An Alternative Method to Accurately Model Magnetic Components Using Ansys HFSS 3D
Bibliographic record
Abstract
The trend of using wide band-gap devices in power electronics has increased the capability of power converters to shrink more in size and weight as they can operate at higher frequency (MHz range). This provides the opportunity to make power modules more compact, low profile, and portable. This huge enhancement in active components pushes the limitations of high power converter to passive components. The passive components behave differently at high frequency due to the variation in permeability of magnetic cores, skin and proximity effect, inter-winding and intra-winding capacitance; especially at MHz range the parasitics might be the dominant factor. To mitigate the challenges of using passive components at high frequency, modeling of the magnetic components considering parasitics should be re-evaluated since the lumped models can not accurately represent the behavior of the inductors or transformers in simulations. Finite element method (FEM) based simulations such as Ansys Maxwell 3D are common tools to simulate magnetic components using quasi-static approximation of Maxwell's equations. In this paper, an accurate modeling method is proposed using Ansys HFSS 3D which is a full-wave solver and using the scattering parameter of the simulated component, a model can be generated to be used in the circuit simulation. It has been elaborately investigated that it can precisely model the magnetic component without any simplifying assumption in the design process. To validate the accuracy of this method, a flat wire inductor is modeled in HFSS 3D and MAXWELL 3D and the results are analyzed in Ansys CIRCUIT and SIMPLORER. The inductor is accurately measured using Agilent E5061B vector network analyzer and it is verified that the simulation results from HFSS can closely represent the behavior of the passive component at high frequency range to be used in time domain circuit simulations.
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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".