Parallel Computation of Wrench Model for Commutated Magnetically Levitated Planar Actuator
Bibliographic record
Abstract
An accurate wrench model is significant for the simulation, manufacture, and control of the commutated magnetically levitated planar actuator (CMLPA). With plenty of coils and permanent magnets employed in the coil set and magnet array of CMLPA, the computational burden of the corresponding wrench model can be substantial. This paper proposes an accurate, universal, and robust parallel massive-thread wrench model (PMWM) for the CMLPA. In PMWM, the magnetic node, Gaussian quadrature, and coordinate transformation are employed to express the interaction between magnet array and coil set. All of these calculation modules are implemented on the graphics processing unit in a massively parallel framework by CUDA. In order to highlight the performance of this PMWM, the wrench model of three different CMLPAs is computed. The computation accuracy and efficiency of proposed PMWM are compared with the finite-element method software Ansys Maxwell and a boundary element method software package named Radia, respectively. The same wrench model is also implemented on a multicore CPU through OpenMP and the comparative results are presented to show significant acceleration of the proposed massive-thread model.
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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 | 0.001 |
| 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".