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The course is designed to reflect the wide applications of Computational Fluid Dynamics. There is an increasing demand for CFD specialists with practical and technical knowledge. This course will enable you to gain the knowledge and appreciation necessary for a strong foundation in a career in this exciting engineering discipline. You will learn to understand, write and apply CFD methods across a broad range of fields, from aerospace, turbomachinery, multi-phase flow and heat transfer, to microflows, environmental flows and fluid-structure interaction problems. Personalise your course by choosing from a range of specialist modules covering application-specific methods and techniques.


  • Start dateOctober
  • DurationFull-time MSc - one year, Part-time MSc - up to three years
  • DeliveryTaught modules 40%, Individual research project 40%, Group project 20%
  • QualificationMSc
  • Study typeFull-time / Part-time
  • CampusCranfield campus

Who is it for?

Designed to meet the education needs of graduates and professional engineers who are looking to kick-start an industrial or research career in the rapidly growing field of Computational Fluid Dynamics. This course bridges the gap between the introductory level of undergraduate courses and the applied expertise acquired by engineers using CFD in industry. You will gain the knowledge and appreciation of CFD methods necessary for a strong foundation in a career in this exciting engineering discipline.

Why this course?

The MSc in Computational Fluid Dynamics provides a solid background so that you will be able to apply CFD methods as a tool for design, analysis and engineering applications. With a strong emphasis on understanding and application of the underlying methods, enthusiastic students will be able to write their own CFD codes during the course.

Sharing modules with several MSc programmes within the school, this enables you to interact with students from other disciplines. In recent years, our students have been had the opportunity for work-based placements at the Aircraft Research Association (ARA), AIRBUS, European Space Agency (ESA), Ricardo and DAF Trucks.

Informed by Industry

Our strategic links with industry ensure that all of the materials taught on the course are relevant, cutting edge and meet the needs of organisations competing within the computational analysis sector. This industry led education makes Cranfield graduates some of the most desirable for companies to recruit.

The Industrial Advisory Panel is comprised of senior industry professionals provides input into the curriculum in order to improve the employment prospects of our graduates. Panel members include:

  • Adrian Gaylard, Jaguar Land Rover
  • Stephen Rolston, AIRBUS
  • Trevor Birch, Defence, Science and Technology Laboratory
  • Evgeniy Shapiro, Ricardo
  • Nick Leppard, BAE Systems
  • Peter Hall, MBDA
  • Richard Ashworth, EADS
  • Richard Pattenden, Qinetiq
  • Stephen Hughes, AWE
  • Andrea Ciarella, Aircraft Research Association (ARA)
  • Matthew Sorrell, Red Bull Racing Formula 1
  • Marco Hahn, Red Bull Racing Formula 1.
Carolina Olmedo Egea promo

All the things I learned on the course were very useful for my career. The pace of assignments and workload was high but it really helped me to face these issues in the real world. The assignments were challenging but they really make they make you think which really helps you to see the bigger picture. 

Carolina Olmedo Egea, Project Management Developing Engineer


The MSc in Computational Fluid Dynamics will meet, in part, the exemplifying academic benchmark requirements for registration as a Chartered Engineer. Accredited MSc graduates who also have a BEng (Hons) accredited for CEng will be able to show that they have satisfied the educational base for CEng registration.

Course details

The taught modules are delivered from October to April via a combination of structured lectures, and computer based labs.

The core part of the course consists of modules which are considered to represent the necessary foundation of the subject material. The course is designed to reflect the broad range of CFD applications by providing a selection of a group project themes in the field of aerospace, automotive or energy, with direct industrial applications. Students on the part-time programme will complete all of the compulsory modules based on a flexible schedule that will be agreed with the course director.

Group project

The group project is fundamental for this course as it creates a virtual consultancy environment by bringing together students from various backgrounds to solve an industrial problem. Each group of students will work on a different thematic project, related to a fluid problem encounter in industry. There are three themes: Aerospace, Automotive and Energy.

The group projects are informed by our industrial partners, supported with guest industrial lectures, technical presentation, specific software demonstration and computer lab tutorials. Each member of the team will be responsible for a specific analysis covering all stages of CFD workflow, pre-processing, solving and post-processing. The team will require to organise meetings, manage resources, manage task dependence, report on the computations and conduct comprehensive analysis.

Problem solving and project co-ordination must be undertaken on a team and individual basis. Students will also develop interpersonal skills necessary to embark on their future careers as engineering and technology leaders.

At the end of the project, the group is required to report and present findings to a panel from industry and academia.

