Characteristics of Turbulence and Drag Forces on Foreign Objects in Tube Arrays
Bibliographic record
Abstract
Steam generator tube wear due to foreign objects (FOs) is a significant industry problem. Removal of foreign objects comes with challenges related to accessibility and the environment inside the steam generator. It is therefore of interest to estimate the rate of wear due to foreign objects lodged within the SG; particularly in the tube-sheet region. To estimate wear, the fluid forces acting on the FO are a key input. Much remains unknown regarding the characteristics of fluid forces acting on small objects located within tube arrays. In the work reported here, an exploratory study of fluid forces acting on complex geometry objects was done. Force measurements were conducted in a wind-tunnel on a range of objects to investigate the effect of object geometry on the drag coefficient and the parameters which may be used to develop drag coefficient correlations. Turbulence forces acting on FOs in a tube array were found to be intimately related to turbulence forces acting on the tubes themselves. A direct relation between the corresponding force PSDs, via turbulence force correlations, is reported. This is a potentially useful result with regard to the nature of fluid forces acting on foreign objects in general.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".