Reduced-Order Modeling of Complex Fluid Flows

In many scientific and engineering applications of complex fluid flows such as the flow control and optimization problem, computational efficiency is of paramount importance. Thus, model reduction techniques are frequently used. To achieve a balance between the low computational cost required by a reduced-order model and the complexity of the target turbulent flows, appropriate closure modeling strategies need to be employed. In this talk, we present reduced-order modeling strategies synthesizing ideas originating from proper orthogonal decomposition and large eddy simulation, develop rigorous error estimates and design efficient algorithms for the new reduced-order models.