Multistage Aerodynamic Optimization Design And Investigation On The Aerodynamic Performance On Aero Engine Axial Turbine

Turbine is an important component of aero engine. The improvements of its performance are of significant meaning to enhance the whole performance level of engine. Aerodynamic design plays a core role in the design of turbine since no high level performance can be achieved without high level aerodynamic design. In past, traditional quasi-three dimensional design (Q3D) methods were widely adopted. However, in recent years, with the incessant maturity of numerical simulation technology, aerodynamic design method based on optimization theory has gradually become prevailing and attracts great interest of researchers in this field of the whole world. However, few studies can be found at present in relation to combining modern optimization design methods and Q3D methods to perform the aerodynamic optimization design of turbine. Based on this point, the main task of this article is to study the design method of combining modern automatic optimization design methods and Q3D methods so as to achieve aerodynamic optimization of the multistage turbine.First of all, through analysis of the advantages and shortcomings of the modern aerodynamic design system of turbine (including modern automatic optimization design methods), the paper brings forward that the aerodynamic optimization design of turbine blade profile can be effectively achieved by organically combining modern automatic optimization design methods and traditional design methods. The reliability of applying commercial software of NUMECA to simulate the internal flow field of multistage turbine at different work conditions is validated by adopting the test data of four-stage axial test turbine.In this paper, an aerodynamic optimization design process of multistage axial turbine is compiled to implement the organic combination of Q3D and automatic optimization design. The design process is composed of Q3D design course and multistage local optimization design course. Q3D methods mainly refer to S2 stream surface direct problem calculation. Applying Q3D to conduct preliminary design can elementarily improve the total performance and fix on the total parameter so as to establish a base for the optimization design in the next step. Then adopting multistage local optimization design further improves the total performance through improving local performance. Local optimization course jointly applies genetic algorithm and artificial neural network. Flow computation applies three-dimensional viscosity Navier–Stokes equation solver. Such local optimization process has three features: (i) local optimization based on aerodynamic performance of every cascade; (ii) several times of optimizations being performed to every cascade; and (iii) alternate use of coarse grid and fine grid to compute flow field. In general, the design process may make full use of these two design methods and complete design in the condition of without huge expenditure of manpower and calculation time. Such process was applied to the aerodynamic optimization design of an air-cooled turbine stage, a three-stage turbine and a four-stage high load low pressure turbine. The total performance of these three turbines is improved in some degree.Through analysis of the typical turbine loss model of recent years, correlative parameters that influence the main factors of all sorts of losses have been found out. In optimization design, the total flow loss can be reduced in a great extent by reasonably adjusting these parameters. On such basis, this paper brings forward the concept of combination optimization parameter group of local optimization. Combination optimization parameter group are composed of blade profile optimization parameter team, meridional channel optimization parameter team, stacking law optimization parameter team and the combination of optimization parameter team. The setting up methods, application condition of every parameter team and how to implement local optimization design by adopting parameter team through flow field diagnosis are presented in this paper. Finally, two difficult problems in optimization design are analyzed in this paper. The two problems are the optimization design of high load low pressure turbine and the optimization design under multiple work conditions. The optimization design of high load low pressure turbine should apply proper fine calculation grid. Furthermore, adjusting the power and the stage load coefficient of every stage and the tangential lift coefficient of every stator and rotor so as to distribute more uniform in every stage is beneficial to improve total performance of turbine. The optimization design under multiple work conditions is multi-objective optimization design. Whether or not applying optimization design under multiple work conditions in turbine design should be decided according to the concrete conditions. The turbine with similar performance under every work condition should adopt the design method of mainly focusing on single work condition while the turbine with very different performance under every work condition should be designed by adopting optimization design under multiple work conditions.