MétaCan
Menu
Back to cohort
Record W2592800975 · doi:10.1038/s41526-017-0014-z

Swing-like pool boiling on nano-textured surfaces for microgravity applications related to cooling of high-power microelectronics

2017· article· en· W2592800975 on OpenAlexaff
Sumit Sinha‐Ray, Wenshuo Zhang, Barak Stoltz, Rakesh P. Sahu, Suman Sinha‐Ray, Alexander L. Yarin

Bibliographic record

Venuenpj Microgravity · 2017
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsMcMaster University
FundersNational Aeronautics and Space Administration
KeywordsBubbleBuoyancyBoilingMicroelectronicsMaterials scienceRecoilMechanicsSuperheatingSwingMicroscale chemistryDragNanotechnologyThermodynamicsPhysicsMechanical engineeringAtomic physicsEngineering

Abstract

fetched live from OpenAlex

Here, we demonstrate that heat removed in pool boiling from a heater mimicking high-power microelectronics could be used to facilitate a swing-like motion of the heater before being finally dissipated. This swing-like motion could be beneficial for shedding a large vapor bubble that encapsulates high-power heaters in microgravity where buoyancy force is unavailable for vapor bubble removal. The swing-like motion is propelled by vapor bubble recoil, the force which exists irrespective of gravity and buoyancy. We also demonstrate that this force could be significantly enhanced by depositing on the heater surface supersonically blown polymer nanofibers with cross-sectional diameters below 100 nm. These nanofibers provide additional nucleation sites, resulting in much more frequent bubble nucleation and departure, and thus a higher overall vapor recoil force propelling the heater motion. Such nanofibers strongly adhere to the heater surface and withstand prolonged harsh pool boiling. The measured velocity of the model swing-like heater in Novec 7300 fluid is about 1 cm/s.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.246
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations30
Published2017
Admission routes1
Has abstractyes

Explore more

Same venuenpj MicrogravitySame topicSpacecraft and Cryogenic TechnologiesFrench-language works237,207