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Record W2319980751 · doi:10.2514/6.2011-3487

Testing Two High Heat Flux Liquid Nitrogen Jet Impingement Evaporative Coolers

2011· article· en· W2319980751 on OpenAlexaff
E. E. Takara, I. HSU, Bozhidar Evtimov

Bibliographic record

Venue42nd AIAA Thermophysics Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsEvaporative coolerLiquid nitrogenJet (fluid)Heat fluxFlux (metallurgy)MechanicsMaterials scienceNitrogenThermodynamicsEnvironmental scienceNuclear engineeringHeat transferPhysicsEngineering

Abstract

fetched live from OpenAlex

Since the efficiency of high power components, such as doped Yttrium Aluminum Garnet (YAG) laser amplifiers, improves dramatically at cryogenic temperatures, a cooling system capable of high performance at cryogenic temperatures is desirable. Because of its many favorable qualities, pool boiling liquid nitrogen (LN2) is the cryogenic cooling system of choice. However, as the power density of systems increases and laser systems migrate to agile hosts; the limitations of pool boiling become more restrictive. To expand beyond the flux and orientation limits of LN2 pool boiling, two systems based on jet impingement were developed and tested. The first cooled fluxes up to 70 W/cm 2 at a heat load of 1500 W. It demonstrated temperature stability, variations less than 1.5 K at 60 W/cm 2 over 20 minutes. The second system provided 50 W/cm 2 cooling (500 W total) while inverted, operating against gravity.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.056
GPT teacher head0.226
Teacher spread0.170 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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