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Record W2625056730 · doi:10.1115/1.4037082

Heat Transfer During High Temperature Gas Flow Through Metal Foam Heat Exchangers

2017· article· en· W2625056730 on OpenAlexaff
Pakeeza Hafeez, S. Chandra, J. Mostaghimi

Bibliographic record

VenueJournal of Heat Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsUniversity of TorontoUniversity of New Brunswick
Fundersnot available
KeywordsMaterials scienceHeat transferMicro heat exchangerPlate heat exchangerHeat exchangerPressure dropComposite materialMetal foamDynamic scraped surface heat exchangerHeat transfer coefficientThermodynamicsCombustorMechanicsCombustionCritical heat fluxChemistryPorosity

Abstract

fetched live from OpenAlex

An experimental study of heat transfer in metal foam heat exchangers fabricated from 10 and 40 pores per inch (PPI) was conducted. Heat exchangers were made by either brazing Inconel sheets to foam or plasma spraying Inconel skins on the foam. A burner test rig was built to produce high temperature combustion gases at either 550 °C or 750 °C that were passed over the exposed surface of heat exchangers that were cooled by passing air through them at rates of up to 200 SLPM. Both pressure drop and temperature rise of the air were measured. Friction factors and volumetric heat transfer coefficients were calculated for air velocities varying from 0.1 to 5 m/s and dimensionless correlations to predict these derived. The heat exchangers with 40 PPI foam were measured to have higher heat transfer rates and larger pressure drop than those with 10 PPI foam. Thermal sprayed heat exchangers were found to perform better than those that were brazed since they had lower thermal contact resistance between the external shell and foam struts. An analytical model was developed assuming local thermal nonequilibrium (LTNE) and predictions from model were found to be in good agreement with experimental results.

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 categoriesMeta-epidemiology (narrow)
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 score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.226
Teacher spread0.213 · 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.

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

Citations5
Published2017
Admission routes1
Has abstractyes

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