of the two-phase boundary, where the TraPPE FFs are generally superior, the AI-FFs show improved predictions for the Fick diffusion coefficient with average expanded statistical uncertainties of 12%. This could be demonstrated by comparison of the simulation results with the theoretical ab initio calculations and the available experimental data, resulting in average absolute deviations of 7% and 13% for the AI-FFs and TraPPE FFs.