Design and Validation of a Variable Reluctance Differential Solenoid Transducer
Bibliographic record
Abstract
This paper presents a novel variable reluctance differential solenoid transducer (VRDST) that offers improved high-speed sensing performance over linear voltage differential transformers (LVDTs) and differential variable reluctance transformers (DVRTs). The VRDST has the unique ability to measure both position and velocity simultaneously using two independent measurements. This paper investigates a basic geometry for a VRDST. The position and velocity measurement methods are derived and implemented in a Simulink simulation. The simulated VRDST model is augmented with an FEA simulation to predict the magnetic characteristics of the investigated design. The results and predictions established by the simulation and analytical models are validated experimentally with a physical prototype. The similarity between the experimental and simulated results suggest the proposed FEA and Simulink simulations can be used to accurately predict the performance of a physical VRDST. The findings from the analytical modeling, simulation study and experimental validation all unanimously prove the position measurement performs well when measuring low-speed displacements, while the integrated-velocity measurement is useful for measuring high-speed displacements. The differing frequency ranges of these two independent measurements are found to complement each other and suggest the VRDST is superior compared to DVRTs or LVDTs for applications requiring wide bandwidth position measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".