Advanced ultrasonic inspection technologies applied to the \nwelded joints of hydraulic turbine runners
Notice bibliographique
Résumé
Due to the importance of energy production cost, it is critical to reduce unnecessary or unpredicted halts of power generation equipment. Hydro-Québec, as a major power generation company, uses models to estimate the service life of turbine runners to avoid the aforementioned halts. For these models, the characteristics of flaws in the runners are one of the most influential inputs. Since non-destructive testing (NDT) techniques are used to characterize these flaws, it is important to assess the reliability of these methods and to identify methods that could provide better inspection results. The current project aims to provide IREQ with the performance of NDT methods to supply reliable flaw data (both measured and simulated) for their life estimation model. By increasing the accuracy of life estimations, Hydro-Québec will be able to minimize the number of halts and hence reduce the power generation costs. Despite all the previous studies, there is an essential need to extend the knowledge on the detectability of the welding flaws in weld joints of hydroelectric turbine runners. \n \nThis research is part of a program aimed at better understanding the performance of ultrasonic testing technology for the inspection of high-stress areas in Francis runner weld joints. In this research, we will first try to thoroughly study the capability of advanced ultrasonic inspection technologies for hydraulic turbine runners. Inspection of the T-joint mock-up sample was carried out by various NDT methods, namely conventional pulse-echo, phased array, and total focusing method (TFM). With these results, detection rates were obtained in order to compare the effectiveness of each method. In the second step, the reliability of different NDT methods (UT, RT, PAUT, and TFM) in detecting flaws in welded components was investigated using a statistical approach based on the Probability of Detection (POD). The different inspection techniques could thus be compared based on a 90% POD (a90) to determine what is the flaw size that can be reliably detected. The first and second phases deal with the efficiency of ultrasonic inspection as applied to a mock-up sample made of SS415 plates and welded using the same materials and procedure. Finally, the demonstration on a real turbine runner using the highest POD techniques has been experimented. A dual inspection strategy could be implemented after manufacturing or during the in-service inspection with two pass inspections to improve the fitness-for-service assessment of hydraulic turbine runners. The first pass would consist of the use of PAUT with a conventional array while the second pass would be based on the use of TFM. PAUT has shown excellent sensitivity to volumetric and planar defects, while TFM provides more accurate flaw size dimension. This dual inspection strategy aims to increase the reliability of ultrasonic inspection, leading to reduced costs and improved reliability for the hydraulic turbine runner industry. The data collected on the real turbine opened a new opportunity to improve the NDT procedure development. \n \nDemonstrating how TFM has better sizing accuracy is crucial for optimizing the inspection process. This leads to an improvement in the detectability of flaws and improves the process of generating POD curves based largely on TFM examinations as the ground truth method to get the flaw size information. This makes it possible to draw POD curves without using destructive tests which are expensive to perform, and irreparably destroy the specimens. Our work is centered on a real turbine runner using various ultrasonic array configurations to characterize defects. Developing the inspection methodology for hydraulic turbine runners helps us to achieve better sizing measurements of flaws used in the fatigue assessment model. Also, the outcome of this research would be employed to improve the fitness-for-service assessment of hydraulic turbine runners after manufacturing or during inspection operations performed over the useful life of the part.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».