This unique course covers a wide range of applications focused on aerospace computational aspects. Within the next five years there will be a demand for engineers and leaders who will be using 100% digital techniques for aerospace applications, design and testing.

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

Module Leader
  • Dr Irene Moulitsas
Aim

    Object oriented programming (OOP) is the standard programming methodology used in nearly all fields of major software construction today, including engineering and science and C++ is one of the most heavily employed languages. This module aims to answer the question ‘what is OOP’ and to provide the student with the understanding and skills necessary to write well designed and robust OO programs in C++. Students will learn how to write C++ code that solves problems in the field of computational engineering, particularly focusing on techniques for constructing and solving linear systems and differential equations. Hands-on programming sessions and assignment series of exercises form an essential part of the course.
    An introduction to the Python language is also provided.


Syllabus
    • The OOP methodology and method, Classes, abstraction and encapsulation;
    • Destructors and memory management, Function and operator overloading, Inheritance and aggregation, Polymorphism and virtual functions, Stream input and output;
    • Templates, Exception handling, The C++ Standard Library and STL.
Intended learning outcomes

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

1. Apply the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation - when writing C++ programs.
2. Create robust C++ programs of simple to moderate complexity given a suitable specification.
3. Use the Standard Template Library and other third party class libraries to assist in the development of C++ programs.
4. Solve a range of numerical problems in computational engineering using C++.
5. Use development environments and associated software engineering tools to assist in the construction of robust C++ programs.
6. Evaluate existing C++ programs and assess their adherence to good OOP principles and practice.


Computational Methods

Module Leader
  • Dr Irene Moulitsas
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.

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 a student should be able to:
1. Implement and use numerical integration methods.
2. Use appropriate techniques to formulate numerical solutions to differential equations.
3. Evaluate properties of numerical methods for the solution of differential equations.
4. Choose and implement appropriate methods for solving differential equations.
5. Evaluate properties of systems of linear equations.
6. Choose and implement appropriate methods for solving systems of linear equations.
7. Evaluate the behaviour of the numerical methods and the numerical solutions.

Numerical Modelling for Compressible Flows

Module Leader
  • Dr Panagiotis Tsoutsanis
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.

Syllabus
    • 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.

Numerical Modelling for Incompressible Flows

Module Leader
  • Dr Laszlo Konozsy
Aim

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

Syllabus
    • 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.

Analysis and Visualisation of Big Data System and High Performance Computing

Module Leader
  • Dr Zeeshan Rana
Aim

    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.

Syllabus
    • 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
    • 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 a student 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;
3. Critically evaluate the use of limited simulation data when making engineering decisions;
4. Evaluate the performance of computing (hardware) platforms available for computational fluid dynamics simulations;
5. Propose a systematic approach along with application of the essential software extensions required for parallel computing;
6. Identify what can be achieved through the application of high performance computing for aerospace engineering problems.

Modelling Approaches for Aerospace Application

Module Leader
  • Dr Laszlo Konozsy
Aim

    To understand the key features of mathematical modelling approaches and computational methods used for simulating flows relevant in aeronautical and aerospace applications.


Syllabus
    • Overview of the governing equations of fluid dynamics applicable to external flows including classical and advanced turbulence modelling approaches for aeronautical and aerospace applications;
    • 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 a student 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

Module Leader
  • Dr Laszlo Konozsy
Aim

    This course covers more advanced aspects of CAE, the aim being to introduce students to key concepts and techniques in the use of CAE application software tools. Use is made of structured computer based workshops which employ industry standard systems for CAD through to Engineering Analysis.

Syllabus
    • Introduction to I-DEAS CAE Finite Element Analysis (FEA) Simulation software,
    • CAE FEA Pre- and Post-Processing, Free mesh and Mapped mesh techniques,
    • Quality checks on nodes and elements, Finite element and geometry based
    • boundary conditions, Utilising solids based modelling geometry for downstream
    • CAE FEA, CAE linear statics analysis using the I-DEAS CAE FEA Simulation
    • software, Case Studies.
Intended learning outcomes On successful completion of this module a student should be able to:
1. Analyse and evaluate how modern CAE Analysis tools are used.
2. Construct a computational mesh by using free mesh and mapped mesh generation techniques.
3. Generate finite element analysis models by using either geometry from the I-DEAS solid modeller or an external CAD system.
4. Use the I-DEAS Simulation Analysis module to run linear static analysis modules.

Validation and Verification for Aerospace Applications

Module Leader
  • Dr Zeeshan Rana
Aim

    To introduce the concepts of validation and verification methods including the management of computational errors and uncertainties related to simulation of external flows for aeronautical and aerospace applications.

Syllabus
    • Mathematical foundations of uncertainty quantification methods and related theories including the definition of consistency, stability and convergence
    • Taxonomies of numerical errors and uncertainties
    • Principles of code verification for external flows
    • Introduction to the method of manufactured solutions
    • Principles of solution verification
    • Principles of the generalized Richardson extrapolation including a method how to report numerical errors in a unified way based a proper grid convergence study
    • Principles of mathematical model validation
    • Statistical approaches to epistemic uncertainty
    • Construction of validation and verification hierarchies.
Intended learning outcomes On successful completion of this module a student 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.

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

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.