Accurately Predicting the Performance of Polymer-Based CMUTs by Coupling Finite-Element and Analytical Models
Bibliographic record
Abstract
This paper introduces a hybrid modeling approach to accurately predict the performance of polymer-based Capacitive Micromachined Ultrasonic Transducers (polyCMUTs) by coupling finite element analysis (FEA) with analytical methods. The coupled FEA and analytical (CFA) model integrates characteristics from a single-cell FEA into a multi-cell equivalent circuit. Acoustic cross-coupling between cells is considered using analytical methods, and the acoustic far-field is computed via the Rayleigh integral. We validated the model on rectangular designs with 11x11 cells and varying cell-to-cell pitches. CFA results showed in average less than 7% deviation from full FEA in terms of center frequency, fractional bandwidth, and peak sensitivity, while requiring less than 1% of the computation time. We also observed good agreements with measurements, with a deviation of 17% for the rectangular designs and less than 4% for a larger linear array element (428 cells) we recently produced. This makes the CFA model a powerful tool for fast design exploration and optimization of CMUTs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".