Acoustic emission is a promising method in monitoring cavitation erosion in hydraulic machines during operation

Cavitation erosion is a significant factor in hydraulic machine wear and failures. It is challenging to estimate its severity and occurrence during machine operation. Acoustic emission may be used as a non-intrusive monitoring method, if the data it provides is properly interpreted.

Cavitation is the formation of vapor bubbles either in a static liquid or in a liquid flow due to a drop in static pressure. When these bubbles collapse, as a result of pressure recovery, they may damage adjacent surfaces. These events are major causes of damage and nuisance in hydro machines. Modern hydro turbines are often used to regulate power grids; therefore, they may be operated out of their designed range. The flow-related optimal operation is different from the economic optimal usage.

Detecting and characterizing cavitation and assessing damage during operation can be difficult or even impossible. Acoustic emission (AE) measurements provide a way to measure cavitation without access to the flow, but interpreting the data is challenging. This thesis presents insights in the ways of treating the AE data both in characterizing individual pits created by cavitation impacts and in tracking the evolution of cavitation erosion. Additionally, the erosion rates of three turbine materials were compared, and the main reasons behind the differing erosion rates of two martensitic turbine steels were discovered. The same high-speed cavitation tunnel was used in all cavitation experiments.

This thesis firstly presents a method for enveloping an AE waveform signal and for counting the peak voltage values. The resulting cumulative distributions were compared to those of cavitation pit diameters, and from this comparison, a connection was proposed between AE peak voltage value and pit diameter. The second result was the connection between cavitation cloud shedding frequency and erosion evolution. The process of demodulating high frequency AE signals effectively promotes the low frequency shedding. The shedding frequency increased with accumulating material loss, and it was concluded that this increase is due to geometry effects, namely surface roughness. In addition to the two proposed methods, it was found that the decisive factors in the differing erosion rates of the martensitic stainless steels are the prior austenite grain size, packet and block sizes and the retained austenite fraction.

This thesis provides guidelines directly applicable, such as the martensitic steel classifying, and methods that require further development, if one wishes to utilize them in hydro machine cavitation monitoring instead of laboratory measurements in a cavitation tunnel. The main outcome is that AE is a potential way to monitor cavitation, with the important benefit of not requiring any access to the flow.

The doctoral dissertation of Markku Ylönen, titled Cavitation Erosion Monitoring by Acoustic Emission was publicly examined at the Tampere University Faculty of Engineering and Natural Sciences on Friday 17th of January 2020. The opponent was Professor Romuald Skoda, Ruhr-Universität Bochum. Professor Kari Koskinen, Faculty of Engineering and Natural Sciences acted as the Custos.

The dissertation is available at