Acoustic prediction of flowrate: varying liquid jet stream onto a free surface

TL;DR

This paper explores predicting liquid jet flowrate onto a free surface by analyzing sound, employing machine learning and spectral analysis, and validates the methods against gravimetric measurements, demonstrating promising accuracy for real-world applications.

Contribution

It introduces two novel acoustic-based methods for estimating flowrate and trajectory of a liquid jet onto a free surface, validated against direct measurements.

Findings

Both methods accurately predict flowrate trajectory.

Sound analysis provides a non-intrusive way to estimate flowrate.

Methods show potential for real-life flow monitoring applications.

Abstract

Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.