ANALYSE DE LA PERFORMANCE D'UN DISSIPATEUR D'ÉNERGIE DE TYPE AUGE
Bibliographic record
Abstract
La stabilite d’un dissipateur de type auge peut etre evaluee sur la base de l’etude des efforts hydrodynamiques, en terme de pression et de fluctuation de pression, agissant sur le radier en aval. Les resultats des essais realises sur deux types de radier, l’un fixe en beton et l’autre en materiaux meubles, ont montre que les efforts hydrodynamiques dependent fortement du degre de turbulence de l’ecoulement, des caracteristiques geometriques du dissipateur exprimees par sa hauteur de sortie et du type de radier en aval. De plus, selon les circonstances, l’affouillement donne lieu a des efforts amoindris refletant un ecoulement etabli. Bien que les sollicitations sur le radier soient attenuees par l’affouillement, un dissipateur ayant une tres faible hauteur de sortie presente toujours une distribution de type (A, I), refletant une distribution non securitaire. De plus, l’ecoulement peut engendrer le dechaussement de la structure. Un dissipateur ayant une hauteur de sortie moyenne n’est juge performant qu’en presence d’un faible debit, condition non assuree pendant les crues. Cependant, un dissipateur de grande hauteur de sortie semble offrir une meilleure performance. Ce dernier, non seulement favorise un ecoulement plus stable (une distribution de type (B, II)) dans la zone proche du dissipateur, mais aussi genere un depot de materiaux au pied du dissipateur pouvant proteger ses fondations et augmenter sa stabilite.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".