MétaCan
Menu
Back to cohort
Record W4408306282 · doi:10.1080/09715010.2025.2471339

Experimental and numerical studies on the hydraulics of stepped spillways with baffle walls and blocks

2025· article· en· W4408306282 on OpenAlexaff
Kamyab Habibi, Farinaz Erfani Fard, Seyed Amin Asghari Pari, Amir H. Azimi

Bibliographic record

VenueISH Journal of Hydraulic Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsLakehead University
Fundersnot available
KeywordsBaffleHydraulicsGeologyGeotechnical engineeringCulvertMechanicsEngineeringMechanical engineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

The present experimental study investigates the effect of baffle walls and baffle blocks with different heights on the hydraulics and energy dissipation of stepped spillways. Image processing technique was employed to detect internal flow motion, and experiments were simulated to extract detailed information to examine the effect of baffles on flow motion and energy dissipation. The results indicated that placing baffle blocks initiated transverse flows in the spillway, leading to an increase in energy dissipation rate. It was found that the energy dissipation rate increased with increasing the baffle block’s height and reducing the baffle’s width. The optimum baffle’s geometry was achieved when the baffle’s width was 5% of the spillway width and the baffle’s height was 76% of the step height. At the maximum discharge, the optimum baffle geometry was able to dissipate 39% more energy than that of a spillway without baffles. Three zones of Redirected Flow (RF), Mixing Zone (MZ), and Recirculation Zone (RZ) were identified under the pseudo-bottom line, and the size of defined zones plays an important role in energy dissipation rate. Furthermore, the results showed the formation of four complete cycles with approximately 25% of the spillway’s width on each step of the spillway.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.226
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueISH Journal of Hydraulic EngineeringSame topicHydraulic flow and structuresFrench-language works237,207