Study on Adaptability of Scaling Law to Particles Residence Time Distribution in Bubbling Fluidized Beds

Thermochemical energy storage (TCES) technology is crucial for large-scale renewable energy utilization. The bubbling fluidized bed (BFB) reactor, owing to its high heat and mass transfer efficiency, is commonly used in TCES systems. However, understanding particle flow behavior and residence time distribution (RTD) in BFB reactors remains challenging, especially due to limited research on scaling effects. A cross-flow BFB with continuous feed and discharge is investigated in this study, and a transient numerical model for particle transport is developed. Based on Glicksman’s scaling law, a similarity number related to the particle diffusion coefficient, Ds / Lu, was introduced to ensure that the scaled and prototype fluidized bed models have similar hydrodynamic properties and particle RTD. Additionally, a similarity transformation for particle RTD is established, and the impact of the geometric similarity constant k on flow and RTD similarity is analyzed. The results show that the scaled model can accurately predict the flow and particle RTD behavior of the prototype after applying the similarity transformation, with a maximum error of 9.24%. Based on this scaling law, using a scaled-down model can reduce the computational time to 10% of the prototype’s, while the accuracy remains within 6.05%. Moreover, the scaled model can still predict the prototype RTD under varying particle flow rate, gas velocity, and static bed height, with a maximum error of 10.32%. The proposed scaling law proves highly applicable under different operating conditions and offers an efficient method for predicting particle RTD in large fluidized beds.