Finite Elements based optimal design approach for high voltage pulse transformers
Bibliographic record
Abstract
Summary form only given. High voltage pulse transformers are widely used in klystron modulators. For these topologies, the quality of the modulator output voltage is strongly dependent on the pulse transformer performance. An optimal design process of such transformers is a necessity, particularly when the specifications of the output voltage are tight such as in the case of the CLIC klystron modulator under study at CERN [1]. Using a sufficiently accurate analytical model to optimize a high voltage transformer design will yield the most efficient design process. The accuracy of the design can then be verified with a Finite Element Analysis as a final check. However in the case of high voltage transformers, it can be difficult to obtain an analytical model that is sufficiently accurate particularly due to effects of parasitic capacitance. As such, it would be desirable to find an efficient method to use only Finite Element Analysis to find an optimal design. This paper presents a new design approach for high voltage pulse transformers, based on a FEA dimensioning model only. For each iteration of the non-linear optimization process, the transformer parameters used to compute the objective and constraints functions are directly derived from the 2D FEA dimensioning model. Programming techniques to speed-up the FEA model evaluation for each iteration of the optimization procedure are presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.009 | 0.002 |
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".