Simulations and validation of an axial-flow pit turbine
Bibliographic record
Abstract
The geometric design of an axial-flow pit-turbine is essential as flow separation will decrease turbine efficiency. In this thesis, steady Reynolds Averaged Navier Stokes (RANS) simulations were conducted with a two-equation closure model and validated with experimental results. The research focuses on evaluating the performance of a k-omega Shear Stress Transport (SST) turbulence model with wall functions in separated flows. RANS simulations were carried out in OpenFOAM using an unstructured grid for both lab and full-scale models of the pit-turbine. By testing a lab-scale model, Particle Image Velocimetry (PIV) was performed to provide insights into the flow behaviour in regions of separation. It was observed that the pressure results from the lab and full-scale RANS models were in agreement with experimental data except in regions of an adverse pressure gradient. The k-omega SST model is sensitive to wall-functions and therefore flow properties near the wall are incorrectly calculated in regions of flow separation. At sharp streamline curvatures, the pressure drop is overpredicted for both the lab and full-scale models. However, at very large Reynolds numbers for the full-scale turbine, the turbulence model underpredicts and delays flow separation at a larger radius of curvature when compared to experiments.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".