ArticleJournal of chemical and engineering data2025
Entropy Scaling of Viscosity IV-Application to 124 Industrially Important Fluids.
Article in Journal of chemical and engineering data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Benchmarking machine learning and molecular dynamics methods for the prediction of the thermophysical properties of organic liquids.Digital discovery · 2026Article
- Article
- Viscosity and Thermal Conductivity Models of 151 Common Fluids Based on Residual Entropy Scaling and Cubic Equations of State.ACS omega · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In our previous work [Yang X.J. Chem. Eng. Data2021, 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.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.
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Registered trials
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