MétaCan
Menu
Back to cohort

Effects of different momentum ratios and Reynolds number in a T-junction with an upstream elbow

2024· article· en· W4401833493 on OpenAlexfundno aff
Yew Hoong Wong, Lisa Lampunio, Yu Duan, M.D. Eaton, Michael Bluck

Bibliographic record

VenueNuclear Engineering and Design · 2024
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaSingapore Eye Research InstituteNational Research Foundation
KeywordsReynolds numberMechanicsMomentum (technical analysis)Upstream (networking)ElbowPhysicsStatistical physicsMathematicsEngineeringEconomicsTurbulenceMedicineTelecommunicationsAnatomyFinancial economics

Abstract

fetched live from OpenAlex

This study focuses on analysing thermal mixing in T-junctions with varying momentum and Reynolds number ratios, utilizing computational fluid dynamics (CFD) simulations. The T-junction is a critical component of the primary nuclear thermal–hydraulic circuit within a pressurized water-cooled reactor (PWR). The T-junction connects the pressurizer (PRZ) with the steam generator (SG) and the reactor pressure vessel (RPV). Water from the PRZ and the SG are at different temperatures and incomplete thermal mixing occurs when these two fluid streams meet at the T-junction. This incomplete thermal mixing can induce thermal stratification of the water within the T-junction as well as thermal striping phenomena. Thermal striping phenomena can lead to fluctuations of the temperature at the inner pipe wall of the T-junction. Thermal stratification and thermal striping phenomena can induce thermo-mechanical fatigue and eventual pipe failure which can affect the safety of the reactor. Therefore, a high-fidelity, mechanistic understanding of the turbulent thermo-fluid mixing within T-junctions of PWRs might lead to improvements in component reliability and safety within nuclear power plants (NPPs). The primary aim of the research, presented in this paper, is to understand and quantify the effect of variations in the momentum ratio on the turbulent fluid flow within T-junctions. This is achieved by either varying the branch pipe diameter while keeping the inlet velocity constant (part one) or by adjusting the branch pipe inlet velocity while maintaining a constant diameter (part two). Despite the different variations in momentum ratios, the specific momentum ratios under consideration in both parts of the study remain consistent (namely 98 and 66.4). It is also noteworthy that the momentum ratios considered in the paper can be classified as wall-jet and impinging-jet, according to the definition in (Hosseini, Yuki, & Hashizume, 2008). It should be noted that the momentum ratio is manipulated by adjusting the flow parameters, leading to variations in the Reynolds number ratio between the main pipe inlet and the upstream branch pipe at the T-junction. The turbulent flows in the cases that are considered are simulated using the Improved delayed detached eddy simulation (IDDES-SST) model. The numerical results from these simulations indicate, for the considered momentum ratios, that maintaining the same momentum ratio does not produce similar mean flow behaviour and turbulent quantities of interest (QoI). For instance, the size of the flow recirculation zone is more likely linked to the diameter of the branch pipe. Moreover, the turbulent QoI and temperature fluctuations at the determined locations are likely affected by the changed flow recirculation zone as well as the Reynolds number of the branch pipe flow.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNuclear Engineering and DesignSame topicNuclear Engineering Thermal-HydraulicsFrench-language works237,207