Large Eddy Simulation Analysis of Fluid Temperature Fluctuations at a T-junction for Prediction of Thermal Loading
Bibliographic record
Abstract
T-junctions are widely used for fluid mixing in power and process plants. Temperature fluctuations generated by the mixing of hot and cold fluids at a T-junction can cause high cycle thermal fatigue (HCTF) failure. The existing Japanese guideline for evaluating HCTF provides margin that varies greatly depending on the case for the evaluation result. Computational fluid dynamics (CFD)/finite element analysis (FEA) coupling analysis is expected to be a useful tool for the more accurate evaluation of HCTF. Precise temperature fluctuation histories are necessary to determine the thermal loads because fatigue damage prediction requires temperature fluctuation amplitudes and their cycle numbers. The present investigation was intended to discover the accurate prediction methods of fluid temperature fluctuations, prior to performing CFD/FEA coupling analysis. Large eddy simulation (LES) turbulence models suitable for the simulation of unsteady phenomena were investigated. The LES subgrid scale (SGS) models used included the standard Smagorinsky model (SSM) and the dynamic Smagorinsky model (DSM). The effects of numerical schemes for calculating the convective term in the energy equation on the simulation results were also investigated. LES analyses of the flow and temperature fields at a T-junction were carried out using these numerical methods. For comparison, the simulation conditions were the same as the experiment in literature. All of the simulation results show the flow pattern of a wall jet with strong flow and temperature fluctuations, as observed in the experiment. The simulation results indicate the numerical schemes have a great effect on the temperature distribution and the temperature fluctuation intensity (TFI). The first-order upwind difference scheme (1UD) significantly underestimates the TFI for each LES SGS model, although it exhibits good numerical stability. However, the hybrid scheme (HS), which is mainly the second-order central difference scheme (2CD) blended with a small fraction of 1UD, can better predict the TFI for each LES SGS model. Furthermore, the DSM model gives a prediction closer to the experimental results than the SSM model, while using the same numerical scheme. As a result, it was found through the systematic investigations of various turbulence models and numerical schemes that the approach using the DSM model and the HS with a large blending factor could provide accurate predictions of the fluid temperature fluctuations. Furthermore, it is considered that this approach is also applicable to the accurate prediction of any other scalar (e.g., concentration), based on the analogy of scalar transport phenomena.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".