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Record W4385583333 · doi:10.1080/15567036.2023.2241409

Critical decision making for rehabilitation of hydroelectric power plants

2023· article· en· W4385583333 on OpenAlexaff
Kutay Çelebioğlu, Ece Aylı, Oğuzhan Ulucak, Selin Aradağ, Jerry Westerman

Bibliographic record

VenueEnergy Sources Part A Recovery Utilization and Environmental Effects · 2023
Typearticle
Languageen
FieldEngineering
TopicCavitation Phenomena in Pumps
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsHydroelectricityReliability (semiconductor)Process (computing)Computational fluid dynamicsTurbineRehabilitationReliability engineeringComputer scienceEngineeringMarine engineeringPower (physics)Operations researchMechanical engineering

Abstract

fetched live from OpenAlex

Due to their diminishing performance, reliability, and maintenance requirements, there has been a rise in the demand for the restoration and renovation of old hydroelectric power facilities in recent decades. Prior to initiating a rehabilitation program, it is crucial to establish a comprehensive understanding of the power plant’s current state. Failure to do so may result in unnecessary expenses with minimal or no improvements. This article presents a systematic rehabilitation methodology specifically tailored for Francis turbines, encompassing a methodological approach for condition assessment, performance testing, and evaluation of rehabilitation potential using site measurements and CFD analysis, and a comprehensive decision-making process. To evaluate the off-design performance of the turbines, a series of simulations are conducted for 40 different flow rate and head combinations, generating a hill chart for comprehensive evaluation. Various parameters that significantly impact the critical decision-making process are thoroughly investigated. The validity of the reverse engineering-based CFD methodology is verified, demonstrating a minor difference of 0.41% and 0.40% in efficiency and power, respectively, between the RE runner and actual runner CFD results. The optimal efficiency point is determined at a flow rate of 35.035 m3/s, achieving an efficiency of 94.07%, while the design point exhibits an efficiency of 93.27% with a flow rate of 38.6 m3/s. Cavitation is observed in the turbine runner, occupying 27% of the blade suction area at 110% loading. The developed rehabilitation methodology equips decision-makers with essential information to prioritize key issues and determine whether a full-scale or component-based rehabilitation program is necessary. By following this systematic approach, hydroelectric power plants can efficiently address the challenges associated with aging Francis turbines and optimize their rehabilitation efforts.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.007
GPT teacher head0.226
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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