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Record W2053333881 · doi:10.5539/jmsr.v3n2p44

Prediction of Onset of Mode I Delamination Growth Under a Tensile Spectrum Load

2014· article· en· W2053333881 on OpenAlexvenueno aff
J. Raju, Manjusha S. Duragkar, N. Jagannathan, C. M. Manjunatha

Bibliographic record

VenueJournal of Materials Science Research · 2014
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDelamination (geology)Tension (geology)Structural engineeringFinite element methodUltimate tensile strengthStress (linguistics)Composite materialEngineering

Abstract

fetched live from OpenAlex

In this investigation, the onset of mode I delamination growth under a standard aircraft spectrum load sequence, mini-FALSTAFF truncated to contain only tension-tension fatigue cycles is predicted. The study was carried out on a standard Double Cantilever Beam (DCB) test specimen of Uni Directional (UD) layup Carbon Fiber Composite (CFC) IMA/M21. Finite element modeling and analysis was carried out using ABAQUS standard to determine Strain Energy Release Rate (SERR) by Virtual Crack Closure Technique (VCCT). Both two dimensional (2D) and three dimensional (3D) models were studied. Using the FE analysis results, an empirical equation was derived for SERR (G) and load (P) relationship. Further, the load cycles in the spectrum sequence were rain flow counted to separate individual cycles. For each of the counted cycle, the SERR was obtained and corresponding N onset for that cycle was estimated from the Constant Onset Life Diagram (COLD) of the material. Linear damage accumulation law was used to predict the number of spectrum load blocks required for onset of mode I delamination growth. The prediction was carried out for various reference loads (P ref), and the corresponding reference SERR (G ref) of the spectrum sequence. Predicted results show that decreasing the reference stress increases the mode I delamination onset life under spectrum loads.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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)0.0000.000

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 head0.058
GPT teacher head0.335
Teacher spread0.278 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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