F. López Peña

TL;DR: The methodology presented here makes use of Artificial Neural Networks (ANNs) for enhancing the results obtained by low level fluid dynamic approximations in such a way that an accuracy level comparable to that obtained with more advanced CFD simulations is achieved while maintaining much lower computational costs.

TL;DR: Some results obtained by its application on a towing tank test campaign, aimed at developing a parametric roll detection system, are presented.

TL;DR: A framework based on a mixed reality simulator for coordinating teams of autonomous Unmanned Aerial Vehicles (UAVs) is been developed and a specific implementation of this system focussed on mapping the pollutant dispersion of a plume in the atmosphere is presented.

TL;DR: A delay based artificial neural network is used to analyze turbulent flow signals obtained by a hot wire anemometer along time in different points within the wake of a circular cylinder to extract the signal features concerned with the larger eddies and coherent structures present in the turbulent flow-field.

TL;DR: A simple, fast, and robust aerodynamic simulator is embedded in the design environment to predict the performance of any turbine produced as intermediate individual of the evolutionary process to reduce computations.