Assessment of T-History Method Variants to Obtain Enthalpy–Temperature Curves for Phase Change Materials With Significant Subcooling
Bibliographic record
Abstract
To assess the potential of thermal energy storage systems using phase change materials (PCMs), numerical simulations rely on an enthalpy–temperature curve (or equivalent specific heat curve) to model the PCM thermal storage behavior. The so-called “T-history method” can be used to obtain an enthalpy–temperature curve (H versus T) through conventional laboratory equipment and a simple experimental procedure. Different data processing variants of the T-history method have been proposed yet no systematic comparison between these versions exists in the literature nor is there a consensus as to which should be used to obtain reliable enthalpy–temperature curves. In this paper, an inorganic salt hydrate is tested in both heating and cooling. Four different data processing variants of the T-history method are used to characterize the PCM and produce enthalpy–temperature curves for this original experimental data set. Differences in the results produced by the different methods are discussed, the issues encountered are indicated, and possible approaches to overcome these problems are provided. A specific variant is recommended when using the T-history method to determine enthalpy–temperature curves. For PCMs that exhibit subcooling, an alternative interpretation using an absolute temperature interval is described so that the subcooling phase is taken into account in the enthalpy–temperature curve.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".