Artificial Neural Networks and pattern recognition for air-water flow velocity estimation using a single-tip optical fibre probe

Interest in air-water flows has increased considerably for the last decades, being a common research field for different engineering applications ranging from nuclear engineering to large hydraulic structures or water quality treatments. Investigation of complex air-water flow behavior requires sophisticated instrumentation devices, with additional challenges when compared to single phase instrumentation. In this paper, a single-tip optical fibre probe has been used to record high-frequency samples (over 1 MHz). The main advantage of this instrumentation is that it allows direct computation of a velocity for each detected bubble or droplet, thus providing a detailed velocity time series. Fluid phase detection functions (i.e. the signal transition between two fluid phases) have been related to the interfacial velocities by means of Artificial Neural Networks (ANN). Information from previous measurements of a classical dual-tip conductivity probe (yielding time-averaged velocity data only) and theoretical velocity profiles have been used to train and test ANN. Special attention has been given to the input selection and the ANN dimensions, which allowed obtaining a robust methodology in order to non-linearly post-process the optical fibre signals and thus to estimate interfacial velocities. ANN have been found to be capable to recognize characteristic shapes in the fluid phase function and to provide a similar level of accuracy as classical dual-tip techniques. Finally, performance of the trained ANN has been evaluated by means of different accuracy parameters.