Low Computational Cost Thermal Modelling of High-Frequency Power Transformers using an Admittance Matrix Apporach
Bibliographic record
Abstract
Thermal modelling of magnetic components in high-frequency power electronic systems is not trivial. This can be attributed to the complex non-uniform losses, heterogenous construction of magnetic components, and the temperature dependence of electrical and magnetic properties. Accurate thermal modelling of such magnetic components relies on the use of bi-directionally coupled electromagnetic-thermal numerical analysis. Although such bi-directionally coupled numerical models provide accurate results, the computational cost of such models can be restrictive. Hence, there is a need for low computational cost thermal models of magnetic components. In this paper, we develop a low computational cost thermal model of a power transformer using the admittance matrix approach. First, a bi-directionally coupled multiphysics model of a power transformer is developed and validated using experimental test results. Using the numerical model, low-cost thermal models are evaluated for surface heat transfer coefficients varying between 1 to <tex>$\mathbf{200}\ \boldsymbol{W}/\boldsymbol{m}^{\mathbf{2}}\cdot \boldsymbol{K}$</tex>, covering the typical thermal operating range of magnetic components housed in conventional power electronic systems. The final low-cost thermal model's surface temperature and heat flux predictions were within <tex>$\pm \mathbf{8}.\mathbf{2}\%$</tex> of the numerical results, while the junction temperature error was <tex>$\pm \mathbf{6}.\mathbf{18}\ \%$</tex>. The simplified thermal model developed shows close to Boundary Condition Independence (BCI) behaviour and is a low computational cost alternative to the numerical model.
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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) | 0.002 | 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".