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Record W4396557669 · doi:10.61927/igmin173

Investigation of Lateral Vibrations in Turbine-generator Unit 5 of the Inga 2 Hydroelectric Power Plant

2024· article· en· W4396557669 on OpenAlexaff
Nzita André Mampuya, Phuati Edmond Phuku, Ngimbi Robert Muanda, Ngoma Guyh Dituba, Masiala Mavungu

Bibliographic record

VenueIgMin Research · 2024
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsCégep de l'Abitibi TémiscamingueUniversité du Québec en Abitibi-TémiscamingueGeomechanica (Canada)
Fundersnot available
KeywordsHydroelectricityGenerator (circuit theory)Steam turbineVibrationTurbinePower stationUnit (ring theory)Power (physics)Electrical engineeringEngineeringMechanical engineeringPhysicsAcousticsMathematics

Abstract

fetched live from OpenAlex

The article presents a case study on the Investigation of lateral vibrations in the turbine-generator unit 5 of the Inga 2 hydroelectric power plant in the Democratic Republic of the Congo. Lateral vibrations were experimentally determined using twelve proximity and eddy-current probes, positioned on each measurement plane. The results were analyzed using the Dasylab and R software. Hence, it was observed that the vibration amplitudes of the upper guide bearing, lower guide bearing, and pivot/rotor exceeded acceptable or critical limit values of the international vibration standard for a rotating speed between 100-250 rpm. These excesses can lead to rotor mass imbalances, the eccentricity of the rotor axis relative to the rotation axis of the shaft, and the deformation of the coupling shaft between the upper rotor shaft and the turbine rotor shaft. Subsequently, the means and the variances of the vibration amplitudes were evaluated and compared to the reference values of the international standard. The results of the compliance analysis revealed statistically significant differences between the measured amplitudes and the reference values. Thus, it indicates deviations from international specifications.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.142

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.049
GPT teacher head0.300
Teacher spread0.251 · 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 designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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