Optimization of tandem blade based on modified particle swarm algorithm

To improve the optimization design quality of tandem blade, an automatic optimization system of tandem blade was developed based on Modified Particle Swarm Optimization Algorithm (MPSO). The mechanism of original PSO was deeply researched and a modified method of PSO was presented. The performance of MPSO was compared with genetic Algorithm and original PSO. The comparison indicates MPSO can obtain better convergence speed and precision. An optimization system is developed based on BP neural network and MPSO. The optimization system is validated by optimizing a tandem blade of 50° blade turning. Both parameters of blade and parameters of relative position of tandem blade are treated as optimization variables. Results indicate that the total pressure loss coefficient of optimized blade decreases by 22% and the static pressure ratio increases by 0.6% at design incidence. The flow performance of optimized blade is improved at negative incidence. Decreasing the radius of leading and trailing edge properly can reduce the loss of tandem blade. Rational structure of the slot can effectively reduce separation loss of boundary layer on pressure surface of front blade and suction surface of rear blade.