Tobias Kattmann, BOSCH Renningen, Fluid Dynamics (CR/ARF3)
Adjoint-based optimization of unsteady flows has not reached the maturity of its steady counterpart. A main reason is its larger demand of computational power and memory (to store the solution data). To approach the whole topic, a simple unsteady 1D-Diffusion solver together with an unsteady discrete-adjoint was implemented in python. A short outline of this code/results will be given. A possible method to reduce storage requirements for the primal solution data is to perform an (incremental) SVD over the temporal evolution of solution data and storing a low rank approximation. Again, this approach was implemented in python and simple test-cases were carried out to evaluate the method. Further, selected methods from literature for cost reduction of unsteady adjoints (a lot of which from the SU2-community, e.g. model order reduction via Harmonic Balance or temporal/spatial coarsening of the adjoint problem, …) are sketched and a classification is attempted, which is then open to the discussion. Comments, ideas and recommendations are highly appreciated.