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Record W2766759023 · doi:10.1115/pvp2017-66021

Adoption of the Composite Reinforced Pressure Vessels (CRPV) Into the ASME BPV Code

2017· article· en· W2766759023 on OpenAlexaff
Richard Biel, Gregory Cano

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsTransCanada (Canada)
Fundersnot available
KeywordsPressure vesselStructural engineeringComposite numberEngineeringShell (structure)WeldingGlass fiberBoiler (water heating)Mechanical engineeringMaterials scienceComposite materialWaste management

Abstract

fetched live from OpenAlex

Adoption of composite reinforced pressure vessels (CRPV) into the ASME Boiler and Pressure Vessel Code represented advancement in the technology of pressure vessels. The advantage of this construction technique is that the weight of a CRPV for compressed gas service built may be reduced to about one-half conventional pressure vessel of the same capacity. The concept of hoop wrapping fibers in a plastic composite (>90% fiber fill) makes full utilization of the fiber strength as the fibers share the hoop load with a metal cylinder. With reduced hoop stresses in the metal, a substantial reduction in wall thickness is attainable. The process of adoption of this technology presented several challenges and some robust administrative hurdles. These included coordination with ASME BPV Code Section X for the composite application and Section VIII for the steel design and overall acceptance of the Case. The most vexing technical challenge was the inspection of an unfinished weld on the inside of the shell from the outside of the shell. The next challenge was to gain consensus on the testing criteria for the acceptance of finished vessels. Case 2390 was drafted in the winter of 2000 and spring of 2001 and approved for publication after nine revisions with an approval date of October 9, 2002. The Case was subsequently adopted into the body of ASME BPV Code Section VIII, Division 3 [1] (VIII-3) in the 2010 edition.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.012
GPT teacher head0.236
Teacher spread0.224 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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