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Record W4285550784 · doi:10.1615/ichmt.2021.cht-21.30

TRANSIENT RESPONSE OF DIFFERENT REFRIGERANTS USED IN SINGLE-PASS DUAL CHILLER

2021· article· en· W4285550784 on OpenAlexaff
Sambhaji T. Kadam, Anaya Bara, Ibrahim Hassan, Mohammad Azizur Rahman, Αθανάσιος Ι. Παπαδόπουλος, Panos Seferlis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRefrigeration and Air Conditioning Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsChillerRefrigerantWater chillerCoefficient of performanceVapor-compression refrigerationEnvironmental scienceRefrigerationTransient (computer programming)Chilled waterCooling capacityThermodynamicsWater coolingComputer scienceGas compressor

Abstract

fetched live from OpenAlex

A number of process parameters can affect the performance of the district cooling plant, in which the vapor compression refrigeration (VCR) cycle has been widely employed. Two major process disturbances are: the end-user daily varying cooling demand (temperature of the received chilled water) and the seasonal changes in environmental conditions (cooling water temperature). This paper aims to investigate the effect of the selected refrigerants (R134a, R32, R717, R1234yf, R410a) on the transient response of dual chillers. It considers the cooling water temperatures as the process disturbances and disturbance are introduce in two fashion; namely sudden temperature increase and ramp temperature increase. The dynamic behavior of the process dependent variables, namely, the condensing temperature, evaporating temperature, and the coefficient of Performance (COP) are investigated. It is observed that the transient time that is needed for the given refrigerant to reach the new steady-state follows the following rank of refrigerants, starting with the fastest to the slowest: R410a, R32, R134a, R1234yf, and R717. Further, it is observed that the least reduction in COP upon the disturbance occurs in the chillers that run with R717, while the highest is associated with the one that has R134a.

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 score0.126
Threshold uncertainty score0.331

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 head0.018
GPT teacher head0.218
Teacher spread0.200 · 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
Published2021
Admission routes1
Has abstractyes

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