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Record W2104173041 · doi:10.1109/iciasf.1999.827179

A re-evaluation of a pulsed laser technique for measuring surface heat transfer coefficients

2003· article· en· W2104173041 on OpenAlexaff
W. E. Carscallen, Wayne O. Turnbull

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsHeat transfer coefficientHeat transferThermal conductionMaterials scienceMechanicsThermodynamicsLaserMeasure (data warehouse)OpticsPhysicsComposite material

Abstract

fetched live from OpenAlex

Data from a previously reported experimental technique, for measuring wall shear stress using a pulsed laser, is re-evaluated using a new numerically based data reduction technique in order to determine surface heat transfer coefficients. This new technique was suggested by recent experimental studies into the use of uncalibrated liquid crystals and periodic heat fluxes to measure absolute values of the local heat transfer coefficient and wall shear stress. These studies, which were initially based upon a one-dimensional analytical conduction model, demonstrated that the shape of the temperature-time response, to an instantaneous deposition of energy on a surface, is a function of the thermophysical properties of the surface and the value of the local heat transfer coefficient. In order to account for situations where two-dimensional effects are important a new numerical model has been developed. It is possible, using this new model, to extend the previous phase delay technique to measure heat transfer coefficients to the case whereby a pulsed laser is used to initiate a temperature transient. The new results using the old data show reasonable agreement with those predicted by a standard correlation. Suggestions are made on how the technique can be significantly improved.

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)0.001
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: none
Teacher disagreement score0.783
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.048
GPT teacher head0.260
Teacher spread0.212 · 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
Published2003
Admission routes1
Has abstractyes

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