Void Fraction Measurements for Air-Water Pipe Flows Using Quick-Closing Valves and Wire-Mesh Sensors
Bibliographic record
Abstract
In support of a header/feeder phenomena study, an adiabatic, near-atmospheric, air-water flow loop was commissioned simulating a single feeder of a Pressurized Heavy Water Reactor’s primary heat transport system under a postulated Loss of Coolant Accident scenario. An extensive database in representative two-phase flow conditions was collected, 750 tests in total, in order to create a two-phase flow map to be used in the more complex geometries such as header/feeder systems. The flow loop consists of two vertical test sections, for upwards and downwards flow, and one horizontal test section, each with an inner diameter of 32 mm and at least 120 diameters in length. Superficial velocities extended up to 6 m/s for the water and 10 m/s for the air. Void fraction was measured by means of quick-closing valves and a pair of wire-mesh sensors (WMS) in each test section. Two-phase repeatability tests showed that the liquid and gas superficial velocities varied by 1.1% and 0.6% at reference conditions of 2.0 and 2.8 m/s, respectively. The corresponding void fraction measurements varied for the quick-closing valves by at most 6.8%, which indicates a low sensitivity to the closure time of the valves and an appropriate axial distance between them, and 2.3% for the WMS. For both measurement techniques, the largest variations occurred in the vertical downwards test section. For the formal two-phase tests, over 600 distinct flow conditions were performed. The results showed that the two measurement techniques agreed within 5% at high void fractions and low liquid flow rates in vertical flow. For all other cases corresponding to the transitional or dispersed bubbly flow regime, the WMS over-estimated the void fraction by a consistent bias. An empirical correction is proposed, with a root-mean-square error of 6.6% across all tests. The void fraction map resulting from this database provides validation for the WMS measurements, a quantitative assessment of its uncertainty and range of applicability, and will be used as a reference in future tests under similar scale and flow conditions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".