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Experimental Validation of a Smart Microfluidic Cell Cooling Solution

2020· preprint· en· W3085162363 on OpenAlexaff
Gerard Laguna, Manuel Plana, Joan Rosell, Montse Vilarrubí, Amrid Amnache, Étienne Léveillé, Rajesh Pandiyan, Luc G. Fréchette, Jérôme Barrau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMicrofluidicsCoolantMaterials scienceMicroelectronicsHeat fluxFlow (mathematics)Water coolingVolumetric flow rateNanotechnologyMechanical engineeringMechanicsHeat transferEngineering

Abstract

fetched live from OpenAlex

A novel microfluidic cooling solution, based on a microfluidic cell array with self-adaptive valves, for efficient cooling of time-varying and non-uniform heating in microelectronics. The proposed cooling device is formed by an array of microfluidic cells, each one responsible for removing the local heat flux. Coolant flow is fed in parallel to the cells by interdigitated cold and warm flow channels connected to manifolds. Each cell, therefore, has a cold inlet flow, irrespective of its location. Heat is removed by the flow through microchannels enclosed in the cells. Each cell integrates a self-adaptive micro-valve capable of tailoring the local flow rate to the local cooling needs. In this paper, the actuation of self-adaptive valves, placed in each cell of an array of 6 by 10 microfluidic cells, is experimentally demonstrated. Also, the performance of the microfluidic cell cooling system is assessed and compared with regular microchannels, under a non-uniform and time dependent heat load, in order to identify the benefits of this novel microfluidic cooling solution.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.722

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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 designBench or experimental
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

Citations4
Published2020
Admission routes1
Has abstractyes

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