RESERACH ON STEAM CONDENSING FLOWS IN NOZZLES WITH SHOCK WAVE
Bibliographic record
Abstract
In the Institute of Power Engineering and Turbomachinery of the Silesian University of Technology there is an experimental facility dedicated to the wet steam flow investigation through the nozzles and linear cascades, especially to the identification of the both aerodynamic and thermodynamic losses. The proposed work concerns a novel experimental research comprising the new and more effective techniques for transonic wet steam flow through Laval nozzles. The applied modern experimental techniques are based on the static pressure measurement and Schlieren technique for flow-field visualization. The Schlieren technique is a well-known method to visualize density gradients in compressible flows. It translates phase differences into amplitudes and sometimes color differences that can be seen. A synchronized instantaneous measurement of the pressure and Schlieren photographs allows to capture unsteady effects in the steam condensing flows. Additionally, the chemical analysis of the condensate is planned in order to estimate the impurities content in steam. It confirms the type of the investigated condensation phenomenon, homogeneous or heterogeneous one. This technique has been already validated and used for losses estimation for very simple test cases. Experimental results will be comparison with CFD calculations. The CFD results will be made using ANSYS-CFX and our in-house code for modelling the steam condensing flows. ACKNOWLEDGEMENTS The presented work was supported by the National Science Centre founds within the PBU–7/RIE5/2012.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".