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Record W4414954487 · doi:10.1115/pvp2025-155939

A Study on the Fluidelastic Stability of a Hybrid Layout Array in Two-Phase Flow

2025· article· en· W4414954487 on OpenAlexaff
Sameh Darwish, Njuki Mureithi, Abdallah Hadji, Minki Cho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBundleTube (container)Flow (mathematics)Transverse planeSquare (algebra)LimitingStability (learning theory)Cross section (physics)

Abstract

fetched live from OpenAlex

Abstract An experimental test program was carried out to investigate the stability behavior of the SG tube bundle in the APR1400 nuclear reactor design. The SG has a unique tube bundle geometry. In the vertical legs of the tube bundle, the tube array has a triangular tube geometry. In the upper U-bend, this geometry gradually changes such that at the upper (horizontal) region of the U-bend, the triangular geometry is replaced by a rotated square geometry. The U-bend itself is rectangular in shape (as opposed to the more common semi-circular form). The rectangular form of the U-bend results in a section of the U-bend where the array geometry is a ‘hybrid mixture’ at the transition from rotated triangle to rotated square geometry. The test section design allows tests to be performed under partial admission conditions where the U-bend tube legs are immersed in water for both the single phase (water) and two-phase (air-water) tests. The transverse and streamwise stability behavior is investigated by limiting the number of tube supports to two in the vertical tubes sections. Tests for this supporting condition show that the SG tube bundle is highly stable. The transverse stability is expected in view of the positive support action in this direction. The tube bundle is also found to be fully stable in the streamwise direction, for flow velocities well above the SG operating flow velocity (dynamic pressure) conditions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.302

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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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
Published2025
Admission routes1
Has abstractyes

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