Measurement and prediction of the heat transfer properties of hydrocarbon mixtures with potential application as aluminum cold rolling base oils
Bibliographic record
Abstract
Purpose – The objective of this paper is to measure the differences in heat transfer properties of refined hydrocarbon distillate fractions that are commonly used as base oils in aluminium sheet cold rolling applications and assess if the heat transfer coefficient (HTC) values for these oils can be predicted from their compositions. The composition and physical properties of these fluids affect their tribological behaviour by influencing hydrodynamic lubrication, wear debris removal and cooling. Design/methodology/approach – A purpose-built test rig was used to measure HTCs for a wide range of hydrocarbon solvents used as aluminium cold rolling oils. The results are expressed in the form of the HTCs relative to those of 14- to 16-carbon-chain-length normal paraffins. Measured HTC values were compared to values calculated from oil compositions and from the thermal conductivities of compounds representing different classes of typical oil components. Findings – There were significant differences between the heat transfer properties of various hydrocarbon solvents, and these differences could be estimated from their content of normal and simple iso-paraffins and heavily branched and cyclic hydrocarbons. The HTC of hydrocarbon mixtures increases with the increasing content of n-paraffinic compounds. Originality/value – This paper shows how one can estimate the relative HTCs of oils of known compositions, based on the relative thermal conductivities of model compounds. This is relevant to prediction of cooling properties of aluminium cold rolling base oils.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".