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. This unique course covers a wide range of applications focused on aerospace computational aspects.

The MSc in Aerospace Computational Engineering 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. 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.


  • 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?

This course is suitable for those with backgrounds in mathematics, physics, computer science or an engineering discipline. We also welcome applicants with relevant industrial experience such as qualified engineers working with computational methods wishing to extend their knowledge. The part-time option is suitable for qualified engineers looking to extend their knowledge and incorporate CFD into their skill set.

Why this course?

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. By undertaking this MSc, you will enhance your skills through a detailed introduction to the state-of-the-art computational methods and their applications for digital age aerospace engineering applications.

The course 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. As a graduate, you will 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.


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

    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. The library support provided for writing C++ programs using a functional programming approach will also be covered.   

    An introduction to the Python language is also provided.
    • 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
    • Functional programming in C++ 
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.
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


    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.


    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:

  • Formulate and assess numerical integration methods.
  • Use appropriate techniques to formulate numerical solutions to differential equations.
  • Evaluate properties of numerical methods for the solution of differential equations.
  • Choose and implement appropriate methods for solving differential equations.
  • Evaluate properties of systems of linear equations.
  • Choose and implement appropriate methods for solving systems of linear equations.
  • Assess the behaviour of the numerical methods and the computed numerical solutions.

Numerical Modelling for Compressible Flows


    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 you should be able to:

Demonstrate a critical awareness of the mathematical properties of hyperbolic partial differential equations; 

Recognise the importance of non-linearities in the formation of shock waves; 

Distinguish the fundamental differences between monotone schemes, WENO schemes for hyperbolic systems; 

Judge the suitability of various Riemann solvers for various compressible flow problems; 

Create  high-resolution shock capturing schemes for compressible flow problems.

CAD & Airframe Design


    Each computational investigation requires a CAD model from which numerical analyses can be performed. This is true for both CFD and FEA simulations, both of which students are exposed to during their studies on this MSc.

    This module combines elements of CAD with an introduction to aerospace structures, focusing in particular on different components on the aircraft, as well as manufacturing philosophies which in turn inform the creation of parts (and CAD models).

    You will learn how to build up various components of an aircraft using CATIA as the CAD software and will be capable of designing components themselves, based on drawings and technical documents, which they can then use to study numerically the behaviour of the component.


    • Create solid models using Catia,

    • Create surface models using Catia,

    • Create assemblies of different models in Catia,

    • Create parametric models in Catia that can be updated through an external excel sheet,

    • Differentiate between fail-save, safe-life and damage tolerant design philosophies,

    • Understand the constraints imposed by additional reserve factors on the structural components (limit and ultimate load factors).

Intended learning outcomes

On successful completion of this module you should be able to:

1.Create CAD models using commercial and industrial leading CAD software packages.

2. Design various components found on aeroplanes from drawings and technical documents.

3. Judge which design philosophy is best suited to construct parts for different sections of the aeroplane.

4. Appraise the different type of geometries that can be created using either a surface, solid or assembly modelling approach.

5. Create parametric models that automatically update themselves based on external calculations.

Computational Aerodynamics


    Aerodynamics is of fundamental interest in computational aerospace science and engineering. It is taught as a standard undergraduate course on aerospace engineering courses. However, its complexity necessitates to approximate key aerodynamic coefficients through correlations and simplified theories; this is where this course comes into play.

    Computational Aerodynamics does not rely on simplified theories but does employ Computational Fluid Dynamics (CFD) to solve the governing equations of fluid dynamics to replace these approximate theories and correlation-based tools, such as panel methods, lifting-line theory, potential flow-based methods, etc. This allows to provide estimates for efficiency factors (such as the Oswald efficiency factor) rather than relying on correlations, improved lift and drag values beyond the linear regime and these better predicted values can directly be used in flight mechanics models to calculate static and dynamic stability modes.

    Aerodynamics for aerospace applications have further special modelling requirements which will be investigated and taught as part of this course. These include overset grid techniques to capture mesh motion of complex moving parts (e.g. flaps) and sliding mesh / frame motion to capture rotating objects (such as rotors, props, etc.).

    Additionally, practical high-performance computing (HPC) aspects will be taught in the practical session of this module to provide students with the tools to run simulations on HPC systems.

