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Record W4223463722 · doi:10.3390/polym14071458

Evaluating the Residual Stress and Its Effect on the Quasi-Static Stress in Polyethylene Pipes

2022· article· en· W4223463722 on OpenAlexaff
Na Tan, Liyang Lin, Tao Deng, Yongwu Dong

Bibliographic record

VenuePolymers · 2022
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsResidual stressMaterials scienceHigh-density polyethyleneStress (linguistics)Finite element methodPolyethyleneComposite materialResidualStructural engineeringMathematicsEngineering

Abstract

fetched live from OpenAlex

Residual stress is generated during the production process. It can significantly affect the mechanical performance of pressurized polymer pipes. In this paper, six polyethylene (PE) pipes, including three high-density PEs (HDPE) and three medium-density PEs (MDPE) provided by different suppliers, were tested using a one-slit-ring method to measure the residual stress distribution along the hoop direction. Finite element (FE) simulation and mechanical testing were also employed in an iteration process to obtain the mechanical parameters of the six PE pipes. For the same PE pipe code from different suppliers, the results show that the magnitude of the residual hoop stress can be very different, resulting in different mechanical behaviors. In addition, the results are proposed to explain the scenario that was reported previously, i.e., the different critical quasi-static stress (the time-independent stress) levels of the PE pipes with the same pipe code. Since the quasi-static stress is expected to dominate the long-term behavior of the PE pipes, it is of great importance to carefully consider the effect of the residual stress on the determination of the quasi-static stress.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.346

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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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 designSimulation or modeling
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

Citations7
Published2022
Admission routes1
Has abstractyes

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