Two-phase flow pattern recognition in a varying section based on void fraction and pressure measurements
Bibliographic record
Abstract
In a hydroelectric turbine, the air injected during operation has an impact on the yield of the machine leading to important losses of energy. To understand those losses and be able to reduce them, a first step is to understand the pattern of the two-phase flows and describe their characteristics in the turbine. Those two-phase flows can be bubbly, intermittent, or annular, with different types of intermittent flow possible. Two-phase flow patterns are well defined in classical geometries such as cylinders with reliable descriptions available [5]. But, there is a critical lack of knowledge for flow patterns in other geometries. In our present work we take interest into a geometry that is a pipe with periodical changes of the section and realize a flow pattern map. To realize this map, we measure the pressure variations and void fraction fluctuations while changing the flow rates of water and air in our test section. We then use our physical understanding of the phenomena to analyze data and identify different flow patterns, characterize them, and build a new flow pattern map.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".