A Synthetic-to-Real Deep Learning Framework for Two-Phase Probe Signal Processing

Accurate binarization of phase-detection probe signals (gas vs. liquid) is necessary for the estimation of local void fraction, interfacial velocity, and bubble statistics in gas–liquid flows, particularly in nuclear thermal–hydraulic experiments. Classical threshold-based methods—single or double level—perform well on clean laboratory signals but degrade under realistic industrial conditions where noise, baseline drift, and clustered (slug-like) events challenge fixed rules. This work investigates whether deep learning (DL) models trained exclusively on synthetic data can deliver robust, generalizable binarization on real probe measurements. We (i) build a parametric generator of realistic time series from bubbly pulse templates, extended to clusters/slug patterns and perturbed with controlled noise, drift, and oscillatory baselines; (ii) train four lightweight DL architectures—one-dimensional U-Net (UNET-1D), Temporal Convolutional Network (TCN), a minimal one-dimensional Convolutional Neural Network (CNN-1D), and a Bidirectional Long-Short Memory network (BiLSTM)—only on synthetic signals; and (iii) evaluate them against classical threshold methods using event-level and sample-level metrics. On synthetic signal evaluation, UNET-1D and TCN achieve near-perfect event detection and sub-millisecond onset errors. On real bubbly and slug flow sensor data, classical threshold-based methods remain highly competitive on clean sensor signals, while DL models retain advantages under non-stationary baselines and clustered events, yielding accurate void and timing with no hand-tuned assumptions. Results support DL as a practical, data-driven complement to fixed algorithms, particularly in noisy or drift-dominated measuring conditions typical of nuclear thermal–hydraulic loops and safety-relevant test facilities.