CFD Modeling of Low Pressure Steam Turbine Radial Diffuser Flow by Using a Novel Multiple Mixing Plane Based Coupling: Simulation and Validation
Bibliographic record
Abstract
The diffuser and exhaust of low pressure steam turbines shows significant impact on the overall turbine performance. The amount of recovered enthalpy leads to a considerable increase of the turbine power output, and therefore a continuous focus of turbine manufacturers is put on this component. On the one hand, the abilities to aerodynamically design such components is improved, but on the other hand a huge effort is required to properly predict the resulting performance and to enable an accurate modeling of the overall steam turbine and therewith plant heat rate. A wide range of approaches is used to compute the diffuser and exhaust flow, with a wide range of quality. Today it is well known and understood, that there is a strong interaction of rear stage and diffuser flow, and the accuracy of the overall diffuser performance prediction strongly depends on a proper coupling of both domains. The most accurate, but also most expensive method is currently seen in a full annulus and transient coupling. However, for a standard industrial application of diffuser design in a standard development schedule, such a coupling is not feasible and more simplified methods have to be developed. The paper below presents a CFD modeling of low pressure steam turbine diffusers and exhausts based on a direct coupling of the rear stage and diffuser using a novel multiple mixing plane. It is shown that the approach enables a fast diffuser design process and is still able to accurately predict the flow field and hence the exhaust performance. The method is validated against several turbine designs measured in a scaled low pressure turbine model test rig using steam. The results show a very good agreement of the presented CFD modeling against the measurements.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".