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LIFD Training Portal

FutureLearn Fluid Dynamics short course

Fluid Dynamics in Practice: Applications to Real-World Challenges (launches 12th May 2025)

Fluids are everywhere—from the air we breathe to the technology that powers our world. But understanding and predicting their behaviour is a complex challenge with real-world impact. Fluid Dynamics in Practice: Applications to Real-World Challenges is a free 4-week online course produced by the University of Leeds on FutureLearn, we explore the fascinating world of fluid dynamics, uncovering how it shapes industries, protects lives, and drives innovation. Join Dr Duncan Borman, and Professor Catherine Noakes as they break down key principles, case studies, and career opportunities in this essential field.

What you’ll learn: Recognising fluids and identifying laminar and turbulent flows using the Reynolds number. Predicting Fluid behaviour with the Bernoulli Principle. Understanding fluid modelling and computational fluid dynamics Understanding airborne infection and monitoring air quality Modelling and predicting extreme weather patterns.

Audience: This course is ideal for maths, science and engineering students eager to deepen their knowledge of fluid dynamics and discover exciting degrees and industries where this expertise is applied. 

Learning resources for Machine Learning in Earth Sciences

Leeds Institute for Fluid Dynamics (LIFD) has teamed up with the Centre for Environmental Modelling and Computation (CEMAC) team to create Jupyter notebook tutorials on the following topics.

  1. ConvolutionalNeuralNetworks
  2. Physics_Informed_Neural_Networks
  3. GaussianProcesses
  4. RandomForests
  5. GenerativeAdversarialNetworks
  6. AutoEncoders
  7. DimensionalityReduction
  8. XGBoost

These notebooks require very little previous knowledge on a topic and will include links to further reading where necessary. Each notebook will take about two hours to run through and should run out of the box on home installations of Jupyter notebooks.

How to run notebooks and further learning resources on this topic.

LIFD OpenFOAM tutorials

This is a set of tutorials for using OpenFOAM developed by students of the EPSRC Centre for Doctoral Training in Fluid Dynamics at the University of Leeds. 6 different tutorials have been developed covering the following topics:

  1. Introduction to OpenFOAM: This will tell you how to get access to OpenFOAM on University of Leeds Linux machines and give a quick overlook of OpenFOAM.

  2. Cavity: Here you will go over how a case is set up in OpenFOAM by running a simple benchmark problem.

  3. Pitz Daily: This will show how to use turbulence models in OpenFOAM based on the Pitz-Daily backwards facing step problem.

  4. Meshing: This will cover everything you need to know about creating and converting meshes in OpenFOAM, by covering blockMesh and snappyHexMesh, as well as as conversion utilities like fluentMeshToFoam.

  5. Solution Improvement: In this tutorial you will learn how to get better results by choosing appropriate numerical schemes and solution methods.

  6. Customising IcoFOAM: In this tutorial you will learn how to customise a solver to your own needs.

Resources are stored on a gitlab repository

NFFDy Summer Programme Speaker Presentations

  • Jean Christophe Loiseau (Ecole Nationale Supérieure d’Arts et Métiers, Sensors and actuators selection using submodular optimization for control problems, Large-scale bifurcation analysis using a non-intrusive time-stepper approach, General tour of the SINDy framework) Jean-Christophe's presentation slides are available here:
  1. SINDy
  2. Submodular
  3. system_id
  • Simon Parker (DSTL, environmental sampling) presentation slides are available here:
  1. Simon Parker Dynamics for compartmented systems – lecture 1
  2. Simon Parker Dynamics for compartmented systems – lecture 2
  • Claire Heaney (Imperial College London, reduced-order modelling; machine-learning for scientific applications; urban and environmental flows). Claire's presentation slides area available here:
  1. 1. NFFDy-Heaney-forward-modelling
  2. 2. NFFDy-Heaney-reduced-order-modelling
  3. 3. NFFDy-Heaney-inverse-problems