3-D Numerical Modeling of AC Losses in Multifilamentary MgB<sub>2</sub>Wires
Bibliographic record
Abstract
Due to their high-current-carrying capacity, round geometry, and low cost, MgB2wires are promising candidates for realizing high-power cables. However, their operating temperature between 4.2 K and 25 K makes ac losses a critical issue for those cables. To optimize the cable architecture for minimizing ac losses, one must be able to predict them quite accurately. As a first step in this direction, we addressed the numerical computation of a single multifilamentary MgB2wire that forms the basic element of a high-current cable. The wire under consideration has 36 twisted MgB2filaments disposed on three concentric layers and embedded in a pure-nickel matrix. An initial comparison between 2-D and 3-D finite elements was performed to justify the need for a full 3-D model, without which coupling losses in the matrix cannot be modeled properly. This is of prime importance since coupling loss is the dominant loss mechanism at high applied fields. Then, simulations of simpler geometries (6- and 18-filament wires) submitted to various transport currents and/or applied fields were performed to identify trends in ac losses and find the best numerical tools for scaling up simulations to the full 36-filament case. The complexity of the model was progressively increased, starting with MgB2filaments in the air matrix and then adding electrical conductivity and magnetic properties in the nickel matrix.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".