Individual project

The modular and group project components of the course finish in May, at which point you will have an excellent understanding of CFD methods and applications. From May to September you will work full-time on your individual research project. The research project gives you the opportunity to produce a detailed piece of work either in close collaboration with industry, or on a particular topic which you are passionate about.

Recent Individual Research Projects include:

• CFD analysis of a sanitary micro-combustor.
• RANS modelling of turbulent drag reduction on aircraft.
• Rarefied CFD simulation by DSMC model of satellite at Low Earth Orbit
• CFD study of aircraft carrier ship air wake interaction with helicopter under extreme conditions.
• Hybrid DNS/LES of a flame kernel configuration in premixed turbulent combustion
• Fluid structure interaction of a deformable wheel-tyre configuration by an overset method.
• Aerodynamics analysis of the DrivAer under realistic highway driving conditions.
• RANS modelling by Deep Learning for aerodynamics applications.
• Multi-objective optimisation for unmanned aerial vehicle formation flight by multi-fidelity approach.
• Aerodynamic analysis and optimisation of the Aegis unmanned aerial vehicle.
• Aerodynamic analysis and noise prediction of rudimentary landing gear.
• Phase separation of oil-water flow in a pipe bend
• CFD simulation of a novel CO Sensor
• Shock wave interaction with biological membranes for drug therapy.
• High resolution simulation of Ariane 5.


Taught modules 40%, Individual research project 40%, Group project 20%


Keeping our courses up-to-date and current requires constant innovation and change. The modules we offer reflect the needs of business and industry and the research interests of our staff and, as a result, may change or be withdrawn due to research developments, legislation changes or for a variety of other reasons. Changes may also be designed to improve the student learning experience or to respond to feedback from students, external examiners, accreditation bodies and industrial advisory panels.

To give you a taster, we have listed the compulsory modules and (where applicable) some elective modules affiliated with this programme which ran in the academic year 2018–2019. There is no guarantee that these modules will run for 2019 entry. All modules are subject to change depending on your year of entry.

Compulsory modules
All the modules in the following list need to be taken as part of this course

Introduction to Fluid Mechanics and Heat Transfer


    To introduce the foundations of fluid mechanics, various formulations of governing equations and their mathematical properties in order to establish a firm basis for other modules.

    • Introduction to thermodynamics of gases and liquids
    • Introduction to heat transfer
    • Compressible flows
    • Incompressible flows
    • Dimensional analysis and similarity parameters
    • Mathematics of governing equations, classification of PDEs
    • Model equations for fluid dynamics
    • Introduction to unstable and turbulent flows.
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Distinguish and analyse the governing equations of fluid mechanics and heat transfer in various formulations for compressible and incompressible viscous and inviscid flows.
2. Estimate the impact of different physical phenomena based on dimensional analysis.
3. Examine mathematical properties of governing equations and be able to critically evaluate correct boundary/initial value problems for various flows.
4. Set up and analyse the systematic application of the model equations and problems used in CFD.
5. Distinguish and compare concepts of stability and turbulence.

Numerical Methods and High Performance Computing


    To introduce the basics of numerical analysis and numerical methods for partial differential and algebraic equations, relevant to Computational Fluid Dynamics, and how to efficiently employ the latest technologies of high performance computing (HPC) for numerically solving these equations.

    • Introduction to numerical analysis
    • Discretisation approaches: finite difference, finite volume, finite element and spectral methods
    • Numerical methods for algebraic equations/systems of equations
    • Numerical schemes for hyperbolic, parabolic and elliptic systems and for fluid dynamics.
    • Desktop versus distributed computing facilities
    • Hardware and software aspects of HPC
    • Parallel computing challenges and main issues
    • Parallelisation approaches for distributed and shared memory systems. MPI & OPENMP
    • Current CFD process with respect to partitioning and distributed computing and related bottlenecks
    • Whole HPC product applications
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Assess the principles of numerical analysis and concepts of stability, approximation and convergence.
2. Evaluate finite difference/volume/element schemes on model problems of computational fluid dynamics.
3. Construct program-code to obtain numerical solutions of algebraic equations and systems of equations.
4. Distinguish the hardware of high performance computing platforms for computational fluid dynamics simulations.
5. Asses essential software extensions i.e. MPI, open-MP, for distributed computing.
6. Evaluate the benefits of high performance computing for computational fluid dynamics applications, in terms of speed-up and efficiency.

Numerical Modelling for Incompressible Flows

Module Leader
  • Dr Laszlo Konozsy

    To understand the state-of-the-art CFD methods used for computing incompressible flows in science and engineering.

