Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

Closed-loop turbulence control is a critical enabler of aerodynamic drag reduction, lift
increase, mixing enhancement, and noise reduction. Current and future applications have …

This book is an introduction to machine learning control (MLC), a surprisingly simple model-
free methodology to tame complex nonlinear systems. These systems are assumed to be …

The description of coherent features of fluid flow is essential to our understanding of fluid-
dynamical and transport processes. A method is introduced that is able to extract dynamic …

Modeling realistic fluid and plasma flows is computationally intensive, motivating the use of
reduced-order models for a variety of scientific and engineering tasks. However, it is …

The cornerstone of finite‐time thermodynamics is all about the price of haste and how to
minimize it. Reversible processes may be ultimately efficient, but they are unrealistically …

Data-driven methods have become an essential part of the methodological portfolio of fluid
dynamicists, motivating students and practitioners to gather practical knowledge from a …

This paper puts forth two new closure models for the proper orthogonal decomposition
reduced-order modeling of structurally dominated turbulent flows: the dynamic subgrid-scale …

The Koopman theory, a concept to globally model nonlinear signals by a linear Hamiltonian,
has been at the frontier of fluid mechanics research for the last decade. Wind engineering …

We propose a data-driven filtered reduced order model (DDF-ROM) framework for the
numerical simulation of fluid flows. The novel DDF-ROM framework consists of two steps:(i) …