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Record W2129926424 · doi:10.1115/1.1773586

Experimental Investigation of the Potential of Metallic Porous Inserts in Enhancing Forced Convective Heat Transfer

2004· article· en· W2129926424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Heat Transfer · 2004
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMaterials sciencePressure dropHeat transferPorosityLaminar flowMechanicsTurbulenceComposite materialHeat fluxConvective heat transferThermodynamicsPorous mediumReynolds number

Abstract

fetched live from OpenAlex

Abstract The present experimental work investigates the effect of a metallic porous matrix, inserted in a pipe, on the rate of heat transfer. The pipe is subjected to a constant and uniform heat flux. The effects of porosity and thickness of the porous matrix on the heat transfer rate and pressure drop are investigated. That is, the surface temperature distribution along a heated section of the pipe, the pressure drop over this section, as well as the inlet temperature of the air were continuously monitored with a data acquisition system and recorded when steady-sate conditions were attained. The results obtained for a range of Reynolds numbers 1000–4500, comprise both laminar and turbulent regime. Also, the results are compared with the clear flow case where no porous insert was used. It is shown that higher heat transfer rates are achieved when using porous inserts at the expense of a reasonable pressure drop, which depends on the permeability of the porous matrix.

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.

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 score0.019
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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