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Record W2378656489

Influence of Subcooling on Nozzle Efficiency

2009· article· en· W2378656489 on OpenAlexaboutno aff
Pengcheng Shu

Bibliographic record

VenueXi'an Jiaotong Daxue xuebao · 2009
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Boiling Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSubcoolingNozzleRefrigerantMixing (physics)Materials scienceMechanicsDischarge coefficientMass flow rateInletDegree (music)ThermodynamicsMechanical engineeringPhysicsHeat transferEngineeringHeat exchanger
DOInot available

Abstract

fetched live from OpenAlex

A velocity-measurement equipment was developed to study the influence of fluid subcooling degree on the nozzle operation performance. The mass flow rate and the impact force were measured and the average mixing velocity of the two-phase fluid was obtained based on the momentum theorem. The influence of fluid subcooling degree on the average mixing velocity of the two-phase fluid at the outlet of LAVAL nozzles and the efficiencies of the nozzles was investigated on a refrigeration system with R410A. The experimental results show that the mass flow rate increased with the increase of the refrigerant subcooling degree at the inlet of the nozzles. However,the increase of upstream subcooling degree resulted in the significant decrease of the refrigerant mixing velocity and the nozzle efficiency. Under test conditions,the refrigerant mixing velocity at the outlet of the nozzle and the nozzle efficiency decreased by 35.7% and 33.9%,respectively,when the upstream subcooling degree increased from 0 ℃ to 11 ℃ for No.2 nozzle.

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)0.003
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.010
GPT teacher head0.235
Teacher spread0.225 · 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
Published2009
Admission routes1
Has abstractyes

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