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Record W4404970605 · doi:10.14447/jnmes.v27i3.a12

Analyzing Metal Material Cooling Intensity: A Method for Obtaining Heat Transfer Coefficients

2024· article· en· W4404970605 on OpenAlexvenueno aff
Deepa Melur Sonnegowda, Lalitha Ponsankarar, Vinay Kumar Domakonda, Kamesh Minnal Ranjan Babu, Vinayak Barewar, Vijay Muni Tadanki, E. Hemalatha, Anusuya Manickam, Aravind Baskaran, Selvarajan Lakshmanan

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2024
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsIntensity (physics)Materials scienceHeat transferHeat transfer coefficientThermodynamicsPhysicsOptics

Abstract

fetched live from OpenAlex

In laminar cooling, the cooling intensity is directly impacted by the heat transfer coefficient and affects the microstructure of metal materials, which in turn affects product performance.In order to determine the laminar cooling heat transfer coefficient, this study suggests a certain approach.Using the nozzle as the center, the dispersal form of coefficient of heat transfer as piecewise function formed a straight line and half sine wave was determined.It lays out the steps to take in experimental data, operating parameters, and structural parameters to get function's characteristic parameters.Experimental results confirm the method's correctness, and the effect of metal-specific characteristics on the temperature field was studied.A solid theoretical foundation for the field production process was provided by the mutual verification of the experimental and computed outcomes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.270
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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