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Record W2049122879 · doi:10.1115/ht2013-17252

Measurement of Heat Transfer Enhancement and Pressure Drop Across Eddy Promoter Air Screens in a Liquid-to-Air-Membrane Energy Exchanger (LAMEE)

2013· article· en· W2049122879 on OpenAlexafffund
Ashkan Oghabi, Davood Ghadiri Moghaddam, Carey J. Simonson, Robert W. Besant

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPressure dropHeat transfer coefficientHeat transferHeat exchangerMaterials scienceMechanicsConvective heat transferReynolds numberHeat transfer enhancementThermodynamicsTurbulencePhysics

Abstract

fetched live from OpenAlex

In liquid-to-air membrane energy exchangers (LAMEEs), the heat and mass transfer resistances in the air channel are dominant. An eddy promoter air screen can effectively enhance the heat and mass transfers in the air channel. In this study, the heat transfer enhancement and pressure drop across three different eddy promoter air screens in an air channel are experimentally investigated. Eddy promoter air screens are comprised of plastic ribs in the stream-wise direction and aluminum cross-bars normal to the air flow direction. A low speed wind tunnel test facility, which simulates the air channel of a LAMEE is designed to measure the friction factor and enhanced convective heat transfer coefficient in the air channel with an eddy promoter air screen. Tests were conducted at Reynolds numbers of 920, 1550, and 2160. In this paper, the effects of the spacing of the cylindrical bars and plastic ribs on the heat transfer performance are studied experimentally. Also, the performance of eddy promoter air screens as a function of enhanced heat transfer coefficient and increased pressure drop is investigated. Results show that the eddy promoter air screens have the highest efficiencies at Reynolds of 1550 and double the convective heat transfer coefficient of the air with respect to a smooth channel.

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: none
Teacher disagreement score0.635
Threshold uncertainty score0.787

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 head0.011
GPT teacher head0.210
Teacher spread0.199 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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