Real-Time FPGA-Based Analytical Space Harmonic Model of Permanent Magnet Machines for Hardware-in-the-Loop Simulation
Bibliographic record
Abstract
This paper presents a real-time analytical space harmonic model of permanent magnet synchronous machines with shaped poles. The goal of this configuration is to produce an air-gap flux density distribution as close to sinusoidal as possible to enhance the machine performance. The analytical model is derived to predict the magnetic fields and the machine behavior by solving Maxwell's equations and applying the superposition theorem. The digital hardware realization of the model is developed on the field-programmable gate arrays (FPGAs) in a paralleled and pipelined paradigm for an efficient hardware design. Such real-time emulation on FPGA can be used in the design procedure and the assessment of newly prototyped controllers and drive systems in the hardware-in-the-loop platform. In this paper, the analytical model of the machine is simulated in real time on 7.5 ns input FPGA clock cycles, and the achieved executive time is 1.8 ${\mu }\text{s}$ . The captured real-time analytical results demonstrate the good accuracy of the emulator in comparison with the offline 2-D time-stepping transient finite-element solution obtained by JMAG software.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".