Abstract Summary (Max 250 words)
This work introduces an Artificial Intelligence–assisted Radioactive Particle Tracking (RPT-AI) framework for rapid and quantitative characterization of multiphase hydrodynamics in mechanically agitated reactors. The approach combines robotized calibration with deep neural-network reconstruction to directly map detector radiation signals to three-dimensional tracer positions at sub-millimetric accuracy, eliminating the need for model-based inversion or Monte Carlo simulation. Millions of detector–position pairs are collected automatically, allowing the trained network to generalize across geometries and operating conditions without empirical tuning. The resulting flow fields provide velocity distributions and derived metrics including pumping rates, mixing constants, and near-wall velocities within a single-day experimental cycle, at a fraction of the time and cost of CFD or legacy RPT workflows. The framework was applied to stirred tanks while systematically varying impeller type, spacing, and bottom geometry. Analysis at constant power input revealed that multiple optima exist depending on the process objective. Strongest bulk recirculation occurs for dual-Rushton systems with larger inter-impeller spacing or for propellers operating over ellipsoidal bottoms, whereas fastest homogenization is achieved with closely spaced Rushton impellers that reinforce axial exchange. Wall-velocity measurements showed that heat-transfer performance correlates more strongly with near-wall hydrodynamics than with conventional tip-speed surrogates. These results highlight the limits of classical correlations and CFD closures, which reproduce qualitative trends but often underpredict geometry-dependent effects and multi-impeller interactions by 20–30%. By directly measuring local flow fields rather than inferring behavior from surrogate parameters, RPT-AI provides an experiment-based foundation for hydrodynamic scaling and design optimization. The method enables accelerated design–build–test cycles wherein CAD modifications and additive-manufactured prototypes can be evaluated iteratively in real reactors. Beyond characterization, the large high-fidelity datasets produced by RPT-AI create a pathway toward AI-driven hydrodynamic models trained on experimental truth data. These “large process models” may bridge CFD, digital twins, and operations through hybrid, data-validated reactor simulation. RPT-AI therefore establishes a scalable and geometry-agnostic platform for predictive hydrodynamics, supporting faster and more reliable reactor design and scale-up.
