Prototyping of Nonlinear Time-Stepped Finite Element Simulation for Linear Induction Machines on Parallel Reconfigurable Hardware
Bibliographic record
Abstract
The finite element method (FEM) is widely used for accurate design and analysis of electric machines; however, it suffers from long execution time. In this paper, for the first time hardware acceleration of two-dimensional FEM for a single-sided linear induction motor on the field programmable gate array (FPGA) is proposed. The nonlinearity of the iron core as well as the movement are taken into consideration. A new sparse solver is proposed based on left-looking Gilbert-Peierls algorithm for the system of linear equations of FEM that need to be solved in different iterations and time steps. Implementation of the model is performed in a massively paralleled and deeply pipelined hardware architecture using VHDL coding with single precision floating-point number representation. The proposed emulation was performed at various time steps resulting in significant average speedup of 9.73 times in comparison with JMAG-Designer as a commercial finite element software, and the overall hardware latency of each time step for the emulation was 49.2 ms in average with minimum achievable FPGA clock of 5.59 ns.
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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 | 0.000 |
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 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".