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Record W2313037244 · doi:10.2514/6.2006-3806

Comparative Study of Turbulence Models in Predicting the Thermal and Hydrodynamic Characteristics of a Micro Heat Exchanger

2006· article· en· W2313037244 on OpenAlexaff
Elmer Galvis, B.A. Jubran, Jiawen Xi, Kamran Behdinan, Zouheir Fawaz

Bibliographic record

Venue9th AIAA/ASME Joint Thermophysics and Heat Transfer Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTurbulenceHeat exchangerThermalMechanicsMaterials scienceMechanical engineeringPhysicsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

A Micro Heat Exchanger (MHE) can effectively control the temperature of surfaces in high heat flux applications. In this study, several turbulence models are analyzed using a 3-D finite element model of a MHE. The MHE consists of a narrow planar flow passage between flat parallel plates with small cylindrical pin fins spanning these walls. The pin fin array geometry investigated is staggered, with pin diameter of 8.46 mm, height to diameter ratio of 1.0 and streamwise (longitudinal) and spanwise (transverse) to diameter ratios of 1.5 and 2.5, respectively. Pressure loss and heat transfer simulated results for 4,000≤Re≤50,000 are reported and compared with previously published numerical and experimental results. An overall thermal performance parameter is defined in terms of both the Nusselt number and the friction factor, in order to investigate the performance of the MHE at different Reynolds numbers. It was found that the flat micro pin fin overall thermal performance exceeded the parallel plate counterpart (smooth channel) by a factor of 1.2 to 2.2 in the investigated Reynolds number range. Moreover, among the six turbulence models investigated, the RNG model tends to be the best model to predict both the Nusselt number and the friction factor and capture the main feature of the flow field in MHE.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score0.806

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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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 designSimulation or modeling
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

Citations0
Published2006
Admission routes1
Has abstractyes

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