Development of advanced material models for the simulation of low frequency electromagnetic devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The increase in the efficiency of electrical machines is dependent on the better use of magnetic materials and a reduction of losses incurred in them.Efficient design requires the use of computer simulation and this in turn depends on the better understanding of underlying physics and material properties.The behavior of these materials is severely affected by the operating conditions inside the machines, such as the excitation waveform shapes, higher induction levels, high frequency excitations, mechanical stresses and the elevated operating temperatures.None of the existing computational models takes all of these effects into account.These issues highlight the weaknesses in the existing material models and create a demand for both a better understanding of what is happening in the materials and, ultimately, better computational models.This thesis seeks to develop a relationship between states of the art hysteresis models i.e. the Jiles-Atherton and Preisach models and eventually provide a generalized, computationally economical and accurate model that can predict iron losses in the ferromagnetic core inside an electrical machine operating under the aforementioned conditions.The results computed using both the iron loss models are compared with the measurements for non-oriented electrical steel.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it