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Record W2766375862 · doi:10.1115/pvp2017-66008

Thermal Stress Ratchet Check in Piping Analysis

2017· article· en· W2766375862 on OpenAlexaff
R. Adibi-Asl, M. Noban, E. Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsAmec Foster Wheeler (Canada)
Fundersnot available
KeywordsRatchetPipingStress (linguistics)ThermalHeat transferRatchet effectStructural engineeringPressure vesselMechanicsRange (aeronautics)Materials scienceEngineeringMechanical engineeringPhysicsThermodynamicsWork (physics)Composite material

Abstract

fetched live from OpenAlex

The ASME B&PV Code, Section III NB-3600 provides a requirement to avoid thermal stress ratcheting under thermal transient conditions. The ratchet check in this code is based on calculating the range of temperature difference between the outside and inside surfaces of the pipe for pairs of load sets. The calculated temperature ranges are then compared with a code equation which is a function of pressure, geometry and material properties. This paper reviews the thermal stress ratchet requirements in ASME Section III Subsection NB with respect to piping analysis. A simplified formula is proposed for thermal stress ratchet check which eliminates the difficulties and ambiguities associate with the formulation provided in the NB-3600 code. The paper also investigates temperature distributions for various components (including flat plate and pipe) by performing heat transfer analysis. The results are compared with the ratchet limit. Using plate solutions to estimate the temperature range of pipe could sometimes give non-conservative results.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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 designSimulation or modeling
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

Citations0
Published2017
Admission routes1
Has abstractyes

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