Numerical investigation of non-equilibrium steam condensing flow in various Laval nozzles
Bibliographic record
Abstract
Investigation of condensing steam flow plays an important role due to the significance of the steam turbines which is a considerable part of global power generation. Condensation has an enormous impact on the efficiency of the low-pressure turbine which is lower in comparison with high-pressure steam turbine efficiency and study of this problem highly important. Turbine blade erosion, thermodynamic and aerodynamic losses, high level of the liquid mass fraction are caused by condensation in the low-pressure turbine stages. Experiments and numerical studying should be conducted to examine the condensation process and various losses caused by condensation. \n \nIn present work, the computational fluid dynamic (CFD) simulations were carried out to research the behaviour of non-equilibrium condensing steam flow inside the last turbine stages. For the CFD simulation, the Eulerian-Eulerian approach was applied. The shear stress transport (SST) k-ω turbulence model was used for solving the turbulence of steam flow. Various convergent-divergent (CD) nozzles were modelled for investigation of the surface tension effect on condensing steam flow. The model was validated with experimental data from literature sources. The results showed that the nucleation rate, droplet numbers and wetness are highly sensitive to the bulk tension factor. The surface tension of the liquid droplets significantly influences the correctness of simulated results provided by the nucleation model. The accuracy of the applied model is satisfactory to capture the condensation shock.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".