Kinetics of crack propagation in hydraulic turbine runner material exposed to river water
Bibliographic record
Abstract
Hydraulic turbines are critical for Quebec's electricity production, as hydroelectricity accounts for 95% of its power generation. During operation, Francis turbines are subjected to cyclic loading and exposed to river water, leading to crack initiation and propagation. The integrity of these components is assessed through regular inspections, which provide updates on life predictions derived from damage tolerance models based on fatigue crack growth rate (da/dN) as a function of the applied stress intensity factor range (?K). This study investigates for the first time the influence of river water and material’s microstructure on corrosion-fatigue crack growth (CFCG) behavior of 13Cr-4Ni low-carbon martensitic stainless steel. This research helps improve damage tolerance models by accounting for environmental effects. Fatigue crack growth tests were conducted in air and river water at two ?K values of 8 MPa·m0.5 and 15 MPa·m0.5 on steels with minimal (SM) and maximal (SRA) reformed austenite (RA) fractions of 3% and 20%, respectively. To investigate the environment’s influence, a setup was developed where the sample was partially immersed using an enclosure and the synthesized river water was circulated using a pump. The load frequency was varied from 10 Hz to 0.1 Hz and two load ratios (R) of 0.1 and 0.7 were applied. Measurements showed that CFCG rate (CFCGR) was mitigated by crack closure at frequencies of 10 Hz and 1 Hz, resulting in fatigue crack growth rates (FCGR) comparable to those in air. After accounting for crack closure, environmental effects on crack propagation kinetics were revealed. On average, fatigue cracks propagated faster in water for both microstructures. The scatter of data compromises the robustness of this conclusion for SM. In SRA material, significant environmental effect was observed below effective ?K (?Keff) of 9 MPa·m0.5, attributed to longer crack tip exposure times due to slower crack propagation speed (da/dt). A comparison of CFCGRs between SM and SRA revealed that above ?Keff of 6.5 MPa·m0.5, SRA exhibited slower CFCGR due to greater crack closure, extensive crack branching, and RA-to-martensite transformation. Given the negligible environmental impact at high load frequencies, Hydro-Québec can safely use FCGR data from air tests. This experimental work correlating microstructure and crack propagation reveals that higher RA fractions led to slower CFCGR and FCGR. Developing RA-enriched alloys and optimizing hydraulic turbine manufacturing by new developed alloys could improve turbine runner durability. To complement this work, tests under variable R conditions better representing actual turbine loading should be performed.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".