Optimization of Tandem Blade Based on Improved Particle Swarm Algorithm

Abstract

To improve the design quality of high-turning tandem blade, a coupling optimization system for the shape and relative position of tandem blades was developed based on an improved particle swarm optimization algorithm and NURBS parameterization. First of all, to increase convergence speed and avoid local optima of particle swarm optimization (PSO), an improved particle swarm optimization (IPSO) is formulated based on adaptive selection of particle roles, adaptive control of parameters and population diversity control. Then experiments are carried out using test functions to illustrate the performance of IPSO and to compare IPSO with some PSOs. The comparison indicates IPSO can obtain excellent convergence speed and simultaneously keep the best reliability. In addition, the coupling optimization system is validated by optimizing a large-turning tandem blade. Optimization results illustrate IPSO can obviously increase the optimization speed and reduce the time and cost of optimization. After optimization, at design condition, the total pressure loss coefficient of the optimized blade is decreased by 40.4%, and the static pressure ratio of optimized blade is higher and the total pressure loss coefficient is smaller at all incidence angles. In addition, properly reducing the gap area of tandem blade can effectively reduce the friction loss of the blade boundary layer and the mixing loss created by mixing the gap fluid and the mainstream fluid.