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Record W2795870030 · doi:10.11159/icsenm18.126

Relining of 100 Bar Water Power Pressure Pipes with CF/EP-MatrixComposites – A Case Study

2018· article· en· W2795870030 on OpenAlexvenueno aff
Reinhold W. Lang, Markus Gall, Erich Wagner, F Hahn

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBar (unit)Composite materialMaterials scienceMatrix (chemical analysis)Geology

Abstract

fetched live from OpenAlex

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.

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)0.000
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 score0.100
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicMechanical stress and fatigue analysisFrench-language works237,207