Cold spray repair of cavitation damages on hydropower components: impact of the deposition process and cavitation resistance benchmarking
Bibliographic record
Abstract
With the emergence of sustainable manufacturing concept, repair of near end-of-life structural components has become increasingly relevant or even critical in many industrial sectors. As such, there is a strong interest for fast and cost-effective solutions to extend the lifetime of large metallic parts. In the case of hydropower components made of steel, cold spray represents a promising solution for the repair of cavitation damages. The capability to rebuild metallic surfaces without affecting the base material gives cold spray a clear edge over the current welding repair techniques, which could lead to significant cost savings. In this presentation, we will disclose the results of our investigation on the properties and performance of cold sprayed 1025 carbon steel, 316 & 309 austenitic stainless steels, and Cavitec alloy (TRIP-type alloy specifically developed for protection against cavitation damages) deposited with nitrogen. A thorough analysis of powder characteristics, cold sprayability of these materials, deposit microstructure, XRD phase analysis and cavitation resistance will also be presented, and correlations between cavitation results and deposit properties will be discussed. Finally, cavitation resistance of the cold spray deposits, with and without heat treatment, will be benchmarked versus bulk materials.
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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 | 0.001 |
| 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 | 0.001 |
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".