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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Miettinen, Juha
Tampere University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (7/7 displayed)
- 2020Estimation of Cavitation Pit Distributions by Acoustic Emissioncitations
- 2018Cavitation erosion resistance assessment and comparison of three francis turbine runner materialscitations
- 2018Cavitation Bubble Collapse Monitoring by Acoustic Emission in Laboratory Testingcitations
- 2017Cavitation bubble collapse detection by acoustic emissioncitations
- 2017Cavitation Bubble Collapse Detection by Acoustic Emission
- 2015Wear and corrosion resistant laser coatings for hydraulic piston rodscitations
- 2014Fatigue behavior of laser clad round steel barscitations
Places of action
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article
Estimation of Cavitation Pit Distributions by Acoustic Emission
Abstract
International audience ; Cavitation erosion in hydraulic machinery, such as in turbines and pumps, often leads to significant reduction of the service life of the affected components, with serious consequences for their maintenance costs and operation efficiency. In this study, the potential contribution of acoustic emission (AE) measurements to the assessment of cavitation damage is evaluated from experiments in a cavitation tunnel. Stainless steel samples were exposed to cavitation and damage was characterized from pitting tests carried out on mirror-polished samples. The pits were measured using an optical profilometer and cavitation damage was characterized by pit diameter distribution. In parallel, AE time signal was measured directly from behind the samples. A dedicated signal-processing technique was developed in order to identify each burst in the AE signal and determine its amplitude. The AE amplitude distribution compares well with PVDF and pressure sensor measurements from literature. It is concluded that AE signal analysis can be used to monitor the formation of pits without visual examination of the damaged surface. This provides a basis for possible future applications of nonintrusive cavitation erosion monitoring in hydraulic machines, provided the findings remain true in a more complex environment.