MétaCan
Menu
Back to cohort
Record W1892204666

Experimental Study of Temperature Distribution of Two-Stage CascadeRefrigeration System Using R508B as Working Fluid at Low Stage

2005· article· en· W1892204666 on OpenAlexaboutno aff
Yeonggeun Kim, Lubi Rahadiyan, Hyomin Jeong, Hanshik Chung

Bibliographic record

Venue대한기계학회 춘추학술대회 · 2005
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantBoiling pointThermodynamicsBoilingVolume (thermodynamics)Gas compressorStage (stratigraphy)CascadeVapor-compression refrigerationMaterials scienceChemistryChromatographyPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper presents the experimental study on temperature distribution of two-stage cascade vapor-compression system for achieving super-low temperature. Two-stage cascade vapour compression cycle is a method to achieve super low temperature under -80℃. This system requires two different types of refrigerant at higher and lower stages. However, by the Montreal protocol, several refrigerants for the low-stage system as well as high stages have been banned due to its threat to ozone layer. Alternatives retrofit refrigerant for replacing CFC and HCFC lies from HFC to natural refrigerant. In this study, azeotropic zero-ODP refrigerant, R508B blend of two HFC refrigerant R23/R116 (46.0/54.0) was utilized in the low stage cycle, this refrigerant has advantages based on normal boiling point compare to its compositing element. On this experiment, cooling chamber was filled with ethyl alcohol 0, 10, 20, and 25 liter respectively as refrigerant load. By changing the volume of cooling chamber fluid, temperature distribution of the system was observed. More ethyl alcohol mass in the cooling chamber, longer steady state condition achieved. It is also found that the change of ethyl alcohol in the cooling chamber gives little effect on the final temperature distribution.

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 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 score0.103
Threshold uncertainty score0.830

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 head0.017
GPT teacher head0.263
Teacher spread0.246 · 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.

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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

Same venue대한기계학회 춘추학술대회Same topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207