MÉTHODES NUMÉRIQUES ET OUTILS LOGICIELS POUR LA PRISE EN COMPTE DES EFFETS CAPACITIFS DANS LA MODÉLISATION CEM DE DISPOSITIFS D'ÉLECTRONIQUE DE PUISSANCE
Bibliographic record
Abstract
Given the increasing complexity of static converters present in any electrical system, design engineers need more and more powerful tools for electromagnetic modeling, especially concerning ElectroMagnetic Compatibility (EMC). The objective of this work is to take into account in the form of parasitic capacitances, the electrical coupling in high frequency in the EMC modeling of power electronics devices. Several integral formulations based on the Moment Method, and the Adaptive Multi-Level Fast Multipole Method have been developed and validated for the extraction of equivalent capacitances. This last method, which speeds up the computation times while limiting the memory space required (no full matrix storage), has been adapted to the problem to ensure more accurate results linked to the mesh. A prototype of this algorithm has been integrated into the software InCa3D, based on the PEEC method, allowing to build an equivalent circuit composed of lumped elements in which capacitive effects are coupled to the resistive and inductive model of the structure. Several test cases, from the literature or industrial applications, have been simulated by using these equivalent circuits, either in a circuit solver or in InCa3D in order to evaluate their conducted and radiated EMC performances. Finally, comparisons made with measurements gave good results and thus validate the proposed approach. Such a strategy can easily be part of any system-type modeling because it allows the treatment of complex industrial devices over a wide frequency band with a lightweight model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".