@article {2020,
	title = {Data-driven POD-Galerkin reduced order model for turbulent flows},
	journal = {Journal of Computational Physics},
	volume = {416},
	year = {2020},
	pages = {109513},
	abstract = {<p>In this work we present a Reduced Order Model which is specifically designed to deal with turbulent flows in a finite volume setting. The method used to build the reduced order model is based on the idea of merging/combining projection-based techniques with data-driven reduction strategies. In particular, the work presents a mixed strategy that exploits a data-driven reduction method to approximate the eddy viscosity solution manifold and a classical POD-Galerkin projection approach for the velocity and the pressure fields, respectively. The newly proposed reduced order model has been validated on benchmark test cases in both steady and unsteady settings with Reynolds up to $Re=O(10^5)$.</p>},
	doi = {10.1016/j.jcp.2020.109513},
	url = {https://arxiv.org/abs/1907.09909},
	author = {Saddam Hijazi and Giovanni Stabile and Andrea Mola and Gianluigi Rozza}
}
