Design and Optimization of Soft Magnetic Composite Machines With Finite Element Methods
Bibliographic record
Abstract
In this paper, a synthesis of design methodologies for electromagnetic devices made with soft magnetic composite (SMC) material is presented. To realize efficient and low-cost SMC devices, it is necessary to benefit from interesting isotropic SMC material properties such as 3-D flux circulation path. In this case, the best approach is to carry out a topological structure research and perform a global optimization using physical models. However, the iterative optimization process can be computationally intensive and some compromises should be found between accuracy and computation time. One way is to use coarse models with important simplifying hypotheses but the optimal solution is often not valid. In this case, one can perform a limited number of finite element (FE) simulations to compute some correction coefficients and improve optimization convergence to a valid optimal solution. Another way is to perform the device optimization by using directly FE models with low mesh density and several simplifications of the device geometry. Such design approaches are illustrated by three concrete realizations with SMC material.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".