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Record W1970380231 · doi:10.1115/pvp2008-61005

On the Use of Belleville Washers to Reduce Relaxation in Bolted Flange Joints

2008· article· en· W1970380231 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

VenueVolume 2: Computer Applications/Technology and Bolted Joints · 2008
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsFlangeBolted jointStructural engineeringRigidity (electromagnetism)Finite element methodCreepJoint (building)Mechanical jointFlexibility (engineering)Materials scienceEngineeringComputer scienceComposite materialMathematics

Abstract

fetched live from OpenAlex

Bolted flange joints are prone to leakage when exposed to high temperature. In most cases this is due to relaxation that takes place as a result of material creep. One way to solve this problem is to use Belleville spring washers or longer bolt with spacers. However, there is practically no reliable analytical model that can evaluate the exact number of washers or length of the bolts required to reduce relaxation to a target minimum level. This paper describes an analytical model based on the flexibility and displacement interactions of the joint different elements including the axial rigidity of the flange and bolts, used to evaluate relaxation. The developed analytical flange model can accommodate either Belleville spring washers or longer bolts with spacer tubes to reduce the bolt load loss to a maximum target value. This model is validated by comparison with the more accurate FEA findings. Calculation examples on a bolted flanged joint are presented to illustrate the suggested analytical calculation procedure.

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 score0.355
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.025
GPT teacher head0.229
Teacher spread0.203 · 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