Aerodynamic optimization and internal flow analysis of axial flow fan with blade deflection based on artificial neural network and multi-objective genetic algorithm

A study on the aerodynamic multi-objective optimization and internal flow analysis of the AXF950 axial flow fan with blade deflection is presented in this paper. A refined numerical model was systematically constructed to investigate the influence of blade deflection on the internal flow characteristics of the fan. The effects of blade geometric variations on flow structures, pressure distribution, and tip leakage vortex intensity were comprehensively examined. For the optimization process, a multi-objective genetic algorithm, combined with an artificial neural network, was effectively employed. Two optimized models, designated as axial flow fan I (efficiency-dominant) and axial flow fan III (pressure-dominant), were subsequently developed. Under rated operating conditions, improvements in static pressure efficiency of 2.3% and 1.84%, respectively, were observed, along with corresponding increases in design point static pressure of 5.9% and 8%. Furthermore, the impact of blade deflection geometrical parameters on fan performance was elucidated across cross-sections S1–S5, revealing how specific geometric adjustments alter the radial pressure gradient and secondary flow development. The optimized designs were demonstrated to reduce flow separation and leakage vortex intensity significantly, while blade tip loading and static pressure were concurrently enhanced through improved aerodynamic matching. This research provides a theoretical foundation and technical support for the design of high-performance axial flow fans by clarifying the underlying mechanisms through which blade deflection modifies internal flow features and overall fan performance.