A liquid-filled buoyancy-driven convective micromachined accelerometer
Bibliographic record
Abstract
A novel class of accelerometer, based on the buoyancy of a heated fluid within a micromachined cavity, has previously been developed and reported. Based on dimensional analysis and computational modeling, it is predicted that the sensitivity of the accelerometer can be increased by several orders of magnitude over previously reported results by choosing a suitable liquid as the working fluid, though this increased sensitivity comes at the cost of an increased response time. A liquid-filled accelerometer is constructed; its sensitivity and response time are measured, and shown to be consistent with theoretical predictions and with the results of finite-element analysis. It is noted that the existing literature provides no basis for predicting the effect of Prandtl number on the sensitivity and response time of the accelerometer. The prediction of response time requires analysis of the transient response of the heated fluid to a sudden acceleration. This is a novel problem: previous studies of transient convection have focused on the effects of a newly imposed temperature differential in an existing gravity field, rather than a newly imposed acceleration on an existing thermal field. An approximate expression for response time as a function of radius ratio and Prandtl number is developed by curve-fitting to the results of FLOTRAN simulation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".