Application of adaptive multilevel substructuring technique to model CMOS micromachined thermistor gas sensor, part (I): A feasibility study
Bibliographic record
Abstract
A study has been conducted to investigate the feasibility of using the multilevel substructuring method to perform parametric analysis for MEMS devices. The feasibility study was conducted on a CMOS micromachined thermistor gas sensor. Two multilevel substructuring methods were used, mainly the cumulative and nested superelements methods. The interface problem was found to increase rapidly with the increase in the number of superelements for the cumulative technique. On the other hand, the nested superelements technique was found to provide an almost fixed and much more reduced interface problem. The results show that, for the same number of reduced elements, the nested superelements method provides a better speedup factor (2.36-4.61) compared to the cumulative method. In the current study, two strategies were used to deal with the nonlinear thermal analysis of the microheater. In the first strategy the substructuring was only limited to the linear portions of the model. In the second strategy the substructuring was extended to include portions of the model with a reduced nonlinearity. The second strategy increased the computational savings by a percentage of 20%, compared to the first strategy with a reasonable loss of accuracy of only about 3%.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".