MétaCan
Menu
Back to cohort
Record W150758552

Centrifugal Compressor Performance Deviations with Various Refrigerants, Impeller Sizes and Shaft Speeds

2014· article· en· W150758552 on OpenAlexaboutno aff
Yuanjie Wu, Chris Thilges

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2014
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsRefrigerantGas compressorCentrifugal compressorImpellerEngineeringFlow (mathematics)Mechanical engineeringMarine engineeringMechanics
DOInot available

Abstract

fetched live from OpenAlex

Under the pressure of the Montreal Protocol originally written in 1987, the current popularly used HCFC and HFC refrigerants for comfortable air conditioning, such as low pressure refrigerant R123, will have to be phased out in the near future. This forces companies in centrifugal chiller industry to redesign and retest new centrifugal compressors which can accommodate alternative refrigerants. Keeping the present flow path designs is an economic design process for the alternative refrigerants, and is well adapted by companies in this industry. In addition extensively employ the flow similitude theory is also an effective way to minimize the cost of lab tests. However employing similitude in compressible flow does not forecast the content in which compressors meet the uncontrollable situations such as choke and surge. Conclusions can only be drawn from experimental data [Dixon, Ferguson]. This paper will present the theory and experimental verification for a low cost method to predict the compressor performance under an alternative refrigerant based on existing compressor maps. A few factors that affect compressor performance maps, but whose effects cannot be predicted by similitude theory are also analyzed by reviewing large amount of tested data.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.003
GPT teacher head0.139
Teacher spread0.136 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePurdue e-Pubs (Purdue University System)Same topicRefrigeration and Air Conditioning TechnologiesFrench-language works237,207