Data analysis for hot-film anemometry in turbulent bubbly flow

Phase‐Sensitive Constant Temperature Anemometry

“…We compare the performance of the phase sensitive constant temperature anemometer against a bubble detection algorithm. This method is based on the pattern recognition approach described in reference 14 We record 8 segments of 30 min with 10 kHz sampling frequency at a Re = 20.000. The first four segments were used for training data.…”

“…However, bi‐modality is usually not found in turbulent bubbly flow measurements, which renders thresholding algorithms less reliable. A different approach has been developed by Luther et al14 It assumes that phase interfaces can be identified by detecting characteristic features of the bubble–probe interaction. The pattern recognition problem is solved using an adaptive wavelet dictionary, which is trained with experimental data.…”

Energy spectra in microbubbly turbulence

“…The velocity spectra were deduced from hot-film measurements, employing a neural network type algorithm 17,19,20 to filter out the bubbly spikes 21 from the hot-film signal. Indeed, we obtained an energy spectrum with a slope less than −5 / 3, similar to the numerical simulations.…”

Quantifying microbubble clustering in turbulent flow from single-point measurements

“…Such measurements had earlier been employed to determine the modification of turbulent spectra through bubbles. [22][23][24] For the calculation of the velocity spectra bubbles hitting the probe had first to be identified in the hot-wire signals 25,26 and then filtered out. In the present paper, we employ the very same hot-wire time series to obtain information on the bubble clustering in the turbulent flow.…”