Use of the inverse method to determine the thermal properties of liquid n-octadecane accounting for natural convection effect
Bibliographic record
Abstract
This paper numerically makes use of the inverse method coupled with experimenal results obtained through the fluxmetric method in the Laboratoire de Génie Civil et géo-Environnement (LGCgE) to determine the specific heat, thermal conductivity and thermal expansion coefficient of liquid n -octadecane in an enclosure accounting for natural convection present in the experimental system. The impact of ommiting the presence of natural convection for the thermophysical propertie determination is studied and shows very little effect on the specific heat value but a large error (by a factor of two in this study) on the thermal conductivity value. The correct properties identified when accounting for natural convection ( ρ = 2150 J/kg‧K; k = 0.16 W/m‧K; β = 9.38 × 10 −4 K −1 ) are consistent with the literature. Additional direct numerical work was done to determine the impact of using the different thermal conductivity values, comparing numerical to additional expermental restuls where natural convection was stronger. The thermal conductivity value played a minial role when simulating the behavior of the systems rightly accounting for natural convection. Combining the numerical and experimental resutls allowed the calculation of encolsure specific Nusselt numbers for the various experimental conditions used and a comparison to known correlations. It was determine that the Nusselt number obtained in the liquid n -octadecane were larger by 10 to 48%.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".