Computational Fluid Dynamics Modeling of Low Pressure Steam Turbine Radial Diffuser Flow by Using a Novel Multiple Mixing Plane Based Coupling—Simulation and Validation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The diffuser and exhaust of low pressure steam turbines show 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 are 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 computational fluid dynamics (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 (MMP). 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it