Data-Driven Fluid Mechanics

Learning Outcomes

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Manipulate and analyse large datasets and present key information in digestible ways (M17)
• Study and learn independently, including engaging with technical literature (M4)

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Select appropriate methods to quantify and characterize flow behaviour, discussing principles and limitations of the techniques employed (M2, M3)

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Data-driven methods for flow analysis and control (M1, M3)
• Fundamental concepts of flow stability, dimensionality reduction and control design for fluid flows (M1)

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Apply data-driven methods to large fluid dynamics data sets, utilising suitable computational techniques and tools (M1, M3, M12)

Model Order Reduction

• Modal analysis, dimensionality reduction and coherent structures in turbulent flows
• Manipulation and handling of numerical and experimental data
• Proper Orthogonal Decomposition: derivation, implementation and interpretation
• Fundamental concepts of flow stability
• Linearisation of the equations of motion, eigenspectra and stability modes
• Dynamic Mode Decomposition: derivation, implementation and interpretation

Flow Optimisation and Control

• Feedback control, sensors and actuation
• Cost function, optimal control theory and the Riccati equation
• Estimation and the Kalman filter
• Data assimilation methods for solving inverse problems and identifying unknown parameters
• Data-driven system identification and model-predictive control

Teaching and learning methods

The module features a series of lectures where data handling and computational techniques are introduced and motivated in the context of specific fluid flow phenomena. Case studies are used to illustrate how specific techniques can be utilised to gain fundamental understanding of flow behaviour and characteristics. The lectures are supported by laboratory sessions where practical data manipulation and flow analysis techniques are demonstrated on benchmark problems. Background reading, self-study and peer-to-peer learning will complement your learning.

Resources & Reading list

General Resources

Lecture materials distributed on blackboard..

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External