Experimental characterization of the constitutive materials of MgB<sub>2</sub>multi-filamentary wires for the development of 3D numerical models
Bibliographic record
Abstract
Abstract Today MgB 2 superconducting wires can be manufactured in long lengths at low cost, which makes this material a good candidate for large scale applications. However, because of its relatively low critical temperature (less than 40 K), it is necessary to operate MgB 2 devices in a liquid or gaseous helium environment. In this context, losses in the cryogenic environment must be rigorously minimized, otherwise the use of a superconductor is not worthy. An accurate estimation of the losses at the design stage is therefore mandatory in order to allow determining the device architecture that minimizes the losses. In this paper, we present a complete a 3D finite element model of a 36-filament MgB 2 wire based on the architecture of the Italian manufacturer Colombus. In order for the model to be as accurate as possible, we made a substantial effort to characterize all constitutive materials of the wire, namely the E – J characteristics of the MgB 2 filaments and the electric and magnetic properties ( B − H curves) of nickel and monel, which are the two major non-superconducting components of the wire. All properties were characterized as a function of temperature and magnetic field. Limitations of the characterization and of the model are discussed, in particular the difficulty to extract the maximum relative permeability of nickel and monel from the experimental data, as well as the lack of a thin conductive layer model in the 3D finite element method, which prevents us from taking into account the resistive barriers around the MgB 2 filaments in the matrix. Two examples of numerical simulations are provided to illustrate the capabilities of the model in its current state.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".