    • Overview of various formulations of the governing equations and numerical methods for incompressible flows (linear & high-resolution methods)
    • Solution approaches: pressure Poisson, projection (approximate and exact), artificial compressibility
    • Centred schemes
    • TVD and Riemann solvers for incompressible methods
    • Second and high-order methods (time and spatial discretise)
Intended learning outcomes On successful completion of this module a student should be able to:
1. Set up spatial and time discretisation methods for solving fluid mechanics problems governed by the incompressible Navier-Stokes/Euler equations.
2. Analyse the applicability of mathematical methods for incompressible flows along with the classification and properties of different state-of-the-art CFD incompressible methods as used in engineering practice as well as in research and development.
3. Assess uncertainties and limitations associated with each method.

Numerical Modelling for Compressible Flows

Module Leader
  • Dr Panagiotis Tsoutsanis

    To introduce basic concepts in the discretisation and numerical solution of the hyperbolic systems of partial differential equations describing the flow of compressible fluids.

    • Mathematical properties of hyperbolic systems
    • Conservation Laws
    • Non-linearities and shock formation
    • WENO schemes
    • MUSCL schemes Introduction to the Riemann problem
    • Lax-Wendroff scheme
    • Introduction to Godunov's method
    • Flux vector splitting methods
    • Approximate Riemann solvers
    • Explicit and implicit time-stepping schemes
Intended learning outcomes On successful completion of this module a student should be able to:
1. Demonstrate a critical awareness of the mathematical properties of hyperbolic partial differential equations;
2. Recognise the importance of non-linearities in the formation of shock waves;
3. Evaluate the limitations of finite difference methods for hyperbolic systems of partial differential equations;
4. Distinguish the fundamental differences between monotone schemes, WENO schemes for hyperbolic systems;
5. Judge the suitability of various Riemann solvers for various compressible flow problems;
6. Create high-resolution shock capturing schemes for compressible flow problems;
7. Evaluate the influence of various approximate Riemann solvers for 1D and 2D compressible flow problems.

Turbulence Modelling


    To introduce students to closure methods for the Navier-Stokes equations as applied to turbulent and transitional flows, and the classical physical modelling approximations required to achieve this. To introduce the advanced turbulence modelling approaches used in Computational Fluid Dynamics such as Large Eddy Simulations and Direct Numerical Simulations.

    • Introduction to Reynolds Averaged Navier Stokes Modelling
    • Zero, One and Two equation models
    • Reynolds Stress Transport Schemes
    • Low-Re Modelling
    • Transition Modelling Extensions
    • Best Practice Guidelines
    • Overview of the basic equations used in LES, including filtered and unfiltered formulations
    • Classical LES and sub-grid scale models
    • Implicit LES (numerical and physical principles)
    • Numerical and physical properties of DNS
    • Applications and challenges for LES and DNS
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Demonstrate a critical awareness of the basic principles of modelling approximations required to close RANS equations;
2. Differentiate between different levels of closure and their limitations;
3. Appraise and Evaluate how modelling methods may be extended to transition prediction;
4. Critically compare between the different types of model available in current codes;
5. Demonstrate a critical awareness of the numerical and physical principles of LES and DNS in the simulation of transitional and turbulent flow simulations;
6. Critically evaluate the challenges in the implementation of LES and DNS in science and engineering.

Grid Generation / CAD


    To introduce the concepts of grid generation, including structured and unstructured approaches. To provide hands-on experience using commercial CAD and grid generation packages.

    • Computer aided design interface with grid generation
    • Geometry Modelling and Surface Grids
    • Algebraic Mesh Generation
    • Structured Meshes from Partial Differential Equations
    • Automatic generation of Unstructured Meshes
    • Multiblock Mesh Generation
    • Unstructured grids by Delaunay Triangulation
    • Mesh Adaptation on Unstructured Grids
    • Unstructured Grids for Viscous Flows
    • Grid generation tutorials with ANSYS-Icem-cfd and Pointwise commercial software.

Intended learning outcomes

On successful completion of this module a student should be able to:
1. Evaluate the requirements of grid generation for Computational Fluid Dynamics applications.
2. Examine interfacing techniques between computer aided design software and grid generation software
3. Examine alternative methods for efficiently generating computational grids.
4. Differentiate grid generation algorithms from structured and unstructured grids.
5. Construct structured and unstructured grids with commercial grid generation software
6. Assess the control and efficiency of grid generation procedures.
7. Estimate grid quality techniques.

Data Analysis and Uncertainty


    To provide an introduction into the use of visualisation, data mining, and interactive human-computer interfaces for the analysis and interpretation of CFD simulations. Visualisation can be a critical component in helping an engineer gain insight into the typically complex optimization problems that arise in design. Through the combination of visualisation and user interaction in computer tools, the engineer's insight can help guide the computer in the process of identifying better, more effective designs. Visualisation can also be combined with automated data mining techniques to improve optimization procedures.

