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Record W2806856384 · doi:10.1115/1.4040421

Elastic Interaction in Bolted Flange Joints: An Analytical Model to Predict and Optimize Bolt Load

2018· article· en· W2806856384 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2018
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFlangeBolted jointStructural engineeringFinite element methodGasketEngineeringTension (geology)PreloadJoint (building)Compression (physics)Materials scienceMechanical engineeringComposite material

Abstract

fetched live from OpenAlex

Bolted flange joints are widely used in the nuclear power plants and other industrial complexes. During their assembly, it is extremely difficult to achieve the target bolt preload and tightening uniformity due to elastic interaction and criss-cross talks. In addition to the severe service loadings, the initial bolt load scatter increases the risk of leakage failure. The objective of this paper is to present an analytical model to predict the bolt tension change due to elastic interaction during the sequence of initial tightening. The proposed analytical model is based on the theory of circular beams on linear elastic foundation. The elastic compliances of the flanges, the bolts, and the gasket due to bending, twisting, and axial compression are involved in the elastic interaction and bolt load changes during tightening. The developed model can be used to optimize the initial bolt tightening to obtain a uniform final preload under minimum tightening passes. The approach is validated using finite element analysis (FEA) and experimental tests conducted on a NPS 4 class 900 weld neck bolted flange joint.

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.

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 score0.156
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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