LAUSR

Recent works have established the utility of sparsity-promoting norms for extracting spatially localized instability mechanisms in fluid flows, with possible implications for flow control. However, these prior works have focused on linear dynamics of infinitesimal perturbations about a given baseflow. In this paper, we propose an optimization framework for computing sparse finite-amplitude perturbations that maximize transient growth in nonlinear systems. A variational approach is used to derive the first-order necessary conditions for optimality, which form the basis of our iterative direct-adjoint looping numerical solution algorithm. When applied to a reduced-order model of a sinusoidal shear flow at Re = 20, our framework demonstrates that energy injection into a single vortical mode yields comparable energy amplification to the nonsparse optimal solution, which concentrates 92% of the energy in the same mode. Subsequent analysis of the dynamic response of the flow establishes that these sparse optimal perturbations trigger many of the same nonlinear modal interactions that give rise to transient growth when all modes are perturbed in an optimal manner. It is also observed that as perturbation amplitude is increased, the maximum transient growth is achieved at an earlier time. Our results highlight the power of the proposed optimization framework for revealing sparse perturbation mechanisms for transient growth and instability in fluid flows. We anticipate the approach will be a useful tool in guiding the design of flow control strategies in the future.