Entropy scaling of viscosity IV-Application to 124 industrially important fluids

• Verfasst von: Martinek, Viktor [VerfasserIn]
• Verfasst von: Bell, Ian [VerfasserIn]
• Verfasst von: Herzog, Roland [VerfasserIn]
• Verfasst von: Richter, Markus [VerfasserIn]
• Verfasst von: Yang, Xiaoxian [VerfasserIn]
• Titel: Entropy scaling of viscosity IV-Application to 124 industrially important fluids
• Verf.angabe: Viktor Martinek, Ian Bell, Roland Herzog, Markus Richter, and Xiaoxian Yang
• E-Jahr: 2025
• Jahr: February 13, 2025
• Umfang: 16 S.
• Fussnoten: Gesehen am 28.03.2025
• Titel Quelle: Enthalten in: Journal of chemical & engineering data
• Ort Quelle: Columbus, Ohio : American Chemical Society, 1959
• Jahr Quelle: 2025
• Band/Heft Quelle: 70(2025), 2, Seite 727-742
• ISSN Quelle: 1520-5134
• DOI: doi:10.1021/acs.jced.4c00451
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: EQUATIONS
• Sach-SW: MODEL
• Sach-SW: PURE
• Sach-SW: REFRIGERANTS
• Sach-SW: THERMAL-CONDUCTIVITY
• Sach-SW: TRANSPORT-PROPERTIES
• K10plus-PPN: 1920759085

Abstract

In our previous work [Yang, X. et al. J. Chem. Eng. Data 2021, 66, 1385-1398], a residual entropy scaling (RES) approach was developed to link viscosity to residual entropy using a 4-term power function for 39 refrigerants. In further research [Yang, X. et al. Int. J. Thermophys. 2022, 43, 183], this RES approach was extended to 124 pure fluids containing fluids from light gases (hydrogen and helium) to dense fluids (e.g., heavy hydrocarbons) and fluids with strong association force (e.g., water). In these previous research studies, the model was developed by manual optimization of the power function. The average absolute relative deviation (AARD) of experimental data from the RES model is approximately 3.36%, which is higher than the 2.74% obtained with the various models in REFPROP 10.0. In the present work, the power function was optimized by iteratively fitting the global (fluid-independent power terms) and local parameters (fluid-specific and group-specific parameters) and screening the experimental data. The resulting equation has only three terms instead of four. Most notably, the AARD of the new RES model is reduced down to 2.76%; this is very close to the various multiparameter models in REFPROP 10.0, while the average relative deviation (ARD) amounts to 0.03%, which is smaller than REFPROP 10.0's 0.7%. A Python package is provided for the use of the developped model.