Modal decomposition techniques are showing a fast growth in popularity for their wide range
of applications and their various properties, especially as data-driven tools. There are many …

Solving computational fluid dynamics problems requires using large computational
resources. The computational time and memory requirements to solve realistic problems …

This article presents a review on two methods based on dynamic mode decomposition and
its multiple applications, focusing on higher order dynamic mode decomposition (which …

Fluid dynamics problems are characterized by being multidimensional and nonlinear.
Therefore, experiments and numerical simulations are complex and time-consuming …

This article presents a new method to identify the main patterns describing the flow motion in
complex flows. The algorithm is an extension of the higher-order dynamic mode …

In this numerical and theoretical work, we study the turbulent channel flow of Newtonian and
elastoviscoplastic fluids. The coherent structures in these flows are identified by means of …

We develop a reduced order model to represent the complex flow behaviour around vertical
axis wind turbines. First, we simulate vertical axis turbines using an accurate high order …

Understanding flow structures in urban areas is widely recognized as a challenging concern
due to its effect on urban development, air quality, and pollutant dispersion. In this study …

Spatio-temporal dynamic mode decomposition (STDMD) is an extension of dynamic mode
decomposition (DMD) designed to handle spatio-temporal datasets. It extends the …

This article presents three different low-order models to predict the main flow patterns in
synthetic jets. The first model provides a simple theoretical approach based on experimental …