Predicting the Viscosity of Asymmetric Hydrocarbon Mixtures with the Expanded Fluid Viscosity Correlation
Bibliographic record
Abstract
Mass-based mixing rules are proposed for the Expanded Fluid viscosity correlation that are suitable for asymmetric mixtures and replace the original volumetric mixing rules. The Expanded Fluid correlation provides viscosity values as a function of fluid density and characterizes each pure compound with three fluid-specific parameters: c 2, ρ s o and c 3, when using experimental densities and two parameters, c 2, ρ s o, when using a cubic equation of state. The proposed set of mixing rules predicts the viscosity of over 90 binary mixtures with overall average absolute relative deviations (AARD) of 2.9% and 7.8% using measured densities and densities estimated from an equation of state, respectively. To improve the predictions in equation of state applications, a binary interaction parameter is introduced to the mixing rules, compensating for inaccuracies associated with density predictions from simple cubic equations of state. Using fitted interaction parameters, the overall AARD is 3.6%. The binary interaction parameters are generalized as a function of the molecular weight and Watson K-factor. Using these generalized binary interaction parameters, the overall AARD is reduced to 5.4% for over 90 binary mixtures. The proposed mixing rules and the general binary interaction parameters correlations are also tested on an independent data set which includes 40 binary, ternary, and multicomponent mixtures. The overall AARDs are 8.4% using measured densities and 11.6 and 7.1% using equation-of-state based densities with zero and generalized binary interaction parameters, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".