Most optimal design and control tasks in aerodynamics and aeroacoustics are associated with large design spaces with many hundreds or thousands of design variables, making design sensitivity evaluation and gradient-based optimization a computationally prohibitive task. A key strength of our research group is the development of unsteady discrete adjoint solvers, based on algorithmic differentiation (AD). Specifically, the adjoint formulation allows for the entire design sensitivity vector of the design objective with respect to the design variables to be computed in one-stroke, at the cost of a single unsteady adjoint solution, independent of the number of design variables. The use of reverse-mode AD presents two additional advantages: 1. The gradient vector obtained is machine-accurate and 2. Dual-consistency is ensured by differentiating through the entire fixed-point iterator defining the primal simulation – an adjoint solver is guaranteed to converge if the primal simulation is convergent without any additional stabilization which tend to be dissipative. To date, our group have developed AD-based discrete adjoint solvers for several high-dimensional aerodynamic and aeroacoustic optimization problems: 

A. Noise-minimized rotor blade designs 

B.     Jet noise minimization via nozzle shape optimization 

C. Trailing-edge noise reduction by porous material

D. Leading-edge turbulence interaction noise reduction via shape optimization   

E. Multi-rotor noise reduction via synchrophasing