Relining of 100 Bar Water Power Pressure Pipes with CF/EP-MatrixComposites – A Case Study
Bibliographic record
Abstract
Following the permission for reconstruction by the Austrian federal water authority in 2000, the penstock of one of Austria's largest reservoir hydropower stations, built in the 1940/50ies near Kaprun (Salzburg) and operated by VERBUND Hydro Power GmbH, was rebuilt with modern grade high-pressure steel pipes.However, for economic and technical reasons it was decided to refurbish the final penstock pipe sections just prior to the turbines when entering the power house (i.e. the "turbine distribution penstock"), with a nominal pressure rating of 100 bar, via relining the inner surface of the original steel pipes with a specifically developed carbon fiber/epoxy matrix (CF/EP) composite prepreg.In this manner, a high-strength inner CF/EP composite pipe shell was built as self-supporting construction.Based on an extensive experimental program for material qualification incl.issues related to CF/EP prepreg production, handling and deployment and aspects of structural performance, permission by the water authority to implement the CF/EP relining was obtained in March 2003.The reconstructed power conduit of the Kaprun main scheme was put in operation in June 2004.The paper provides an overview of the material development, qualification and quality assurance work that led to applying this novel and worldwide unique CF/EP relining technique for refurbishing extreme high-pressure pipes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".