New corresponding state principle based correlation of the temperature dependence of the thermal conductivity for pure saturated inorganic liquids
Bibliographic record
Abstract
Abstract In this paper, we propose a corresponding state principle based correlation for the temperature dependence of the thermal conductivity for 15 pure saturated inorganic liquids. The temperature range that this correlation covers is from the triple point temperature ( T tr ) to T 0 , which is about 0.979 times to 0.989 times the critical temperature ( T C ). In order to compare with the data from the reference fluid thermodynamic and transport properties database (REFPROP) database, the average absolute deviations (AADs) of the correlation and others for each liquid are calculated. It is found that the correlation proposed here can reproduce the REFPROP data with AAD <1% for 10 liquids, AAD <2% for 12 liquids, AAD <3% for 14 liquids, and AAD <5% for all of the 15 inorganic liquids considered. Because our proposal is specifically designed for saturated inorganic liquids, compared with previously published correlations which were specifically designed for organic liquids or others, the one proposed herein is overall the most accurate for the saturated inorganic liquids considered. Additionally, we independently process water and obtain a 4‐term polynomial correlation with high accuracy for it. The proposed correlations can be applied directly to accurately estimate the temperature dependence of the thermal conductivities of the liquids considered in thermal engineering areas.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".