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Record W2118305861 · doi:10.1177/2041297510393801

A conceptual microchannel system design methodology incorporating axiomatic design theory for size-controllable monodispersed microsphere generation by a liquid chopper utilizing a PZT actuator

2011· article· en· W2118305861 on OpenAlexaff
K-Y Song, Madan M. Gupta

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture · 2011
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicrochannelAxiomatic designControllabilityConceptual designActuatorChopperComputer scienceDesign processSystems designProbabilistic designProcess (computing)Control engineeringEngineering design processMechanical engineeringMaterials scienceSystems engineeringEngineeringNanotechnologyProcess engineeringManufacturing engineeringMathematicsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper proposes a design methodology for conceptual design of microchannel systems by applying axiomatic design theory (ADT). The microchannel system concerned, as an example, in this paper is for the purpose of preparing uniform microspheres based on the phase separation principle. There are two general design goals for this system: controllability of the size of microspheres and uniformity or narrow size distribution of microspheres. The conventional microchannel system designs in literature are found to be the so-called ‘coupled design’, which is regarded as a poor design, according to ADT. This paper proposes a ‘decoupled design’ process, which is considered better than a ‘coupled design’ process according to ADT. This paper demonstrates the effectiveness of such a ‘decoupled design’ process with a new microchannel system with an embedded liquid chopper utilizing a PZT actuator by simulation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.736
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.048
GPT teacher head0.228
Teacher spread0.180 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2011
Admission routes1
Has abstractyes

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