WELCOME

ModelFLOWs is a research group whose main promoter and tutor is Soledad Le Clainche and was formed at the School of Aeronautics Engineering at Universidad Politécnica de Madrid (UPM). This team uses different data-driven methods, i.e. reduced order models (ROMs) or neural networks (NN), to generate, study and predict databases related to complex flows (turbulent, reactive, etc.).

Numerical Simulations

We compute fluid dynamics simulations on open sources (i.e. Nek5000, OpenFOAM).

Reduced Order Models

We apply reduced order model (i.e. POD, DMD, HODMD) to analyze databases of various fields.

Deep Learning

We employ machine learning and neural networks to reconstruct and predict a wide spectrum of databases.

Latest news

DES of a Slingsby Firefly Aircraft: Unsteady Flow Feature Extraction Using POD and HODMD

A. Corrochano, A.F. Neves, B. Khanal and S. Le Clainche

In this paper, higher-order dynamic mode decomposition (HODMD) was applied to find the main patterns and frequencies of a transient aerodynamic flow field when an aircraft wing experiences stall.