Overview
- Start dateSeptember
- DurationFull-time: MSc - one year; Part-time: MSc - up to three years; Full-time PgCert - one year; Part-time PgCert - two years; Full-time PgDip - one year, Part-time PgDip - two years
- DeliveryTaught modules: 40%, group project: 20%, individual research project: 40%
- QualificationMSc, PgDip, PgCert
- Study typeFull-time / Part-time
- CampusCranfield campus
Who is it for?
With its blend of skills-based and subject-specific material this course aims to provide students with generic practical skills and cutting-edge knowledge adaptable to the wide variety of applications in the field of aerospace computational engineering.
The part-time option is suitable for qualified engineers to extend their knowledge and incorporate CFD into their skill set.
Why this course?
This course aims to enhance your skills through a detailed introduction to the state-of-the-art computational methods and their applications for digital age aerospace engineering applications. It provides a unique opportunity for cross-disciplinary education and knowledge transfer in the computational engineering of fluid and solid mechanics for aerospace industrial applications. Focusing on fully integrated digital design for aerospace applications, you will be able to understand and implement numerical methods on various computing platforms for aerospace applications. You will be able to meet the demand of an evolving workplace that requires highly qualified engineers possessing core software engineering skills together with competency in mathematical analysis techniques.
Sharing modules with the MSc in Computational Fluid Dynamics and the MSc in Computational and Software Techniques in Engineering, this course gives you the opportunity to interact with students from other disciplines.
Informed by industry
Our strategic links with industry ensure that all of the materials taught on the course are relevant, timely 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. Our industrial partners support this course by providing internships, acting as visiting lectures and delivering industrial seminars.
Course details
The taught modules are delivered from October to April via a combination of structured lectures, and computer-based labs. Many of the lectures are given in conjunction with some form of programming; you will be given time and practical assistance to develop your software skills.
Students on the part-time programme complete all of the compulsory modules based on a flexible schedule that will be agreed with the Course Director.
Course delivery
Taught modules: 40%, group project: 20%, individual research project: 40%
Group project
The group project is related to a wide range of aerospace applications, including a unique digital wind tunnel development. Projects are available for a) full-aircraft simulations and development of advanced turbulence models, b) structural analysis, c) fluid-structure interaction, d) coupling these aforementioned computational methods including an integrated digital design, e) advanced visualisation techniques, and f) the next generation of computational methods relevant to the aerospace industry.
Individual project
The taught element of the course finishes in May. 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.
Modules
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 and elective (where applicable) modules which are currently affiliated with this course. All modules are indicative only, and may be subject to change for your year of entry.
Course modules
Compulsory modules
All the modules in the following list need to be taken as part of this course.
C++ Programming
Aim |
An introduction to the Python language is also provided. |
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Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to: 1. Apply the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation - when writing C++ programs. |
Computational Methods
Aim |
The module aims to provide an understanding of a variety of computational methods for integration, solution of differential equations and solution of linear systems of equations. |
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Syllabus |
The module explores numerical integration methods; the numerical solution of differential equations using finite difference approximations including formulation, accuracy and stability; matrices and types of linear systems, direct elimination methods, conditioning and stability of solutions, iterative methods for the solution of linear systems. |
Intended learning outcomes |
On successful completion of this module you should be able to:
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Numerical Modelling for Compressible Flows
Aim |
To introduce basic concepts in the discretisation and numerical solution of the hyperbolic systems of partial differential equations describing the flow of compressible fluids. |
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Syllabus |
• 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 you should be able to: Demonstrate a critical awareness of the mathematical properties of hyperbolic partial differential equations; |
Numerical Modelling for Incompressible Flows
Aim |
To understand the state-of-the-art CFD methods used for computing incompressible flows in science and engineering. |
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Syllabus |
• 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 you 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. |
Analysis and Visualisation of Big Data System and High Performance Computing
Aim |
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Syllabus |
