Modelling and Design of Advanced High Frequency Transformers
Bibliographic record
Abstract
Carbon dioxide emission reduction has been a popular topic in recent years because it alleviates the current global warming crisis. Hence, there is an urgent need to adapt current technologies to help reduce carbon dioxide emissions before the global warming situation worsens. Amongst carbon dioxide reduction technologies, Electric Vehicle (EV) and renewable energy technologies are most likely to assist in improving this current status of the environmental issue in coming years. Due to increased interest in energy storage systems, such as EV and renewable energy, there is a pending need to improve the existing DC-DC converters used. The DC-DC converter systems used at present are usually bulky, costly and inefficient due to their low operating frequency. Hence, by increasing the operating frequency of the DC-DC converter, the size of the passive elements can be greatly reduced. Among these passive elements, the transformer occupies the most important role indisputably. A High Frequency Coaxial Transformer (HFCT), with a range of 1 kW to 20 kW was designed and introduced in this dissertation. The operating frequency was raised to between 100 kHz and 300 kHz in order to achieve high power densities and high performance efficiency. However, the coupling capacitance accompanied by an increased operating frequency (which couples high frequency noise between the primary and secondary windings) can cause serious common mode problems. Hence, the Faraday shield was placed between the windings of introduced transformers. This reduces the coupling capacitance and consequently the electromagnetic interference. The shielding effect analysis has been conducted to verify the HFCT performance with the insertion of the Faraday shield.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".