On the Effects of Unsteadiness on the Condensation Process in Low-Pressure Steam Turbines
Bibliographic record
Abstract
The condensation process in a turbomachine is in reality an essentially random and unsteady phenomenon. On a time-averaged basis, the condensation zone is spread over a much greater distance in the flow direction than a simple steady-flow calculation would indicate. The droplet growth rate also shows different characteristics which are observed in experiments measured in real low-pressure steam turbines. These differences are mainly introduced by the large-scale temperature fluctuations which are caused by the segmentation of blade wakes by successive blade rows. Furthermore, the additional losses by condensation have to be reconsidered for an unsteady simulation. This paper describes a time-accurate Eulerian/Lagrangian two-phase model which is implemented within the DLR in-house code TRACE [1]. The phases are coupled through appropriately generated source terms for heat, mass and momentum. For the subcooled thermodynamic properties of steam the local formulation of IAPWS-IF97 [2, 3] is used. The implementation has been validated in a previous publication of the author [4] using one and two-dimensional experiments of Laval nozzles and a cascade blade from literature. The focus of this work is on the unsteady Phenomena which are investigated in a stage of an industrial low-pressure steam turbine.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".