Effects of different momentum ratios and Reynolds number in a T-junction with an upstream elbow
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".