Energy dissipation in a deep tailwater stilling basin with partial flaring gate piers
Bibliographic record
Abstract
Flaring gate piers (FGPs) have been used to increase energy dissipation in stilling basins downstream of spillways. For projects with a low water head and large unit discharge together with a deep tailwater level, energy dissipation inside a conventional stilling basin is usually insufficient. This paper proposes a new partial flaring gate pier (partial FGP) scheme to intensify the energy dissipation inside the stilling basin. The results for the no FGP scheme, the conventional FGP scheme, and the partial FGP scheme were compared using a physical model study and numerical simulations. It was found that the partial FGP scheme (the alternation of flaring and no flaring gate piers in chambers) can contain the submerged hydraulic jump and high-speed water jet in the upstream region of the stilling basin. Thus, the water jet from the FGP chamber was forced to laterally diffuse, thereby intensifying the shear friction and turbulent kinetic energy and forming a vertical vortex from the bottom to the surface. Compared with the other two schemes, the flow pattern in the partial FGP scheme was improved significantly with much deeper air entrainment depth inside the stilling basin and much lower turbulent kinetic energy in the outgoing flow. The mean velocity of the outgoing flow also decreased by more than 20%. The common problems of secondary hydraulic jump outside the stilling basin were eliminated.
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".