Employing air holes in an electrostatic force driven MEMS DC electric field sensor to improve performance
Bibliographic record
Abstract
Manitoba Hydro International and The University of Manitoba have collaborated to develop a high voltage DC electric field sensor for monitoring high voltage power lines and power systems equipment in Manitoba. Different types of electric field sensors have been designed, fabricated and tested by electrical engineering graduate students in the Microsensors Research Lab. This thesis describes modifications made to a previous design, which utilizes electrostatic force to deflect a torsional force-driven oscillating membrane. The sensor utilizes a flexible PCB polyimide as a substrate, with a total device thickness of 34 μm. The intent of this thesis is to investigate the effects of air drag on the rotating membrane. By incorporating air holes in the sensing membrane the effects of air resistance are reduced. The sensor experiences less damping and an increased Q-factor, which results in less energy lost per cycle of oscillation and an increased maximum amplitude. Three sizes of sensors were designed and fabricated, and each size of sensor has three varieties. One variety has no air holes on the membrane, another has some air holes on the membrane, and the third variety has lots of air holes. The best performing sensor is the largest sensor with the most amount of air holes. This 5 mm × 5 mm sensing membrane was subjected to a 163 kV/m static electric field with a membrane AC bias of ±6 V operating at the resonance frequency of 72 Hz, and achieved an output voltage of 7.73 V. It has the best defined peak and lowest half-energy bandwidth of 7.5 Hz corresponding to a Q-factor of 10. The linear spring constant was measured to be 5.9 N/m. It is shown in this thesis through simulation and experiment, that the addition of air holes on an electrostatic force-based torsional PCB-MEMS electric field sensor will reduce air damping, increase the Q-factor, and allow for a larger maximum amplitude of oscillation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".