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Record W3095372143 · doi:10.1115/pvp2020-21012

Stress Analysis of SS316L Tube Die Expansion for High Pressure Gas Coolers

2020· article· en· W3095372143 on OpenAlexaff
Zijian Zhao, Abdel‐Hakim Bouzid

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsResidual stressMaterials scienceExpansion jointExpansion chamberThermal expansionDie (integrated circuit)Finite element methodIsotropyStress (linguistics)HydroformingStructural engineeringTube (container)Composite materialMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract SS316L finned tubes are becoming very popular in high-pressure gas exchangers and particularly in CO2 cooler applications. Due to the high-pressure requirement during operation, these tubes require an accurate residual stress evaluation during the expansion process. Indeed, die expansion of SS tubes creates not only high stresses when combined with operation stresses but also micro-cracks during expansion when the expansion process is not very well controlled. This research work aims at studying the elastic-plastic behavior and estimating the residual stress states by modeling the die expansion process. The stresses and deformations of the joint are analyzed numerically using the finite element method. The expansion and contraction process is modeled considering elastic-plastic material behavior for different die sizes. The maximum longitudinal, tangential and contact stresses are evaluated to verify the critical stress state of the joint during the expansion process. The importance of the material behavior in evaluating the residual stresses using kinematic and isotropic hardening is addressed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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