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Record W1966737721 · doi:10.1115/1.3148088

Fitness-for-Service Evaluation of Thermal Hot Spots and Corrosion Damage in Cylindrical Pressure Components

2009· article· en· W1966737721 on OpenAlexafffund
P. Tantichattanont, Seshu Madhava Rao Adluri, R. Seshadri

Bibliographic record

VenueJournal of Pressure Vessel Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicFatigue and fracture mechanics
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPipingPressure vesselStructural engineeringInternal pressureMaterials scienceThermalMechanicsFinite element methodHardening (computing)Shell (structure)Composite materialPhysicsEngineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Thermal hot spots and corrosion damage are typical of damages occurring in pressure vessels and piping. Structural integrity of such components needs to be evaluated periodically to determine “fitness-for-service” (FFS) of the components. In the present paper, three alternative methods for level 2 FFS assessments (as described in API 579) are proposed. They are based on variational principles in plasticity, the m-alpha method, the idea of reference volume, and the concept of decay lengths in shells. Decay lengths in the axial and circumferential directions for cylindrical shells are derived based on elastic shell theories. They are used to specify the reference volume participating in plastic action and the extent of what can be called “local” damage. Interaction between longitudinal and circumferential effects is investigated. A linear interaction curve is shown to give good estimation of the “remaining strength factor” for damage of practical aspect ratios. The stretching and bulging effects due to the damage are also studied. The limit defining the threshold to dominance of stretching action is proposed by using an approximate equilibrium calculation based on yield-line analysis. The effectiveness of the proposed assessments is demonstrated through an example and verified by level 3 inelastic finite element analysis.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.652
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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 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

Citations4
Published2009
Admission routes2
Has abstractyes

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