Adrian Silva Caballero 

Project Description

The increasing number of wind turbine projects and the rise of onshore wind farms under forests have prompted exploration of complex atmospheric regions, requiring deeper understanding. Key challenges involve turbulent flows over intricate terrains and their interaction with the atmospheric boundary layer, which affect wind turbine operation, lifespan, and maintenance.

This research proposes conducting Computational Fluid Dynamics (CFD) simulations, in particular Large Eddy Simulations (LES) to predict and understand the underlying aerodynamic and meteorological phenomena. Emphasis will be placed to develop modelling techniques to capture the most relevant physics to numerically reproduce wind flows aiming at reducing the number of CFD runs, where machine learning, statistical analysis and LES hold the potential for dimensionality reduction, and consequently the heavy computational costs associated with such simulations.

Supervisors

Antonio Segalini

Stefan Ivanell

Alexander Radi

Hugo Olivares Espinosa

Background

I was brought up in the Valley of Mexico. In Mexico, I graduated as a Renewable Energy Engineer by The National University of Mexico. My bachelor’s thesis was focused in studying different nano-electrodes topologies for energy storage applications. After completing my bachelor thesis, I decided to pursue a Joint Master’s degree in Renewable Energy in the Marine Environment by UCC, UPV/EHU, and ECN. During the master’s I spent most of my time in France. In Le Havre, my master’s thesis took place, which focused in enhancing processes of an offshore wind factory through digitalization.

Outside my academic life I enjoy outdoor activities, like climbing, hiking and cycling. Additionally, I take pleasure in travelling, cooking, reading, and dancing.

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The urgency of addressing climate change and the pivotal role of renewable energy in achieving sustainable goals resonate deeply with my professional and personal aspirations, which made this opportunity particularly compelling to pursue. Furthermore, deepening my understanding and knowledge in fluid dynamics, turbulence and numerical methods using advanced computational fluid dynamic tools and machine learning towards industrial needs is genuinely fulfilling.