Shape Optimization of Cut-Off in a Multi-blade Fan/Scroll System Using Neural Network

In order to improve efficiency of a system with three-dimensional flow characteristics, this paper presents a new method that overcomes three-dimensional effects by using two-dimensional CFD and neural network. The method was applied to shape optimization of cut-off in a multi-blade fan/scroll system. As the entrance conditions of two-dimensional CFD, the experimental values at the positions out of the inactive zone were used. The distributions of velocity and pressure obtained by two-dimensional CFD were compared with those of three-dimensional CFD and experimental results. It was found that the distributions of velocity and pressure have qualitative similarity. The results of two-dimensional CFD were used for teaming as target values of neural network. The optimal angle and radius of cut-off were determined as 71and 0.092 times the outer diameter of impeller, respectively. It is quantified in the previous report that the optimal angle and radius of cut-off are approximately 72and 0.08 times the outer diameter of impeller, respectively.