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Record W2056571215 · doi:10.1115/ht2009-88557

Thermal Conductivity and Thermal Contact Resistance of Metal Foams

2009· article· en· W2056571215 on OpenAlexafffund
Ehsan Sadeghi, Ned Djilali, Majid Bahrami

Bibliographic record

VenueVolume 2: Theory and Fundamental Research; Aerospace Heat Transfer; Gas Turbine Heat Transfer; Computational Heat Transfer · 2009
Typearticle
Languageen
FieldEngineering
TopicHeat and Mass Transfer in Porous Media
Canadian institutionsSimon Fraser UniversityUniversity of Victoria
FundersSimon Fraser University
KeywordsThermal conductivityMaterials scienceFluidicsComposite materialThermalThermal contact conductanceMetal foamThermal resistanceAspect ratio (aeronautics)Thermal contactContact resistanceParametric statisticsPorosityCross section (physics)Thermodynamics

Abstract

fetched live from OpenAlex

Unique specifications of metal foams such as relatively low cost, ultra-low density, high surface-area-to-volume ratio, and most importantly, the ability to mix the passing fluid provide them a great potential for a variety of thermal-fluidics applications. In the present study, a compact analytical model for evaluating the effective thermal conductivity of metal foams is developed. The medium structure is represented as orthogonal cylindrical ligaments that are equally spaced and sized. A unit cell is taken to represent the metal foam. The model accounts for varying cross-sectional ligaments which is consistent with microscopic images. A numerical analysis is performed to verify the proposed analytical models. The model predictions are in good agreement with existing experimental data and the present numerical results. A parametric study is then performed to investigate the effects of variation in ligament cross-section geometry, uniformity, and aspect ratio over a wide range of porosities. Moreover, Thermal contact resistance phenomenon is included in the analysis.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.024
GPT teacher head0.272
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueVolume 2: Theory and Fundamental Research; Aerospace Heat Transfer; Gas Turbine Heat Transfer; Computational Heat TransferSame topicHeat and Mass Transfer in Porous MediaFrench-language works237,207