Experimental study of 2-layer regenerators using Mn–Fe–Si–P materials
Bibliographic record
Abstract
Abstract This work describes an experimental study of a two layer active magnetic regenerator with varying transition temperature spacing. The transition temperature of the materials is based on the specific heat peak of the materials. A transition temperature based on the average of the heating and cooling curves at zero Tesla field value is used to refer to the materials throughout this paper. This study uses five Mn–Fe–Si–P materials with transition temperatures of 294.6 K, 292.3 K, 290.7 K, 282.5 K and 281.4 K. Six different regenerators are tested. A reference configuration is tested using the 294.6 K material a hot side layer and with a second passive layer of lead spheres as cold side layer. Followed by four configurations that use the same 294.6 K material as hot side layer, but where each configuration uses a different cold side material. For the second active layer the materials are used in sequence; 292.3 K, 290.7 K, 282.5 K and 281.4K. Lastly, a sixth configuration uses the 292.3 K and 282.5 K materials. For each configuration, the temperature span is measured for rejection temperatures from 40 °C to 9 °C and at 0 W and 2 W applied load. Experimental results for temperature span and exergetic cooling power are compared based on the differences from the reference configuration. Materials are analysed based on material performance metrics such as peak adiabatic temperature change, peak entropy change and RCP(s) values. For the cases considered, a closer transition temperature spacing generally gives a greater temperature span and exergetic cooling power than further spaced materials, even when the combined materials have comparatively lower performance metrics. When two materials with higher RCP(s) values with large transition temperature spacing are compared to materials with lower RCP(s) values but, closer transition temperature spacing a higher exergetic cooling power and temperature span is found for the latter.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".