Hybrid analytical model of switched reluctance machine for real‐time hardware‐in‐the‐loop simulation
Bibliographic record
Abstract
Applications of switched reluctance machine (SRM) are increasing in the industry due to their many desirable features. This study proposes a hybrid analytical model (HAM) of the SRM for the hardware‐in‐the‐loop (HIL) simulation. To obtain satisfactory accuracy, the phase flux linkage is solved by the magnetic equivalent circuit (MEC) method when the stator and rotor poles overlap, and by the space harmonic method (SHM) when the poles do not overlap. The backward Euler and Newton–Raphson methods are used to calculate the exciting current, while the Gaussian quadrature is used to compute the electromagnetic torque in the HIL simulation. The digital hardware implementation of computation components are developed on the field‐programmable gate array by exploiting the parallel hardware architecture and fully pipelined arithmetic processing. To highlight the performance of the HAM, the captured real‐time results are compared with the off‐line transient solution obtained through the co‐simulation of Ansys Maxwell ® , Ansys Simplorer ® , and Simulink ® , which model the SRM, drive circuit, and control system, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".