Last stage stator blade profile improvement for a steam turbine under a non-equilibrium condensation condition: A CFD and cost-saving approach
Bibliographic record
Abstract
Non-equilibrium phenomena and related damages have always been one of the great concerns among researchers, designers, and industry managers. In power plants, the overhaul of turbines during a pre-planned schedule includes checking, repairing, and replacing damaged parts, which always challenge industry investors with variable costs. In this study, a modified profile for the stationary cascade blades of a 200 MW steam turbine is predicted by help of the Computational Fluid Dynamics (CFD) according to a cost-saving approach for a power plant. Wet steam model is used to investigate the flow behavior between the turbine blades, due to the sonication and non-equilibrium phenomena. The numerical model based on the Eulerian-Eulerian approach accounts the turbulence caused by the presence of droplets, condensation shocks and aerodynamics. At first, such model has been carefully validated against the available experimental data. Then, the entrance edge of the blade is designed considering different shapes and sizes. The flow behavior at the entrance edge region has been fully investigated. Finally, according to the criteria for measuring the non-equilibrium flow phenomena (erosion rate, Mach number, entropy, exergy destruction and transfer of mass and heat between flow phases), a modified model for the steam turbine blade considering the economic aspects has been presented. The modified blade model exhibits 88%, 0.13% and 7% reduction in the erosion rate, entropy generation and exergy destruction, respectively. Furthermore, the application of this modified blade profile save 456$ of the total monthly maintenance costs.
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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) | 0.000 | 0.001 |
| 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) | 0.001 | 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".