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Record W7082383906 · doi:10.1016/j.ijpvp.2025.105660

Comparison of Stresses in the Junctions of Shell Structures with Bolted Flange Rings

2025· article· en· W7082383906 on OpenAlexafffund

Bibliographic record

VenueInternational Journal of Pressure Vessels and Piping · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture, Water, and Health
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsShell (structure)FlangePressure vesselFinite element methodShell theory

Abstract

fetched live from OpenAlex

Bolted flange joints are favored across a spectrum of pressure vessel applications within diverse industries, owing to their simplicity in installation and operation. However, ensuring their structural integrity and leak-proof performance necessitates careful consideration of both operational conditions and the nature of the connected shell. Yet, the existing ASME BPV Code for flange design lacks inclusion of a leakage criterion or flexibility analysis, hindering accurate assessment of these critical characteristics. This research aims to comprehensively evaluate the integrity and leakage resilience of various shell configurations attached to flange rings. The investigation will scrutinize pivotal factors such as flange rotation and stress distribution at the flange-shell interface, leveraging diverse shell theories across three distinct flange sizes: NPS 26, 48, and 60. Additionally, four prevalent types of shell connections—cylindrical, spherical, dish, and conical—will be juxtaposed. Notably, all shell connections are directly affixed to the raised-face flange ring. To facilitate comparison and validation, these shell connections will be simulated using a versatile finite element program, augmenting the analytical approach. Noteworthy is the incorporation of the gasket's nonlinear behavior in the finite element analysis, a crucial aspect overlooked in the analytical modeling process.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.120

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 head0.014
GPT teacher head0.302
Teacher spread0.289 · 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 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
Published2025
Admission routes2
Has abstractyes

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