Aerodynamic performance and flow optimization of axial fan based on the neural network and genetic algorithm

The blades of an axial fan are optimized using artificial neural networks and genetic algorithms in this paper. In first, a parametric axial fan blade model is established with constraints imposed on several parameters. The chord length, maximum camber, maximum camber position, blade thickness, and airfoil stagger angle are considered as an optimization parameter of axial fan. The static pressure efficiency and static pressure of axial fan are regarded as the optimization objectives. An optimization calculation of an axial fan blade is carried out based on the combination of artificial neural network and genetic algorithm. The objective aim of optimization is to improve the static pressure efficiency, the static pressure of axial fan and to reduce the flow loss of axial fan. Numerical results of axial fan demonstrate that the pressure distribution gradient and turbulent kinetic energy contour maps of the optimized axial fan are effectively suppressed within the impeller region compared with that of original axial fan. Furthermore, the internal flow stability of the optimized axial fan also is significantly improved by studying the pressure fluctuation and the Fast Fourier Transform (FFT) of pressure fluctuation. Experimental results of axial fan aerodynamic performance further demonstrate that the static pressure of the optimized axial fan rises as much as 90.93 Pa and the improved static pressure efficiency is effectively improved as much as 7.43% at the design flow rates compared with that of the original axial fan. The application of optimized axial flow fans is of great significance in energy-saving of energy equipment.