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Record W4414563318 · doi:10.1139/cjce-2025-0029

Mitigating scour in aging run-of-river hydropower infrastructure: an analysis of pressure fluctuations in the physical model of Chancy-Pougny (Switzerland)

2025· article· en· W4414563318 on OpenAlexvenueno aff
Tobias Kurth, Davide Wüthrich, Rafael Duarte, Giovanni De Cesare

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic flow and structures
Canadian institutionsnot available
Fundersnot available
KeywordsHydropowerWater pressureCurrent (fluid)Flow (mathematics)Work (physics)Physical modellingHydraulic jumpHydraulic structure

Abstract

fetched live from OpenAlex

Many run-of-river hydropower plants built without stilling basins now experience progressive scour due to prolonged operation and increasingly frequent floods. The Chancy-Pougny dam on the Rhône River, constructed in the 1920s at the Swiss–French border, exemplifies this issue. Severe flow recirculation was identified as the main cause of erosion, with pressure fluctuations increasing between the original and current stilling basin. While earlier work developed scour protection measures through physical modelling and numerical predictions, the present study focuses on analyzing pressure measurements within the stilling basin to assess how fluctuations can be reduced to limit future scour. Effective mitigation strategies include: (1) raising the basin water level, (2) introducing a guidance wall to restore symmetrical flow, and (3) adding various configurations of half-cube concrete prisms to increase roughness and energy dissipation. A life cycle assessment of prism materials and construction methods further supports a sustainable approach to rehabilitating ageing hydraulic infrastructure.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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