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The Effect of Deformation Stress-Strain and Temperature on The I<sub>c</sub> Degradation of Bi-2223/Ag Tapes

2007· article· en· W2074266055 on OpenAlexaff
Hong Soo Ha, Sang Cheol Kim, Dong Woo Ha, Sang Soo Oh

Bibliographic record

VenueMaterials science forum · 2007
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMaterials scienceComposite materialTension (geology)Stress (linguistics)BendingDeformation (meteorology)Strain (injury)Stress–strain curveSuperconductivityCeramicUltimate tensile strengthCondensed matter physics

Abstract

fetched live from OpenAlex

Bi-2223/Ag tapes are usually used for superconducting power cables and magnets. Bi- 2223 ceramic superconducting core can be damaged under complicated stress and strain conditions such as winding tension, bending and twisting. In this study, we have presented the effect of axial stress and bending strain on the superconducting properties of Bi-2223/Ag tapes. In order to establish the value for 95% retained Ic of the Bi-2223/Ag tapes under various stress-strain conditions, the tension apparatus with bending former was used to apply the tension and bending stress-strain. Tension and bending stress-strain simultaneously applied to the tapes could reduce the critical current even though each applied stress and strain values were not higher than that of 95% retained Ic of the tapes. Complex stress-strain conditions including the thermal stress-strain have accelerated the degradation of the Bi-223/Ag tapes. The deformation temperature was important to maintain the 95% retained Ic of the Bi-2223/Ag tapes after bending or tension deformation because mechanical strength of the tapes can be changed drastically between room temperature and 77 K.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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 designObservational
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
Published2007
Admission routes1
Has abstractyes

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