Application of an Algorithmically Differentiated Turbomachinery Flow Solver to the Optimization of a Fan Stage

Abstract

The adjoint method has already proven its potential to reduce the computational effort for optimizations of turbomachinery components based on flow simulations. However, the transfer of the adjoint-based optimization methods to industrial design problems turns out to pose specific requirements to both the adjoint solver as well as the optimization algorithms which utilize the gradient information. While the construction of the adjoint solver through algorithmic differentiation is described in a parallel publication, we focus here on the robust application of the gradient information in a high-dimensional multi-objective op- timization with several constraints including non-differentiated mechanical constraints. We describe the optimization methods, which comprise the use of gradient-enhanced Kriging meta-models, and subsequently apply these to the design optimization of a contra-rotating fan stage. The results show that through the described combination of methods the adjoint method can be used in practical design optimizations of turbomachinery components.