High Performance MEMS Thermal Gyroscope
Bibliographic record
Abstract
This paper reports modeling a new design of Thermal MEMS gyroscope through the use of the Comsol Multiphysics software package. Being very small and having no movable parts have made thermal MEMS gyroscope very practical. Previously designed Thermal MEMS gyroscope shows some limitation such as being vulnerable to gravity force. Finding a technique to increase the range of thermal MEMS gyroscope reliability motivated us to come up with a new design that we will refer to as the ‘Forced Convection MEMS gyroscope’. A two-dimensional finite-element model of the device has been developed to investigate its performance. An external force has been introduced to the system to create a higher-velocity hot gas stream that will be deviated more in response to rotation. The external force should be great enough that convection currents resulting from gravity or acceleration will have minimal impact on the gyroscope sensitivity. A heating element can still be used, but its primary purpose is now to warm the flowing gas so that it can be detected by the sensors. In this paper we will also show that, in order to completely eliminate the impact of gravity and increase the sensitivity of the gyroscope, it is possible to eliminate the heaters entirely and instead use heated sensors to detect gas currents. In other words, the sensors are working as hot-wire anemometers. Our simulations suggest that this design variant results in higher sensitivity. We have also carried out optimization studies to identify the best location for the heaters and sensors. A prototype of this device has been fabricated based on MEMS techniques, and an external pump is used to produce an oscillating gas flow within the device.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".