Active magnetic regenerator performance enhancement using passive magnetic materials.
Bibliographic record
Abstract
Abstract Magnetic refrigeration devices using permanent magnets are currently limited to useful field strengths of less than 2 T, and more practically less than 1.5 T. In this range, the useful magnetocaloric effect is less than 6 K and limits the cooling power of active magnetic regenerator (AMR) devices. Maximizing the useful magnetocaloric effect is critical in enabling commercially viable permanent magnet devices, and methods of increasing the net change in magnetic field would be beneficial. It has been shown [O. Peksoy, A. Rowe, J. Magn. Magn. Mater. 288 (2005) 424] that the geometry of a regenerator and the magnetic properties of the refrigerant can alter the local magnetic field, H . This is called demagnetization . A numerical model is used to study demagnetizing effects in a single-material AMR. The use of additional passive magnetic material to reduce demagnetization is examined and suggests that augmenting the effective field, H , is possible. Numerical results are validated with experiments in near room temperature using AMRs consisting of Gd and Gd 0.74 Tb 0.26 adjacent to layers composed of 1010 carbon steel. Experimental data show an increase in the no-load temperature span for certain operating conditions and confirm the beneficial impact of using passive magnetic material to reduce demagnetization effects at low fields.
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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 | 0.000 | 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".