Validating Numerical CFD Simulations With Experimental Data for Turbulence Phenomena in Axial Flow Gas Turbine Diffusers
Bibliographic record
Abstract
Scale models give engineers an excellent understanding of the aerodynamic behavior behind their design; nevertheless, scale models are time consuming and expensive. Therefore computer simulations such as Computational Fluid Dynamics (CFD) are an excellent alternative to scale models. One must ask the question, how close are the CFD results to the actual fluid behavior of the scale model? In order to answer this question the engineering team investigated the performance of a large industrial Gas Turbine (GT) exhaust diffuser scale model with performance predicted by commercially available CFD software. The experimental results were obtained from a 1:12 scale model of a GT exhaust diffuser with a fixed row of blades to simulate the swirl generated by the last row of turbine blades five blade configurations. This work is to validate the effect of the turbulent inlet conditions on an axial diffuser, both on the experimental front and on the numerical analysis approach. The object of this work is to bring forward a better understanding of velocity and static pressure profiles along the gas turbine diffusers and to provide an accurate experimental data set to validate the CFD prediction. For the CFD aspect, ANSYS CFX software was chosen as the solver. Two different types of mesh (hexagonal and tetrahedral) will be compared to the experimental results. It is understood that hexagonal (HEX) meshes are more time consuming and more computationally demanding, they are less prone to mesh sensitivity and have the tendancy to converge at a faster rate than the tetrahedral (TET) mesh. It was found that the HEX mesh was able to generate more consistent results and had less error than TET mesh.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".