Hydrodynamic film thickness measurements and CFD analysis identify the root causes of repetitive thrust bearing failures on a 45 MW hydro generating unit at Hydro-Québec
Bibliographic record
Abstract
High temperature level recorded on the thrust bearing of a 45 MW hydro generating unit was resulting in frequent production stoppage. In spite of improvements brought to the oil cooling system since the rehabilitation in 2008, the operator had to activate the bearing oil lift system to keep the temperature below acceptable limits. Primary root cause analysis first pointed to the design of the shoe that was centrally pivoted, not allowing the formation of a thick hydrodynamic film. The removal of a strip of the soft metal layer near the trailing edge of the shoe resulted in a significant surface temperature reduction (about 15 deg. C), as predicted by a CFD model of the oil film. The goal of this machining was to increase the pivoting angle by moving the centre of hydrodynamic pressure. Proximity sensors were installed at each corner of the redesigned shoe to measure the film thickness and the bearing attitude. Signal analysis revealed a step of a magnitude close to the oil film thickness between the two halves of the rotating thrust block. This was the cause of another failure few hours since restarting the unit. The lessons learnt through these measurements and analyses were carefully applied to the ultimate build. The unit now runs with a robust thrust bearing and even survived a significant cooling flow reduction event. This paper presents the CFD analysis results and the measurements acquired during these events.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".