    To provide hands-on experience using both commercial and community developed visualisation packages.

    To introduce the concepts of error and uncertainty and how they relate to the credible numerical solution of the partial differential equations encountered in computational fluid mechanics.

    • Data interchange formats
    • Interpretation of data
    • Graphical representation of data
    • Parallel data visualisation
    • Data mining, reduced order modelling, model identification and surrogate models
    • Data fusion
    • Virtual reality visualisation
    • The right answer: consistency, stability and convergence revisited
    • Taxonomies of error and uncertainty
    • Principles of code verification
    • Introduction to the method of manufactured solutions
    • Principles of solution verification
    • Role of systematic iterative and space-time grid convergence studies
    • Richardson extrapolation
    • Principles of validation
    • Construction of validation hierarchies
Intended learning outcomes

On successful completion of this module a student should be able to:

1. Critically appraise alternative techniques for the visualisation and interpretation of CFD results;
2. Analyse commercial and community developed visualisation software packages to real CFD data;
3. Critically evaluate the use of limited simulation data when making engineering decisions;
4. Distinguish between error and uncertainty in computational simulations. Appraise the potential sources of error and uncertainty in computational simulations;
5. Critically evaluate the tools that are available for the quantification of error and uncertainty in computational simulations;
6. Be able to design/create and set-up credible computational simulations.

The Role of Experimental Data in CFD


    To provide an introduction into practical techniques for experimental data collection and its subsequent post-processing. To contrast the resultant data representation with that obtained through CFD simulation.

    • Introduction to the measurement of turbulent flows
    • Velocity and pressure measurement by aerodynamic probes
    • Velocity measurement by hot-wires/hot-film
    • Velocity measurement by optical techniques
    • Temperature measurement
    • Simple optical visualisation, Shadowgraph, Schlieren
    • Laser-based temperature and species measurements
    • Laser Induced Fluorescence
    • Skin friction, convective and radiative heat transfer
    • Error analysis.
Intended learning outcomes

On successful completion of this course the student will be able to:

  • Demonstrate a critical awareness of the alternative experimental methods available for the investigation of turbulent fluid flow
  • Demonstrate the ability to analyse and interpret quantitative and qualitative observations
  • Demonstrate a critical awareness of the relationship between the observations and the underlying theory
  • Critically interpret experimental data and contrast with that obtained from CFD simulations.

Teaching team

You will be taught by experienced academic staff from Cranfield University. Our staff are active researchers as well as tutors, with clients that include AWE, NASA Jet Propulsion Laboratory, European Space Research and Technology Centre (ESTEC), Jaguar Land Rover, BAE Systems, MBDA, MoD and SEA. Our teaching team work closely with business and have academic and industrial experience. Knowledge gained working with our clients is continually fed back into the teaching programme, to ensure that you benefit from the very latest knowledge and techniques affecting industry. The course also includes visiting lecturers from industry who will relate the theory to current best practice. Previously our students have received lectures from industry speakers including: Clyde Warsop, Executive Scientist, BAE Systems Marco Hahn, Senior Project Scientist, ARA Keith McKay, Consultant, ex-BAE Systems Johnathan Green, Senior Project Manager, BMT Fluid Mechanics Ltd Geoff Le Good, Managing Director, GL Aerodynamics Adrian Gaylard, Technical Specialist - Aerodynamics, Jaguar Land Rover Andy Wade, Technical Services CFD Team Leader, ANSYS Richard Mitchell, Technical Services Structural Mechanics Team Leader, ANSYS Matthew Sorrell, CFD Team Leader, Red Bull Technology.

Your career

Strategic industrial links ensure that the course meets the needs of the organisations competing within the computational sector therefore making our graduates some of the most desirable in the world for companies to recruit. An increasing demand for CFD specialists with in depth technical knowledge and practical skills within a wide range of sectors has seen our graduates employed by leading companies including:

  • Alstom
  • BAE Systems
  • Cummins Turbo Technology
  • BHR
  • Hindustan Aeronautics Ltd
  • ARA
  • Rolls-Royce plc
  • Siemens
  • Jaguar Land Rover
  • Bentley
  • Formula 1 teams.

Roughly one third of our graduates go on to register for PhD degrees, many on the basis of their MSc individual research project. Thesis topics are often supplied by individual companies on in-company problems with a view to employment after graduation - an approach that is being actively encouraged by a growing number of industries.

How to apply

Online application form. UK students are normally expected to attend an interview and financial support is best discussed at this time. Overseas and EU students may be interviewed by telephone.