Multipoint Optimization of an Axial Turbine Cascade Using a Hybrid Algorithm

Abstract

This paper presents a constrained multipoint optimization of the LS89 turbine cascade. The objective of the optimization consists in minimizing the entropy losses generated inside the cascade over a predefined operating range. The operating range is bounded by two operating points respectively characterized by a downstream isentropic Mach number of 0.9 and 1.01. During the optimization, two aerodynamic constraints are imposed in order to conserve the same performance as the original cascade. The first constraint is established on the outlet flow angle in order to achieve at least the same flow turning as the LS89 turbine. The second constraint limits the mass-flow passing through the optimized cascade. The optimization is performed using a hybrid algorithm which combines efficiently a classical evolutionary algorithm with a gradient-based method. The hybridization process between both methods is based on the Lamarckian approach which consists in incorporating directly the gradient method inside the loop of the evolutionary algorithm. In this methodology, the evolutionary method allows to globally explore the overall design space while the gradient-based method locally improves certain designs located in the most promising regions of the search space. First, the better performance of the proposed hybrid method compared to the performance of a classical evolutionary algorithm is demonstrated on two benchmark problems. Then, the methodology is applied on a turbomachinery application in order to minimize the losses in the linear LS89 cascade. The optimization process allows to find a new blade profile which reduces the entropy losses over the entire operating range by at least 9.5 %. Finally, the comparison of the flows computed in the baseline and in the optimized cascades demonstrates that the reduction of the losses is due to a decrease of the entropy generated downstream the trailing edges and within the passages between the optimized blades.

A Data-Based Policy Model for Intelligent Turbine Cascade Design

“…Ori_Case3 and Ori_Case4 are from (Behr 2007). The other cases are successively from (Sonoda et al 2006;Kiock et al 1986;Meng et al 2019;Hodson and Dominy 1987;Thulin et al 1982;Chatel et al 2019). These cascades can be divided into two classes roughly.…”

“…In the 1990s, the combination of computational fluid dynamics and an optimization algorithm ("CFD + optimization algorithm") was applied to design turbine cascades. This method gradually became a widely used design method and it also has been continuously developed (Shelton et al 1993;Sonoda et al 2006;Giel 2008;Chatel et al 2019). Although this approach does not make use of much human expert experience, the computational cost is expensive.…”