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Record W2037669302 · doi:10.1115/ijpgc2002-26082

Tube-to-Tubesheet Joints: Maximum Tensile Stress and Contact Pressure Due to Thermal Loading and Temperature Cycling

2002· article· en· W2037669302 on OpenAlexafffund
M. Allam, A. Bazergui, Luc Marchand, M. Derenne

Bibliographic record

Venue2002 International Joint Power Generation Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceFinite element methodTemperature cyclingUltimate tensile strengthResidual stressComposite materialTube (container)Structural engineeringHeat exchangerJoint (building)DurabilityParametric statisticsThermalMechanical engineeringEngineeringThermodynamics

Abstract

fetched live from OpenAlex

Service reliability and durability of tubular heat exchangers and steam generators are much dependent on the proper response of the tube-to-tubesheet joints to the operating conditions. In this paper a 2-D axisymmetric finite element model is proposed and compared to a 3-D finite element solution for the purpose of predicting the temperature effect on the residual contact pressure and maximum tensile residual stresses of such joints. A parametric study using the finite element results shows that, although thermal loading and temperature cycling have a negligible effect on the maximum tensile residual stresses, the room-temperature initial residual contact pressure may be completely relieved following the initiation of plastic deformation in either the tube or the tubesheet during thermal loading. A comparison between the results of the proposed finite element model and those obtained from the literature shows good agreement. A simplified analytical approach, which may be used for the design of tube-to-tubesheet joints, is also proposed to predict the joint behavior at the operating conditions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.999

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)0.0020.000

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 head0.220
Teacher spread0.195 · 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.

Study designBench or experimental
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

Citations3
Published2002
Admission routes2
Has abstractyes

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