Turbomachinery design optimization using automatic differentiated adjoint code

The last decade has established the adjoint method as an effective way in Computational Fluid Dynamics of calculating the gradients of an objective functional in a large dimensional design space. This paper addresses the concerns that code developers face when creating a discrete adjoint computer program for design optimization, starting from a nonlinear flow solver and using Automatic Differentiation. Adjoint code development benefits greatly from using Automatic Differentiation but at its current state of maturity, this technology is best applied selectively rather than on entire codes. The paper discusses the practical aspects of using Automatic Differentiation on a large industrial turbomachinery flow solver with the objective of deriving efficient adjoint code. The use of the adjoint gradients is illustrated in an optimization exercise using gradient based methods on the NASA Rotor 37 public testcase