Design and Validation of a Variable Reluctance Differential Solenoid Transducer With an Ironless Stator
Bibliographic record
Abstract
This article presents the design methodology, simulation results, and experimental results of a novel variable reluctance differential solenoid transducer (VRDST). The new ironless-stator VRDST (ISVRDST) is designed to mitigate the practical challenges inherent in the VRDST while maintaining its beneficial features compared to other linear inductive differential position sensors. The ISVRDST uses an air stator to reduce cost and increase compactness. FEA and Simulink simulations are used to predict the performance of the ISVRDST and a proof-of-concept prototype is designed to experimentally validate the simulation results. Like the VRDST, the ISVRDST is designed to utilize a complementary filter to fuse a low-speed position measurement with a high-speed velocity measurement, resulting in a single wide-bandwidth position measurement. The experimental results of the prototype ISVRDST prove that it is capable of robustly performing and fusing the two measurements utilized by the VRDST. In addition, the analytically predicted error of the fused measurement is found to be less than 1% different than the actual error relative to a high-precision laser vibrometer. When compared to the VRDST, the ISVRDST prototype is found to have a stroke-to-length ratio that is nearly four times greater and a cost that is over 100 times lower.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".