Optimization and Scaled Validation Research on Impellers for Aerated Concrete Mixing

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Abstract
To address the issues of non-uniform slurry mixing, high energy consumption, and low efficiency during the aerated concrete pouring and mixing process, this study investigates a single-layer cross-impeller mixer to perform impeller structure optimization and scaled validation. A multiphase numerical mixing model for aerated concrete was established using the computational fluid dynamics (CFD) method coupled with the Volume of Fluid and Discrete Phase Model (VOF-DPM) approach. A comprehensive evaluation system factoring in mixing uniformity, time, and energy consumption was constructed, wherein the mixing uniformity was evaluated using a modified Lacey index. Furthermore, a scaled model was established based on the Froude and Reynolds dual-similarity criteria. The validation results demonstrate high consistency in mixing characteristics between the scaled and original models, with errors of approximately 1% for mixing time and 4.8% for power. An orthogonal experimental design was applied to construct a sample database for establishing a Backpropagation (BP) neural network prediction model, which was then coupled with a Genetic Algorithm (BP-GA) to perform multi-objective optimization for the blade angle, dimensions, off-bottom clearance, and rotational speed. The optimal parameter combination is determined as a blade angle of 66.4°, length of 274 mm, width of 74 mm, off-bottom clearance of 271 mm, and a rotational speed of 644 rpm. CFD validation indicates that the relative errors for mixing power, time, and the coefficient of variation (CV) are all below 5%. This research provides a theoretical foundation and technical reference for the structural design and process optimization of aerated concrete mixing equipment. By integrating multiphase CFD computing with intelligent optimization, this work advances the paradigm of computational modeling and precise performance evaluation in high-viscosity mixing processes, thereby driving technological innovations in industrial automation and intelligent equipment design for future manufacturing.