Module overview
The module will introduce contemporary computational methods for fluid flow analysis, with a specific focus on techniques that use simulation or experimental data. The module will cover aspects of flow stability and transition, model order reduction and pattern identification and applications of control theory to manipulate flow behaviour. Through a blend of lectures and hands-on laboratory sessions, the module will provide students with the practical knowledge required to implement and apply these methods, together with a solid understanding of fundamental fluid mechanics and mathematical concepts underpinning their use.
Aims and Objectives
Learning Outcomes
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)
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)
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)
Syllabus
Flow stability and transition
- Fundamental concepts of flow stability and transition
- Linearisation of the equations of motion
- Eigenspectra and stability modes
- Manipulation and handling of CFD and experimental data
- The Dynamic Mode Decomposition: derivation, implementation and interpretation
Model order reduction
- Coherent structures in turbulent flows
- Modal analysis and dimensionality reduction
- Galerkin projection, vector spaces and inner products
- The Proper Orthogonal Decomposition: derivation, implementation and interpretation
Flow control
- Feedback control, sensor placement and actuation
- Optimal control theory and the Riccati equation
- Elements of observability and controllability for linear systems
Learning and Teaching
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.
| Type | Hours |
|---|---|
| Preparation for scheduled sessions | 12 |
| Practical classes and workshops | 8 |
| Lecture | 28 |
| Completion of assessment task | 36 |
| Independent Study | 66 |
| Total study time | 150 |
Resources & Reading list
General Resources
Lecture materials distributed on blackboard..
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
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.
| Method | Percentage contribution |
|---|---|
| Set Task | 100% |
Repeat Information
Repeat type: Internal & External