CFD Simulation of an S-Bend Diffuser With Passive Full Surface Effusion Cooling: Novel Slot Method Approach
Bibliographic record
Abstract
Abstract There are different approaches for CFD simulation of effusion-cooled surfaces, such as the 1D Porous Jump Coefficient and periodic boundary conditions. These methods are used to minimize the cost of simulation; however, each model has its own drawbacks. A novel approach called the “Slot Method” has been introduced for the first time in this research. In this method, based on the porosity of the effusion patches, the effusion holes are replaced by a limited number of slots to reduce the cost and time of simulations. The slot method is simulated using the Reynolds-Averaged Navier-Stokes (RANS) methodology to predict the pressure recovery performance of the diffuser, pressure distributions on the walls, outflow velocity distributions, and outflow temperature distributions. The CFD simulations are compared to experimental data from a rectangular S-duct diffuser as well as CFD simulations with 1D Porous Jump Coefficient boundary conditions reported by NG [1]. Four different numbers of slots are simulated using RANS k-ε models to investigate the effect of the number of slots on the main and secondary flows. The simulations show that the higher number of slots has the potential to mimic the behavior of the effusion holes. The simulations exhibit significantly more accurate predictions compared to the 1D Porous Jump Coefficient in terms of pressure recovery performance in the diffuser. Some discrepancies between the experimental data and the Slot Method in the outflow velocity distribution can be attributed to imprecise boundary conditions in the reported experimental data.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".