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Record W2923298445 · doi:10.36884/jafm.12.si.29918

Impact of the Self-Adaptive Valve Behavior on an Array of Microfluidic Cells under Unsteady and Non-Uniform Heat Load Distributions

2019· article· en· W2923298445 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMicrochannelMaterials scienceMicrofluidicsMechanicsPower (physics)Work (physics)Jet (fluid)Working fluidThermodynamicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Previous studies have demonstrated that the performance of a cooling scheme based on a matrix of microfluidic cells with self-adaptive valves under unsteady and non-uniform heat load scenarios improves in terms of pumping power and temperature uniformity, compared to the ones from conventional microchannels and hybrid jet impingement/microchannel cooling devices. The behavior of the thermally dependent self-adaptive valves varies as a function of some design parameters. In this work, the impact of the valve's characteristic curve on the cooling device is assessed to establish the basic rules for the valve design. The performance of a 33 microfluidic cell array is numerically studied under an unsteady and non-uniform heat load scenario. The results show that the valves which open at the most elevated temperature (control temperature of 90C) reduce by 15.5% the pumping power with respect to the valves opening at 60C, while improving by 25.0% the temperature uniformity and reducing both the overcooling and the fatigue.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.467

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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