Dynamic behavior of novel nanocomposite diaphragm in piezoelectrically-actuated micropump
Bibliographic record
Abstract
Abstract Piezoelectric-actuated micropumps have been introduced to generate small-scale flow rates of fluids for intricate applications requiring accurate and controlled flow. The performance of the micropump is basically governed by a piezoelectric actuator, the frequency of the exciting voltage a diaphragm, and the fluid type. In order to improve the micropump performance in terms of flow rate and backpressure, a diaphragm made of passive polymeric membrane reinforced by nanoclay particles which bonded to the piezo-actuator is considered and its viability examined. Both the static and the dynamic performance of the proposed micropump are investigated using a mesh-free method approach. This method is based on moving least squares (MLS) shape functions and first order shear deformation theory. The material properties of the nanoclay-reinforced composite are estimated by a two-step model consisting of an effective particle concept and Halpin–Tsai approach. The effect of the diaphragm thickness and the nanoclay volume fraction as well as the amplitude and the frequency of the exciting voltage are investigated in terms of micropump’s diaphragm deflection, flow rate and pump backpressure. The results of our extensive analysis reveal that the diaphragm thickness plays an important role in the dynamic response of the newly devised micropump. Furthermore, it reveals that increasing the volume fraction of the nanoclay leads to the simultaneous increase in the backpressure and the flow rate of the micropump.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 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".