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Record W2026727109 · doi:10.1115/imece2010-40704

Simulation and Dynamic Characterization of a 3-Layer Piezo-Actuated Valveless Micropump System

2010· article· en· W2026727109 on OpenAlexaff
Hamid SadAbadi, Arvind Chandrasekaran, Muthukuraman Packirisamy, Rolf Wüthrich

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicropumpActuatorDisplacement (psychology)Natural frequencyFinite element methodDiaphragm (acoustics)MicrofluidicsMechanical engineeringEngineeringControl theory (sociology)Materials scienceControl engineeringComputer scienceAcousticsStructural engineeringElectrical engineeringVibrationPhysics

Abstract

fetched live from OpenAlex

In order to design the valveless micropump with a Piezo actuator, it is essential to understand the dynamic properties of the actuating system. Besides several other considerations in designing of microfluidic systems, the efficiency of valveless micropumps also strongly depends on parameters of the actuation system including the actuation frequency. Cleary, higher displacement of the diaphragm results in higher output flow rate of the pump. Thus, studying the dynamic behavior of the actuation system forms one of the important considerations for the design of micropumps. Three different models of the actuating system for the fabricated micropump system are proposed with different boundary conditions and are simulated by finite element method using ANSYS. Comparison of the experimental results and the simulation results of the natural frequencies of the system shows that the proposed simulation method can also be now used as a tool to optimize the design of the actuation system in terms of natural frequency of the system.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.005
GPT teacher head0.208
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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