Classification of Flow Patterns in Angled T-Junctions for the Evaluation of High Cycle Thermal Fatigue
Bibliographic record
Abstract
Temperature fluctuations caused by the mixing of hot and cold streams at tee junctions may lead to high cycle thermal fatigue (HCTF) failure. It is necessary to evaluate the integrity of structures where the HCTF may occur. Therefore, the Japan Society of Mechanical Engineers (JSME) published “Guideline for Evaluation of High Cycle Thermal Fatigue of a Pipe (JSME S017),” in 2003, which provides the procedures and methods for evaluating the integrity of structures with the potential for HCTF. In JSME S017, one of the important procedures of thermal fatigue evaluation is to classify the flow patterns at tee junctions, because the degree of thermal fatigue damage is closely related to the flow pattern downstream of the mixing junction. The conventional characteristic equations for classifying flow patterns are only applicable to 90-deg tee junctions (T-junctions). However, angled tee junctions other than 90 deg (Y-junctions) are also used in chemical plants and refineries for reducing the pressure drop in the mixing zone and for weakening the force of the impingement of the branch pipe stream against the main pipe. The aim of this paper is to develop general characteristic equations applicable to both T- and Y-junctions. In this paper, general characteristic equations have been proposed based on the momentum ratio for all angles of tee junctions. Further, the validity of the proposed characteristic equations and their applicability to all angles of tee junctions have been confirmed using computational fluid dynamics (CFD) simulations. The results have also highlighted that the angle of the branch pipe has a significant effect on increasing the velocity ratio range for less damaging deflecting jet flow pattern, which is an important finding that could be used to extend the current design options for piping systems where HCTF may be a concern. In addition, categorization 3 is recommended as a more proper method for classifying flow patterns at tee junctions when evaluating the potential for thermal fatigue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".