MétaCan
Menu
Back to cohort
Record W2988670099 · doi:10.1520/stp153220120001

Dependence of the Heat Transfer Coefficient at Quenching on Diameter of Cylindrical Workpieces

2012· book-chapter· en· W2988670099 on OpenAlexaff
Božidar Liščić, Saša Singer, Hartmut Beitz

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsQuenching (fluorescence)Heat transfer coefficientMaterials scienceHeat fluxMechanicsHeat transferThermodynamicsThermal conductionConstant (computer programming)Cooling curveBoilingOpticsComposite materialPhysicsMetallurgy

Abstract

fetched live from OpenAlex

For computer simulation of a quenching process, the fundamental prerequisite is to have the relevant heat transfer coefficient (HTC) calculated as function of workpiece's surface temperature and time respectively. In order to calculate the HTC, experimental measurement of the temperature-time history (cooling curve) near the workpiece surface is necessary. In this investigation, cylindrical probes of 20, 50, and 80 mm diameter are used. The cooling curve was measured always at 1 mm below the surface of the probe. Special care has been taken to keep all other factors (design of the probes, temperature measurement, quenching conditions, and calculation procedure), which can influence on the calculated HTC, constant to assure that the only variable is the diameter of the probe. Supposing a radially symmetrical heat flow at half length of the probe, the HTC was calculated using one-dimensional (1-D) inverse heat conduction method. The unexpected striking result of this investigation is the fact that for biggest probe diameter (80 mm), the calculated HTC as function of surface temperature does not show the film boiling phase. A plausible explanation of this effect is given based on the critical heat flux density. The possibility to establish a simple fixed relation (a correction factor) between the HTC and the diameter of cylinders 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 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: none
Teacher disagreement score0.985
Threshold uncertainty score0.989

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)0.0010.000

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 head0.016
GPT teacher head0.202
Teacher spread0.187 · 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

Citations2
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicHeat Transfer and OptimizationFrench-language works237,207