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Variable Pumping Control for Low Power Microfluidic Chip Cooling

2018· preprint· en· W2907761539 on OpenAlexaff
Sabrina da Luz, G. Kattinger, Gerard Laguna, Hassan Azarkish, Montse Vilarrubí, Louis-Michel Collin, Luc G. Fréchette, Jérôme Barrau, S. Billat

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMicrochannelMicroelectronicsComputer coolingPressure dropChipMicrofabricationMaterials scienceMicrofluidicsTemperature controlThermal resistanceVolumetric flow rateMechanical engineeringOptoelectronicsElectrical engineeringNanotechnologyHeat transferMechanicsEngineeringThermal management of electronic devices and systemsFabrication

Abstract

fetched live from OpenAlex

As microelectronic modules increase in power density, liquid cooling has become increasingly required to maintain acceptable chip temperatures. Liquid cooling in microchannels offers low thermal resistance and can be integrated into modules given their small size and implementation by microfabrication methods [1]. Microchannel cooling is therefore particularly well suited for embedded or portable applications. Their performance and compact form factor come, however, to the cost of increased pressure drop compared to larger scale cold plates. Since energy is limited in portable applications, power consumption by the cooling system (product of pressure drop and flow rate) should be minimized. This can be done by reducing the pressure drop with optimal design [2] or by distributing the flow in parallel microchannels or cell arrays [3], [4]. This work focuses on reducing the flow rate by adapting the pumping conditions to only provide the minimal flow rate required to maintain the maximum chip temperature.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.952
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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 designSimulation or modeling
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

Citations4
Published2018
Admission routes1
Has abstractyes

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