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Record W2521976965 · doi:10.1115/1.4034756

Performance Definitions for Three-Fluid Heat and Moisture Exchangers

2016· article· en· W2521976965 on OpenAlexafffund
Mohamed R.H. Abdel-Salam, Robert W. Besant, Carey J. Simonson

Bibliographic record

VenueJournal of Heat Transfer · 2016
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeat exchangerPlate fin heat exchangerNTU methodRefrigerantThermodynamicsPlate heat exchangerMoistureProcess engineeringMoving bed heat exchangerPetroleum engineeringMechanical engineeringEnvironmental scienceMaterials scienceEngineeringPhysicsComposite material

Abstract

fetched live from OpenAlex

This paper presents performance definitions for calculating the overall effectiveness of three-fluid heat and moisture exchangers. The three-fluid heat and moisture exchanger considered in this paper is a combination of a liquid-to-liquid heat exchanger for heat transfer between a desiccant solution and a refrigerant and an energy exchanger for heat and moisture transfer between desiccant solution and air streams. The performance definitions presented in this paper are used to calculate the overall sensible and latent effectivenesses of a three-fluid heat and moisture exchanger, which has been tested under air cooling and dehumidifying operating conditions in a previous work (Abdel-Salam et al., 2016, “Design and Testing of a Novel 3-Fluid Liquid-to-Air Membrane Energy Exchanger (3-Fluid LAMEE),” Int. J. Heat Mass Transfer, 92, pp. 312–329). The effectiveness of this three-fluid heat and moisture exchanger is compared when calculated using the traditional energy exchanger effectiveness equations and the overall performance definitions. Results show that the overall performance definitions provide effectiveness values that are less sensitive to changes in the inlet refrigerant temperature and therefore are more generally applicable for energy exchanger design than the traditional effectiveness equations used in the literature.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.218

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 head0.026
GPT teacher head0.211
Teacher spread0.185 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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