摘要
This paper presents an optimization of a high pressure turbine by constructing non-axisymmetric endwalls to the stator row and the rotor hub.The optimization was quantified by using optimization algorithms based on the multi-objective function.The objective was to increase total-to-total efficiency with the constraint on the mass flow rate equal to the design point value.In order to ensure that global optimum could be achieved,the function of parameters was first approximated through the artificial neural network,and then optimum was achieved by implementing the genetic algorithm.It was adopted through the design and optimization environment of FineTM/Design3 D.Three individual treatments of the endwalls were presented.Firstly,the hub and the shroud of the stator were optimized together.Secondly,the hub of the rotor was optimized.Thirdly,the rotor hub was optimized in the presence of the optimized stator.The result of the investigation showed that the optimized shape of the endwalls can significantly help to increase the efficiency up to 0.18%with the help of a reduction of the transverse pressure gradient.The coefficient of secondary kinetic energy,entropy coefficient,spanwise mass averaged entropy were reduced.In order to investigate the periodic effects,the design of the optimized turbine under steady simulations was confirmed through unsteady simulations.The last part of the investigation made sure that the performance improvement remained consistent over the full operating line at off-design conditions by the implementation of non-axisymmetric endwalls.
This paper presents an optimization of a high pressure turbine by constructing non-axisymmetric endwalls to the stator row and the rotor hub. The optimization was quantified by using optimization algorithms based on the multi-objective function. The objective was to increase total-to-total efficiency with the constraint on the mass flow rate equal to the design point value. In order to ensure that global optimum could be achieved, the function of parameters was first approximated through the artificial neural network, and then optimum was achieved by implementing the genetic algorithm. It was adopted through the design and optimization environment of FineTM/Design3 D. Three individual treatments of the endwalls were presented. Firstly, the hub and the shroud of the stator were optimized together. Secondly, the hub of the rotor was optimized. Thirdly, the rotor hub was optimized in the presence of the optimized stator. The result of the investigation showed that the optimized shape of the endwalls can significantly help to increase the efficiency up to 0.18% with the help of a reduction of the transverse pressure gradient. The coefficient of secondary kinetic energy, entropy coefficient, spanwise mass averaged entropy were reduced. In order to investigate the periodic effects, the design of the optimized turbine under steady simulations was confirmed through unsteady simulations. The last part of the investigation made sure that the performance improvement remained consistent over the full operating line at off-design conditions by the implementation of non-axisymmetric endwalls.
