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Use of the inverse method to determine the thermal properties of liquid n-octadecane accounting for natural convection effect

2024· article· en· W4399877946 on OpenAlexaff
Yassine Chérif, Laurent Zalewski, Emilio Sassine, Dominic Groulx

Bibliographic record

VenueInternational Journal of Heat and Mass Transfer · 2024
Typearticle
Languageen
FieldEngineering
TopicPhase Change Materials Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsOctadecaneNatural convectionMaterials scienceInverseThermalConvectionThermodynamicsMechanicsMathematicsChemistryPhysicsGeometry

Abstract

fetched live from OpenAlex

This paper numerically makes use of the inverse method coupled with experimenal results obtained through the fluxmetric method in the Laboratoire de Génie Civil et géo-Environnement (LGCgE) to determine the specific heat, thermal conductivity and thermal expansion coefficient of liquid n -octadecane in an enclosure accounting for natural convection present in the experimental system. The impact of ommiting the presence of natural convection for the thermophysical propertie determination is studied and shows very little effect on the specific heat value but a large error (by a factor of two in this study) on the thermal conductivity value. The correct properties identified when accounting for natural convection ( ρ = 2150 J/kg‧K; k = 0.16 W/m‧K; β = 9.38 × 10 −4 K −1 ) are consistent with the literature. Additional direct numerical work was done to determine the impact of using the different thermal conductivity values, comparing numerical to additional expermental restuls where natural convection was stronger. The thermal conductivity value played a minial role when simulating the behavior of the systems rightly accounting for natural convection. Combining the numerical and experimental resutls allowed the calculation of encolsure specific Nusselt numbers for the various experimental conditions used and a comparison to known correlations. It was determine that the Nusselt number obtained in the liquid n -octadecane were larger by 10 to 48%.

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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.142

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 head0.046
GPT teacher head0.304
Teacher spread0.259 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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