A Torque-Balance Model for Predicting Arch Stability and Flow Blockage

Gas-assisted discharge of granular materials plays a critical role in shaft furnaces, moving-bed reactors, and other industrial multiphase systems, where interstitial gas flow strongly influences arch stability and may induce progressive flow blockage. Existing analytical models generally neglect aerodynamic gas–particle interactions, whereas CFD–DEM simulations provide high predictive accuracy at the expense of substantial computational cost. To bridge this gap, the present study develops and validates a physically based Torque-Balance Model for predicting gas-assisted granular discharge, arch stability, and flow blockage. A comprehensive experimental investigation was performed using a quasi-two-dimensional transparent apparatus and a thermally stabilized shaft model operated under controlled conditions. Gas-assisted discharge was examined for different gas-flow directions, gas properties, outlet geometries, and particulate materials using hydrogen, helium, and air. High-speed imaging together with gravimetric measurements enabled detailed characterization of discharge regimes and arch evolution. The proposed analytical framework explicitly incorporates interparticle mechanical interactions, aerodynamic drag, outlet geometry, and gas-pressure effects within a unified torque-balance formulation. The model describes successive stages of the discharge process, including stable discharge, transition to blockage, and complete flow suppression, while maintaining computational efficiency suitable for engineering calculations. Experimental results demonstrated that gas-flow direction governs arch stability and discharge behavior. Co-current gas flow promoted repeated arch collapse and enhanced discharge, whereas counter-current flow progressively stabilized the granular arch and ultimately produced complete flow blockage. Validation against the complete experimental database demonstrated excellent agreement between theoretical predictions and experimental observations, yielding an average prediction error below 10%, a maximum deviation within ±20%, and a coefficient of determination of R2 = 0.96. The proposed Torque-Balance Model provides a computationally efficient and physically interpretable engineering framework that bridges the gap between simplified empirical correlations and computationally intensive CFD–DEM simulations and can be applied to the prediction and optimization of gas-assisted granular discharge in industrial multiphase systems.