Material screening metrics and optimal performance of an active magnetic regenerator
Bibliographic record
Abstract
A variety of metrics to rank the magnetocaloric materials can be found in the literature, but a quantitative assessment showing their efficacy has not been reported. A numerical model of an active magnetic regenerator cycle is used to assess the predictive ability of a set of material metrics. The performance of eight cases of known magnetocaloric material (including first order MnFeP1-xAsx and second order materials Gd, GdDy, Tb), and 15 cases of hypothetical materials are considered. Using a fixed regenerator matrix geometry, magnetic field, and flow waveforms, the maximum exergetic cooling power of each material is identified. Several material screening metrics such as relative cooling power (RCP) are tested and a linear correlation is found between maximum RCP and the maximum exergetic cooling power. The sensitivity of performance to variations in the hot side and cold side temperatures from the conditions giving maximum exergetic power are determined.
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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".