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Record W4395464370 · doi:10.18280/i2m.230203

High-Temperature Creep Measurement in Metallic Alloys Using Pulsed Eddy Current Sensors: Influence of Geometry and Magnetic Properties

2024· article· en· W4395464370 on OpenAlexvenueno aff
Shyam T. Venkatesulu, Archana Sharma, V. H. Patankar, Ankur Kaushik, Abhishek Basak, Sunil K. Sinha

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCreepEddy currentMaterials scienceCurrent (fluid)MetalGeometryMetallurgyThermodynamicsElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Due to limited space constraints, the creep measurement of test specimens in material testing reactors is done mostly by time consuming offline techniques viz.: air gauges, diameter gauges, etc.A new compact sensor based on pulsed eddy current testing technique is designed for online creep measurement during mechanical properties testing of metallic alloy specimens for future nuclear reactors.The pulsed eddy current sensors that are positioned opposite to the test specimen measure gaps to estimate creep, by analyzing the slopes of the signals near the Lift-off point of Intersection (LOI) region.The paper discusses the temporal shifts in pulsed eddy current signals when operated at room and high temperatures.Experiments were carried out at room temperature to study the effect of geometry change on test specimens on the linearity and sensitivity of the signals.Similarly, the effect of magnetic test specimens on measurement methodology and sensitivity is also briefed.The results of the study have provided us with confidence in using the online creep measurement sensor technology in material testing reactors with a molten metal coolant medium environment.This technology will help us evaluate the mechanical properties of metallic alloy test specimens, which are intended for use in future generation nuclear reactors.

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: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.923

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 head0.040
GPT teacher head0.272
Teacher spread0.232 · 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
Published2024
Admission routes1
Has abstractyes

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