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Record W4381619500 · doi:10.11159/ffhmt23.159

Quenching Of Moving Aluminum Sheets In Fields Of FlatAnd Full-Jet Nozzles

2023· article· en· W4381619500 on OpenAlexvenueno aff
Stephan Ryll, Bilal Mehdi, Eckehard Specht

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2023
Typearticle
Languageen
FieldMaterials Science
TopicHigh-Velocity Impact and Material Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleAluminiumQuenching (fluorescence)Materials scienceJet (fluid)MetallurgyMechanical engineeringMechanicsComposite materialPhysicsEngineeringOpticsFluorescence

Abstract

fetched live from OpenAlex

The cooling rate of moving aluminum alloy sheets (AA6082) was experimentally investigated.For this purpose, 5mm thick sheets were electrically heated in a furnace to a temperature of 520 C and then cooled with water using different nozzle fields.The nozzles were located at a distance of 50 mm from the sheet.A combination of flat and full jet nozzles was used in two different nozzle configurations, each with the nozzles of one type arranged horizontally, resulting in two rows of nozzles.In the first configuration, two nozzles of each type 70 mm distance from each other were used.For the second configuration, three flat jet nozzles and three full jet nozzles, 35mm apart from each other were combined to form a nozzle field.Flat jet nozzles with a jet angle of 45 and full jet nozzles with a diameter of 1.05 mm were used.The pressure at the nozzle outlet was kept constant at 2 bar in each case.During the cooling process, the sheet velocity was varied from 5 mm/s to 10 mm/s and the temperature field of the black-coated rear side of it was measured with a high-resolution infrared camera.The high frame rate of 200 fps of the IR camera allows for precise determination of the thermal temporal and spatial data of the sheet.The infrared images and the corresponding cooling curves are qualitatively analysed as a function of the sheet speed and nozzle configuration.The investigation aimed to gain knowledge about the choice of nozzle spacing and the cooling intensity of the nozzle type that results in homogeneous cooling.This study can be used for the optimization of existing plants and the design of new plants.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.450

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)0.0000.000

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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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 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
Published2023
Admission routes1
Has abstractyes

Explore more

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