H-formulation for simulating levitation forces acting on HTS bulks and stacks of 2G coated conductors
Bibliographic record
Abstract
Several techniques to model high temperature superconductors (HTSs) are used throughout the world. At the same time, the use of superconductors in transportation and magnetic bearings promises an increase in energy efficiency. However, the most widespread simulation technique in the literature, the H-formulation, has not yet been used to simulate superconducting levitation. The goal of this work is to present solutions for the challenges concerning the use of the H-formulation to predict the behavior of superconducting levitators built either with YBCO bulks or stacks of 2G wires. It is worth mentioning the originality of replacing bulks with HTS stacks in this application. In our simulation methodology, the movement between the HTS and the permanent magnet was avoided by restricting the simulation domain to the HTS itself, which can be done by applying appropriate boundary conditions and analytical expressions for the source field. Commercial finite element software was used for the sake of ease of implementation. Simulation results were compared with experimental data, showing good agreement. We conclude that the H-formulation is suitable for problems involving moving objects and is a good alternative to other approaches for simulating superconducting magnetic bearings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".