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Record W3181247911 · doi:10.18280/acsm.450302

Finite Element Investigation on the Performance of Pressure Vessel Subjected to Structural Load

2021· article· en· W3181247911 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicEngineering Structural Analysis Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPressure vesselMaterials scienceFinite element methodInternal pressureDeformation (meteorology)AlloyMetallurgySofteningTitanium alloyFabricationTitaniumAlloy steelComposite materialStructural engineeringCarbon steelEngineeringCorrosion

Abstract

fetched live from OpenAlex

Pressure vessels are highly used in industries and in commercial purposes such as filtration, boiling, softening and hot water storage tanks. Pressure vessels are subjected to thermal loads and structural loads such as internal and external pressures which leads to its deformation. Present work focusses on the modeling of the pressure vessel according to standard dimensions using CREO 6.0 and analyzing it for three different materials and different pressure values using finite element approach. The materials considered in this study for the fabrication of pressure vessel are carbon steel, stainless steel, and titanium alloy. The finite element analysis results have been presented in graphical form. Results indicated that, titanium alloy is able to withstand high stresses and exhibited high factor of safety of 4.10. And among the steels, stainless steel demonstrated low structural performance.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.368
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.261
Teacher spread0.229 · 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