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Record W7116084302 · doi:10.82417/s7d5-rc02

Numerical simulation study of the blockage effect on the hydrodynamics of a Francis turbine at best efficiency point and partial load

2025· other· en· W7116084302 on OpenAlexaboutno aff

Bibliographic record

VenueEspace ÉTS (ETS) · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsFrancis turbineHydropowerReliability (semiconductor)TurbineComputer simulationFlow (mathematics)Reynolds-averaged Navier–Stokes equationsVibration

Abstract

fetched live from OpenAlex

Hydropower plays a central role in energy decarbonization. As hydraulic turbines are more often used to balance production and demand on electrical grids, the reliability of hydropower installations becomes a critical issue beyond purely economic considerations, making efficient diagnostics essential. This research is part of the MD-Francis project, carried out at the Heki Hydropower Innovation Center at Université Laval. It is conducted in collaboration with a consortium of six industrial partners and four universities, and focuses on detection and diagnostic of anomalies in Francis turbines. Within MD-Francis, various anomalies will be implemented on a structurally homologous model of a Francis turbine. Detailed measurements related to fluid-structure interactions will be performed to identify correlations that could be used to identify and categorize anomalies. This research focuses on the issue of blockages in the inter-blade channels of Francis turbines. These blockages, caused by debris or natural foreign objects (tree trunks, rocks, etc.), can lead to flow imbalances, generating vibrations and affecting overall performance. The blockage causes a head loss, resulting in a reduction of power. Additionally, these vibrations and imbalances can, in turn, cause more significant damage, potentially leading to major shutdowns of the turbine-generator unit. The main objective is to analyze the impact of these blockages on hydrodynamics and performance at the best efficiency point (BEP) and part load (PL) to guide experimental investigations. Numerical flow simulations of the unobstructed Francis, using a RANS approach, are presented and validated against experimental data to form a comparison baseline. Different blockages are then modelled as porous domains located inside an inter-blade channel, allowing for a realistic representation of partial flow restrictions. URANS simulations are used to analyze the hydrodynamic effects of those blockages on the flow. The analysis presented includes the impact on turbine performance, the unbalanced flow distribution in the distributor and runner, the resultant radial forces on the runner, the torque applied to adjacent blades, the rotor-stator pressure signals, and the impact on vortex development in the draft tube.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.254
Teacher spread0.247 · 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.

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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