Author(s): Timon Krimm; Grigorios Hatzissawidis; Gerhard Ludwig; Maximilian Kuhr; Peter Pelz

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Abstract: Two hydrofoils with different obstacle configurations were examined in the cavitation tunnel at Technische Universit¨at Darmstadt to quantify the influence of the obstacles on cavitation dynamics. Both the cavitation number and the incidence were varied. High-speed imaging and high-frequency pressure measurements were conducted to identify characteristic time scales (shedding frequency) and length scales (cavity sheet length). The power spectral density (PSD) of the pressure data was estimated to isolate the characteristic shedding frequencies of cloud cavitation. The examination of the cavitation topology and spectra enables the separation between (i) condensation shockwave and (ii) re-entrant flow as dominant cloud shedding mechanism. The influence of the obstacles is primarily visible in a reduction of shedding frequencies correlated to the re-entrant flow. In addition, there is an effect on the frequencies associated with shockwave-driven cloud cavitation, which could be related to the hindrance of the re-entrant flow. The cavity sheet was automatically detected using a convolutional neural network (CNN). Two methods were applied to obtain the average and maximum cavity sheet length. As expected, the extent of the cavity sheet increases as the cavitation number is reduced. The influence of the obstacles on the cavity sheet length is only apparent if an obstacle is located close to the leading edge.

DOI: https://doi.org/10.1088/1755-1315/1561/1/012009

Year: 2025