Research Group & Sofwtare 

ModelFLOWs

ModelFLOWs is a reserach group that is part of the Department of Applied Mathematics at ETSIAE-UPM.  ModelFLOWs is currently involved in 6 national and EU funded projects, and has also collaborated with universities from all around the world. 

ModelFLOWs' main goal is to develop mathematical models applied to two research lines. The first one seeks to minimize the impact of environmental pollution by improving aircraft energy efficiency, reducing pollution originated by different combustion systems, and developing strategies to reduce air pollution in urban areas. The second aims to advance in personalized medicine and the early detection of cardiovascular diseases.

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Tools Used by ModelFLOWs

See our open source software ModelFLOWs-app here

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. 

ModelFLOWs group members

PhD students

Adrián Corrochano

  • PhD student in Aerospace Engineering
  • Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
  • Research fields: Data Analysis, Machine Learning, Reduced Order Modeling, data-driven tools for patterns detection, turbulent combustion, complex flows
  • International collaborations with: KTH, IMFT, ULB, OIST, Cranfield University and University of Sydney

Eneko Lazpita

  • PhD student in Aerospace Engineering
  • Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
  • Research fields: CFD, Machine Learning, Reduced Order Modeling, Urban Flows, Channel Flow with Heat Transfer, Heart Flow
  • National and International collaborations: KTH, Purdue University, CNIC, Hospital Puerta de Hierro and Harvard University

Rodrigo Abadia-Heredia

  • PhD student in Aerospace Engineering
  • Academic background: BSc in Mathematics & MSc in Industrial Mathematics
  • Research fields: Data Analysis, Machine Learning, forecasting models applied to fluid dynamics problems, building robust algorithms for multiple types of flows
  • International collaborations with: KTH and ULB

Ashton Ian Hetherington

  • PhD student in Aerospace Engineering
  • Academic background: BSc in Mechanical Engineering & MSc in Industrial Engineering
  • Research fields: Machine Learning, Data Analysis, MLOps, Big Data, complex flows, other complex dynamical systems
  • Industrial collaboration with: Boeing Research & Technology Europe

Christian Amor

  • PhD student enrolled at Okinawa Institute of Science and Technology (OIST), Japan
  • Academic background: BSc in Aerospace Engineering & MSc in Industrial Mathematics
  • Research fields: CFD in complex fluids. Non-newtonian jet flows. Data Analysis, Reduced Order Models to speed up CFD simulations.
  • International collaboration with: UPM (PhD thesis co-supervision)

Nourelhouda Groun

  • PhD student in Applied Mathematics
  • Research fields: Machine Learning, Reduced Order Models, fluid dynamics applied to medical imaging
  • National collaboration with: CNIC (National Center of Cardiological Research)

Álvaro Manzano

Guillermo Barragán

Arindam Sengupta

Prajith Pillai

Paul Jeanney

Iñaki Gutiérrez

Postdoctoral investigators

Andrés Bell, PhD

Mahesh Nagargoje, PhD

  • PhD in Chemical Engineering from IIT Guwahati
  • Research fields: CFD, Biofluid Mechanics, Cerebrovascular and heart hemodynamics
  • Research experience: Research Associate at the Department of Neurosurgery, SCTIMST, India
  • Marie Skłodowska-Curie Actions fellow
  • International collaborations: IIT Madras and SCTIMST

International visiting PhD students

Eva Muñoz

  • PhD student in Engineering and Technology Science at Université Libre de Bruxelles (ULB), Belgium.
  • Research fields: Machine Learning, Data Analysis, Reduced Order Modeling on reactive and non-reactive flows for pattern extraction and speeding up combustion

Sofia Tagliaferro

  • PhD student in Applied Health and Life Sciences at the University of Verona (UV), Italy.
  • Research fields: environmental epidemiology, biostatistics, air pollution,  aerobiology, climate change

Other members

Francisco J. García

  • Academic background: BSc in Mechanical Engineering & MSc in Fluid Mechanics
  • Fields of knowledge: numerical simulations using FEM and CFD methods, Machine Learning.
  • Experience: work in international research facilities, governmental institutions as well as in private and state-owned companies 

Juan Sánchez (MSc student)

  • MSc student in Aeronautical Engineering at UPM. 
  • Research fields: CFD simulations, turbulent flows and urban environments. 
  • International collaboration with: TU-Delft

Daniel Serfaty (BSc student)

  • BSc student in Aerospace Engineering at UPM
  • BSc thesis (completed)

National and International collaborations list

Université Libre De Bruxelles (ULB) - Belgium

KTH Royal Institute of Technology (KTH) - Sweden

Technische Universiteit Delft (TU Delft) - Netherlands

Monash University - Australia

Harvard University - US

Boeing Research & Technology Europe (BR&TE) - Spain

Institut de Mécanique des Fluides de Toulouse (IMFT) - France

University of Bristol (UoB) - UK

Cranfield University (CU) - UK

University of Sydney (USYD) - Australia

Okinawa Institute of Science and Technology (OIST) - Japan

Università di Verona (UV) - Italy

Barcelona Supercomputing Center (BSC) - Spain

Centro Nacional de Investigaciones Cardiovasculares (CNIC) - Spain

Hospital Puerta de Hierro - Spain

ARUP - Spain

Microflown Technologies - Netherlands

Universidad Politécnica de Valencia - Spain

BuildWind - Belgium

Air Quality Consultants (AQC) - UK

RWTH Aachen University (RWTH) - Germany

Centre national de la recherche scientifique (CNRS - CORIA) - France

Consiglio Nazionale delle Ricerche (CNR – STEMS) - Italy

Università degli Studi di Napoli Federico II (UNINA) - Italy 

Purdue University (PU) - United States 

For more information please visit the ModelFLOWs official website