MétaCan
Menu
Back to cohort
Record W2249292870 · doi:10.4271/2005-01-2031

Parametric Analysis of Non-Adiabatic Transcritical Flow in Capillary Tubes for a New Refrigeration Cycle

2005· article· en· W2249292870 on OpenAlexaff
Y. Chen, Jun Gu

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsCarleton University
Fundersnot available
KeywordsRefrigerationAdiabatic processCapillary actionMechanicsTranscritical cycleParametric statisticsFlow (mathematics)ThermodynamicsMaterials scienceEnvironmental scienceNuclear engineeringPhysicsGas compressorRefrigerantMathematicsEngineering

Abstract

fetched live from OpenAlex

This paper presents a non-adiabatic homogeneous model of carbon dioxide flow in a novel integrated accumulator-expander-heat exchanger, which integrates the functions of refrigerant storage, expansion and heat transfer, and can lower weight and cost of the system. The model is based on the fundamental conservation equations of mass, momentum and energy. These equations are solved simultaneously through iterations. The in-tube flow can be divided into a single-phase region and a two-phase region. The choking situation at the capillary outlet is evaluated by local sonic velocity judgment. Relationships between cooling pressure, evaporating temperature, capillary size, and other parameters are presented and analyzed in detail. It can be seen that the heat transfer changes with different kinds of capillary tubes under different conditions. The present model can be used for both system design and performance evaluation. It is also very helpful in understanding the transcritical flow behaviour inside capillary tubes.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 designSimulation or modeling
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueSAE technical papers on CD-ROM/SAE technical paper seriesSame topicHeat transfer and supercritical fluidsFrench-language works237,207