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Record W2025467665 · doi:10.1115/pvp2014-28958

Writing and Reviewing FEA Reports Supporting ASME Section VIII, Division 1 and 2 Designs: Practical Considerations and Recommended Good Practice

2014· article· en· W2025467665 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsBGC Engineering (Canada)
Fundersnot available
KeywordsFinite element methodContext (archaeology)Section (typography)EngineeringDivision (mathematics)Mechanical engineeringCode (set theory)Quality (philosophy)Engineering drawingComputer scienceStructural engineeringSet (abstract data type)

Abstract

fetched live from OpenAlex

Finite element analysis (FEA) is used, with increasing frequency, to supplement or justify the design of an ASME Section VIII, Division 1 or 2 pressure vessel. When this occurs, good engineering practice indicates that a competent engineer should review the finite element analysis report. In some jurisdictions, it is required that a Professional Engineer review and certify the report. This paper discusses some of the practical aspects of both writing and reviewing a good quality FEA report — both in the context of the technical perspective and in the context of Code compliance. This paper will serve as a practical assistant to an engineer reviewing an FEA report, as well as a guide to an engineer preparing an FEA report. Aspects such as properly following Code requirements, following appropriate Design By Analysis methodologies, and applying good design practices will be discussed.

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)0.002
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.314
Teacher spread0.277 · 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