  The metering of gas-liquid flows is difficult due to the non-linear
relationship between flow regimes and fluid properties, flow orientation,
channel geometry, etc. In fact, a majority of commercial multiphase flow meters
have a low accuracy, limited range of operation or require a physical
separation of the phases. We introduce the inference of gas-liquid flowrates
using a neural network model that is trained by wire-mesh sensor (WMS)
experimental data. The WMS is an experimental tool that records high-resolution
high-frequency 3D void fraction distributions in gas-liquid flows. The
experimental database utilized spans over two orders of superficial velocity
magnitude and multiple flow regimes for a vertical small-diameter pipe. Our
findings indicate that a single network can provide accurate and precise
inference with below a 7.5% MAP error across all flow regimes. The best
performing networks have a combination of a 3D-Convolution head, and an LSTM
tail. The finding indicates that the spatiotemporal features observed in
gas-liquid flows can be systematically decomposed and used for inferring
phase-wise flowrate. Our method does not involve any complex pre-processing of
the void fraction matrices, resulting in an evaluation time that is negligible
when contrasted to the input time-span. The efficiency of the model manifests
in a response time two orders of magnitude lower than the current
state-of-the-art.