• 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. • Desktop versus supercomputing. • Parallel computing issues. • Parallellisation approaches for distributed and shared memory systems. MPI & OpenMP. • Current CFD Process Bottlenecks. • Whole Product Applications. |
Intended learning outcomes |
On successful completion of this module you should be able to: 1. Set up a systematic understanding of the alternative techniques for the visualisation and interpretation of CFD results. 2. Judge the applicability of commercial and community developed visualisation software packages to real CFD data and critically evaluate the use of limited simulation data when making engineering decisions. 3. Evaluate the performance of computing (hardware) platforms available for computational fluid dynamics simulations. 4. Propose a systematic approach along with application of the essential software extensions required for parallel computing. 5. Identify what can be achieved through the application of high performance computing for aerospace engineering problems. |
Modelling Approaches for Aerospace Application
Aim |
To understand the key features of mathematical modelling approaches and computational methods used for simulating flows relevant in aeronautical and aerospace applications. |
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Syllabus |
• CFD methods for low- and high-speed flows used for advanced aerospace applications. • CFD methods for digital wind tunnel applications. • State-of-the-art case studies and application examples. |
Intended learning outcomes |
On successful completion of this module you should be able to: 1. Set up the governing equations of external fluid dynamics to simulate external flows in a digital wind tunnel; 2. Collect data with a systematic approach and analyse computational results through numerical methods and models for turbulent flows used in aeronautical and aerospace applications; 3. Evaluate the strength and limitations of computational methods used in the aerospace sector; 4. Propose solutions in conjunction with the current efforts made by industry and academia for improving the state-of-the-art methods in the above applications. |
Computational Engineering Structures
Aim |
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Syllabus |
Introduction to the Direct Stiffness (Displacement) Method Development of Truss, Bar Element Equations in 2D and 3D Development of Beam and Frame Element Equations (2D and 3D) Development of the Plane Stress element Equations (Constant and Linear Strain) Accuracy considerations: higher order elements, Isoparametric elements. The role of numerical integration and methods used in FE. Practical Considerations in Modelling; Interpreting Results |
Intended learning outcomes |
On successful completion of this module you should be able to: Analyse and practice the theory of finite element models for structural and continuum elements. Design and solve mathematical finite element models. Interpret results of the FE simulations and analyse error levels. Create and solve mathematical finite element methods. Critically evaluate the constraints and implications imposed by the finite element method. |
Validation and Verification for Aerospace Applications
Aim |
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Syllabus |
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Intended learning outcomes |
On successful completion of this module you should be able to: 1. Distinguish and analyse different classes of numerical errors and uncertainties for simulating flows used in aeronautical and aerospace applications. 2. Evaluate the strength and weaknesses of computational approaches related to the potential sources of errors and uncertainties for aerospace applications. 3. Critically evaluate the tools that are available for the quantification of error and uncertainty for simulating external flows used in aeronautical and aerospace applications. 4. Set up reliable simulations through code verification and computational model validation. |
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. The Course Director for this programme is Professor Karl Jenkins.
Your career
The MSc in Aerospace Computational Engineering is designed to equip you with the skills required to pursue a successful career working in the UK and overseas in computational aerospace design and engineering.
Our courses attract enquiries from companies in the rapidly expanding aerospace computational and digital engineering industrial sector across the world who wish to recruit high quality graduates who have strong technical programming skills, and can assess and evaluate the results of digital/numerical simulations. They are in demand by CAD vendors, commercial engineering software developers, aerospace and computational science-related industrial sectors and research organisations, and have been particularly successful in finding employment.
Some of our graduates go onto PhD degrees. Project topics are most often supplied by industrial companies offering unsolved engineering problems and purely academic-related research projects in the field of computational engineering are also available. Our graduates are highly employable after graduation in the wide range of industrial sector including R&D departments as well as academia. Our approach to a research degree is being actively sought by a growing number of industries and academic research institutions keen to expand their impact and innovation.
How to apply
Applications need to be made online.
Once you have set up an account you will be able to create, save and amend your application form before submitting it.