Digital twin for magnetic levitation systems: General architecture design and uncertainty analysis
Bibliographic record
Abstract
Digital twins (DTs) are widely used for actuator design, virtual prototyping, simulations, and analysis of model-based system engineering. DT technology is promising for magnetic levitation (maglev) systems, as illustrated by the mover design with high-strength neodymium magnets, the Lorentz force and torque (wrench) model comparison, and motion control verification. Digitalized maglev planar actuators (MLPAs) are time-, material-, labor-, and cost-efficient to develop, and the proposed DT is constructed using an open-source PyBullet module and assisted with a parallel-operated graphic user interface (GUI) using the PyQt5 module. Data transfer between physical systems and DTs is available using socket connections. After comparing the physical and virtual experimental results, the complete DT is verified using a 2-dimensional (2-D) Halbach array and single-disc magnet movers. The uncertainties of the MLPAs are implemented using white noise and system delay models. The ignored uncertainty features are introduced and analyzed for experimental deviations. The proposed DT provides a virtual safeguard environment for the next stage of machine learning research and multiple magnet-mover motion control studies. The first MLPA DT is established with a real-time wrench physics engine, which enables research opportunities for multi-mover motion, robotic collaboration, and artificial intelligence applications. This study is also beneficial for the design and analysis research of MLPAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".