Shape optimization has always been an engineers' target in order to improve existing performances of specific components. This process can often be time-consuming, requiring substantial manual inputs and multiple design iterations; furthermore it is able to involve at most about ten design variables, limiting the explorable design space. Adjoint-based optimization, instead, has been acknowledged to be effective for applications characterized by a large number of variables. This kind of shape deformation follows an underlying principle, the Adjoint Method, a specialized mathematical tool that expands the range of a CFD solution by providing detailed sensitivity data, i.e. derivatives of the objective function with respect to the design variables.