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Record W2357722157

Heat flux calculation in high speed casting of stainless steel

2000· article· en· W2357722157 on OpenAlexaffabout
Baofeng Wang, Uist Baotou

Bibliographic record

VenueJournal of Baotou University of Iron and Steel Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThermocoupleCastingMetallurgyMaterials scienceHeat fluxContinuous castingHeat transferAusteniteThermalMartensitic stainless steelFlux (metallurgy)MartensiteComposite materialMicrostructureMechanicsThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

Heat transfer in the mould plays a very important role in continuous casting.An industrial plant trial was performed in Atlas Steel in Canada to investigate the thermal response of the mould in high speed casting of martensitic and austenitic stainless steel billets.And thermal response of the mould in the casting of high carbon steel was also recorded for comparison.A total of 42 thermocouples was installed on all the four mould walls.Tempreature signals under different operating parameters were recorded and analyzed. The average temperature down the midface of the mould wall from the measured singles was calculated.A mathematical model has been developed for Inverse Heat Conduction Problem.The details of establishment of the model are discussed.And the model is successfully employed to determine mould heat flux profiles down the top of the mould in high speed casting of stainless steel billets from thermocouple measurements.Heat flux distributions for different steel grades and casting speeds were calculated.The influence of casting speed and steel grades on the heat transfer in the mould is discussed.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.175
Teacher spread0.170 · 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
Published2000
Admission routes2
Has abstractyes

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