Developments Of A Discrete Adjoint Structual Solver For Shape And Composite Material Optimization

In the field of turbomachinery, a multidisciplinary optimization may seek to optimize an objective in the discipline of fluid dynamics, e.g. maximizing efficiency, while satisfying constraints in the discipline of structural mechanics, e.g.keeping the maximum von Mises stress beneath a defined threshhold. This enables the design of components that are both aerodynamically optimized and structurally feasible. In order to respect structural constraints, gradient-based optimization methods require the sensitivities of the structural objectives with respect to the design variables. A discrete adjoint structural solver, based on the finite element method (FEM), and differentiated using adjoint algorithmic differentiation (AD), plays a key role in computing the structural sensitivities efficiently. The gradients can be computed at a cost of a single adjoint run for each objective function, independent on the size of the design space. Not only does this give us the opportunity to include a large number of design parameters, e.g. a large number of control points for a CAD-based shape paremetrization, but even to go beyond the usual shape design parameters and include composite material design parameters [1], [2] as well. This paves the way for an efficient simultaneous shape and material optimization using adjoint AD. In this work, we introduce the ongoing developments of a discrete adjoint structural solver that has the capabilities of computing sensitivities with respect to shape design parameters, as well as composite material design parameters, with a single adjoint run for each structural objective function. The solver is written in C++ and differentiated using the AD tool CoDiPack [3]. The theory behind this concept is discussed first, followed by preliminary results and a conclusion.