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Record W2794258174 · doi:10.15224/978-1-63248-105-4-23

Current Injection Vortex Promoter for Heat Transfer Enhancement in a Magnetohydrodynamic Duct Flow

2016· article· en· W2794258174 on OpenAlexfundno aff
Hussain Ahmad, J GREGORY, K WISAM

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicFusion materials and technologies
Canadian institutionsnot available
FundersNational Cancer InstituteAustralian Research CouncilUniversiti Teknologi MARAMonash UniversityKementerian Pendidikan MalaysiaUniversity of VictoriaAustralian GovernmentNational Computational Infrastructure
KeywordsMagnetohydrodynamic driveNusselt numberMechanicsDuct (anatomy)Heat transferReynolds numberLorentz forceHeat transfer enhancementMaterials scienceVortexHartmann numberPhysicsThermodynamicsMagnetohydrodynamicsMagnetic fieldHeat transfer coefficientTurbulence

Abstract

fetched live from OpenAlex

The quest for cleaner, cheaper and reliable energy has motivated the development of magnetic confinement fusion reactor technology as a possible means of harnessing the energy produced by nuclear fusion for power generation. However, the magnetohydrodynamic effects act to reduce the thermal-hydraulic performance of the duct flows within the cooling blankets. The present paper reports the investigation of the duct flow with current injection as a vortex promoter for the enhancement of the convection heat transfer. The results indicate a maximum enhancement of approximately 70% over the duct flow without current injection for the highest current amplitude investigated. The competing effects of the Lorentz force and Hartmann damping result in a non-monotonic trend of Nusselt number with respect to friction parameter, while increasing Reynolds number results in a monotonic increase of Nusselt number.

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 categoriesInsufficient payload (model declined to judge)
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 score0.024
Threshold uncertainty score0.998

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)0.0030.000

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 head0.250
Teacher spread0.236 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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