A Fast Time-Step Selection Method for Explicit Solver-Based Simulation of High Frequency Low Loss Circuit and Its Application on EMI Filter
Bibliographic record
Abstract
Electromagnetic interference (EMI) modeling and prediction are essential for the design of most power electronics apparatuses. This article aims at finding a fast method to select time step for explicit solver-based simulation of high frequency low loss (HFLL) circuits like EMI filter. The state-space model of HFLL circuit is constructed and its eigenvalues are proved to be very close to the imaginary axis. Both the nondegenerate and degenerate circuit cases are discussed. During the analysis, a circuit lemma is summarized on how to transform degenerate circuit into nondegenerate circuit and the corresponding inversion of its coefficient matrix is derived based on Sherman-Morrison's formula. Then the Laguerre-Samuelson's inequality is employed to find the upper bound of HFLL circuit's eigenvalues. This process only requires two matrix multiplications and traces of the matrix operation results, thus keeping the computational complexity retaining in O(N2). A typical EMI filter is constructed and its equivalent circuit including the parasitic effects is extracted from ANSYS. This filter is simulated in application between a dc/ac converter and the grid using the fourth-order Runge-Kutta (RK4) solver with a time step selected by the proposed method. Numerical test shows that the spectrum results are very close to those obtained by experiment while being much more efficient than traditional methods, which demonstrates that this time-step selection method could benefit the analysis and time-domain simulation of HFLL circuits.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".