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Record W3118735542 · doi:10.31438/trf.hh2006.108

TEMPERATURE REGULATED NONLINEAR MICROVALVES FOR SELF-ADAPTIVE MEMS COOLING

2006· article· en· W3118735542 on OpenAlexaff
Matthew McCarthy, Nicholas Tiliakos, Vijay Modi, Luc G. Fréchette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicroelectromechanical systemsMaterials scienceMicrofabricationNonlinear systemThermalMechanical engineeringHeat exchangerMicrofluidicsVolumetric flow rateMechanicsFabricationOptoelectronicsNanotechnologyEngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

In this paper, thermal buckling of doubly clamped microfabricated nickel beams is implemented as a passive actuation mechanism to drive temperature-regulated nonlinear micro-valves for adaptive microcooling applications. The nonlinear buckling phenomenon is combined with the nonlinear change in flow rate through parallel plates with a variable spacing. The thermal buckling mechanism and parallel plate flow are modeled analytically, and nondimensional characteristic design curves have been generated. Passive flow-control microvalves were fabricated using deep reactive ion etching and a through-mold nickel electroplating process over a thin sacrificial layer. The model is validated with experimental results from the microfabricated temperature-regulated microvalves. Experimental characterization using an integrated micromachined heat exchanger with air as the working fluid shows the desired nonlinear valving behavior with mass flow rates of up to 5 mg/s for a temperature increase of 50degC, corresponding to 0.25 W of heat removal. It is shown that temperature-induced elastic instabilities in microfabricated structures can be modeled and manipulated to create a nonlinear adaptive valving mechanism. The modeling approach, microfabrication process, and full characterization of the microvalves are presented.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.528
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

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 science0.0000.000
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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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