The problem of the automatic design optimization for multistage axial flow turbines is considered and a design strategy based on a 3D Navier-Stokes solver and a RSM (Response Surface Method) approach is described. A multi-objective optimization code based on non-dominated sorting genetic algorithm (NSGA-2) is used to drive the optimization process in order to maximize the specific power while keeping the massflow rate constrained. In the present work the meridional channel is kept unchanged while for each blade the spanwise distribution of the profile restaggering is considered together with the inclusion of compound lean. The performance from the multistage turbine for the optimization loop are obtained from surrogate models built through a set of artificial neural networks. The neural networks are trained and tested using large DoEs and are not updated during the optimization process. This aspect is considered important to guarantee that the optimization converges to an optimum. The use of the 3D flow solver with coarse meshes in order to validate large DoEs in short times is discussed in some details. The above strategy has been applied to a four stage axial turbine from the open literature.
Optimization techniques based on evolutionary strategies have become a general procedure in the industrial and academic worlds for the aero-mechanical design of turbomachinery blades. The airfoil geometry, parameterized using NURBS curve model, has been optimized minimizing the loss at both design and off-design working conditions, guaranteeing also the mechanical requirements. In the present work the authors, using a genetic algorithm (GA)-based tool, have explored the design space of modern axial flow compressor profiles reaching the target of better performance. The outlet flow angle and the mechanical-related quantities have been taken into account as design constraints. A fully integrated software procedure has been developed and applied to the redesign of existing airfoils in two different ways. The first redesign has been performed only at design condition while a further redesign has been implemented taking into account also two reference off-design conditions in order to increase the useful operating range. These reference off-design conditions are automatically obtained by using a loss curve model recently presented by the authors for prescribed velocity distribution airfoils. The computational tool has been applied for the optimization of the UKS-31 stator midspan section showing improved aerodynamic performance for both single and multi-point analysis.
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