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Record W4404370238 · doi:10.1115/pvp2024-123527

Evaluation of Service Removed HP40-Modified Reformer Tubes and Development of Multi-Axial Pressurization Testing in Full-Size Components

2024· article· en· W4404370238 on OpenAlexaff
Alex Bridges, Eeva Griscom, Michael Gagliano, John Siefert, Jorge Penso, Jordan Barrass

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCyclone Separators and Fluid Dynamics
Canadian institutionsShell (Canada)
Fundersnot available
KeywordsCabin pressurizationMaterials scienceMechanical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract Steam methane reforming (SMR) is the most widely used process for bulk hydrogen production and accounts for most of the hydrogen produced worldwide. As this is an highly endothermic reaction, large amount of heat must be supplied to the system, thus requiring heat resistant materials capable of withstanding continued operation at temperatures exceeding 1,500°F (815°C). In this study, microstructural characterization and mechanical testing was conducted on in-service exposed HP40-modified material. Creep damage and changes in precipitate structure were characterized using a variety of microscopy tools. The effect of microstructural evolution on high temperature creep performance due to long-term aging was investigated using standard creep testing methods using round bar specimens. Additional challenges, such as variation in material pedigree, impact of grain structure on performance and importance of test sample orientation are also discussed. The results show that long-term exposure to high temperatures will reduce the overall life of components; however, the resulting ductility increased in samples exposed to the highest temperature. To address challenges with traditional life management procedures, a method for full-scale, high temperature pressurized creep testing of SMR furnace tube samples is being developed and initial results are presented and compared against those from traditional creep specimen geometries.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.057
GPT teacher head0.276
Teacher spread0.220 · 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 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
Published2024
Admission routes1
Has abstractyes

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