Specifics of Calculating Thermophysical Properties of CO2 and R134a in Critical Point Using NIST REFPROP
Bibliographic record
Abstract
Abstract Thermophysical properties of various fluids (liquids, vapours, and gases) are the fundamental knowledge for the application of these fluids. A computer program can be considered as a very useful tool if it is able to calculate various thermophysical properties of various fluids within a wide range of pressures and temperatures from lower ones and up to critical and supercritical ones. NIST REFPROP is such a program. For tens of years, it can be possibly considered as the best one in the world. However, it is not absolutely perfect. In the previous versions(s) of the NIST REFPROP 9.1 and lower, three basic thermophysical properties, specific heat, thermal conductivity, and volumetric expansivity, were subjected to very significant variations within the critical region. They had almost infinite peak values in the critical point, which was a theoretical approach. In 2018, the latest version was released, Ver. 10.0 (https://www.nist.gov/srd/refprop). It was updated with new fluids, a wider range of pressures and temperatures, and with improved equations / correlations. The objective of this paper is to check if the deficiencies occurred in the previous version(s) have been fixed in NIST REFPROP Ver. 10.0. In this paper, various thermophysical properties of CO2 and R134a have been calculated by using NIST REFPROP Ver 10.0. The results around the critical point have been analyzed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".