    • Review of key fluid mechanic and thermodynamic concepts
    • Review of key non-dimensional numbers
    • Calculate lift and drag polars and extracting different type of drags from simulations
    • Simulate viscous flow around aerodynamic bodies, including an extension to separated boundary layer flows
    • Extract key aerodynamic figures such as minimum drag and maximum lift over drag speed
    • Calculate centre of pressure and aerodynamic centre on aerofoils / wings / horizontal stabiliser and rudder and use extracted moments for static stability analysis.
    • Perform CFD simulations for aerodynamic devices such as wings, aerofoil sections, flaps, etc. and use appropriate modelling strategy to capture the flow around these
    • Use shock-capturing schemes to resolve shock-waves for compressible flows, such as Riemann solvers
    • Apply mesh motion to simulate rotating domains (propellers, rotors, etc.)
    • Automate the CFD calculation through scripting
    • Run automated CFD simulation on an HPC cluster environment
Intended learning outcomes

On successful completion of this module you should be able to:

  1. Develop a suitable cleaned CAD geometry and create a suitable volume mesh using commercial pre-processing software packages.
  2. Perform aerodynamic simulations using Computational Fluid Dynamics (CFD) and appraise the different modelling techniques available to obtain aerodynamic coefficients, forces and moments and their computational cost / accuracy trade-off.
  3. Use high-performance computing (HPC) resources to perform complex aerodynamic flow calculations, which includes setting up automated simulations through scripting to be run on HPC systems. 
  4. Calculate aerodynamic coefficients from raw CFD data and evaluate their differences to simplified aerodynamic correlations and mathematical theories from the literature.
  5. Develop a simulation environment that is able to capture laminar / turbulent and incompressible / compressible flows, including an understanding of shock-wave capturing schemes.

Modelling Approaches for Aerospace Application


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

    • 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 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

    The module is aimed at giving potential Finite Element users basic understanding of the background of the method. The objective is to introduce users to the terminology, basic numerical and mathematical aspects of the method. This should help students to avoid some of the more common and important user errors, many of which stem from a "black box" approach to this technique. Some basic guidelines are also given on how to approach the modelling of structures using the Finite Element Method.
    Introduction to Finite Element Methods (FEM) and applicability to different situations 

    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


    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.

    • 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 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. Among these is Professor Guy Boy from Centrale Superlec.

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 in computational aerospace design and engineering, both in the UK and globally.

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.

Graduates of this course have gone into roles including:

  • Aerospace Engineer
  • Business Engineer
  • CFD Application Engineer
  • Computational Modelling Engineer
  • Data Analyst
  • Data Science Specialist
  • Flight Operations Engineer
  • Mechanical Engineer
  • Pilot Officer
  • Research and Development Project Manager
  • Software Development Engineer
  • Structural Engineer
  • Technical Project Leader
  • Test & Reliability Engineer

Companies that employ our graduates include:

  • Airbus
  • Capgemini Engineering
  • Dassault Aviation
  • easyJet
  • Safran Engineering Services
  • Thales
  • Volvo Group
  • Yodel

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.

Cranfield’s Career Service is dedicated to helping you meet your career aspirations. You will have access to career coaching and advice, CV development, interview practice, access to hundreds of available jobs via our Symplicity platform and opportunities to meet recruiting employers at our careers fairs. Our strong reputation and links with potential employers provide you with outstanding opportunities to secure interesting jobs and develop successful careers. Support continues after graduation and as a Cranfield alumnus, you have free life-long access to a range of career resources to help you continue your education and enhance your career.

Part-time route

We welcome students looking to enhance their career prospects whilst continuing in full-time employment. The part-time study option that we offer is designed to provide a manageable balance that allows you to continue employment with minimal disruption whilst also benefiting from the full breadth of learning opportunities and facilities available to all students. The University is very well located for visiting part-time students from all over the world and offers a range of library and support facilities to support your studies.

As a part-time student you will be required to attend teaching on campus in one-week blocks, for a total of 8 blocks over the 3 year period that you are with us. Teaching blocks are typically run during the period from October to March, followed by independent study and project work where contact with your supervisors and cohort can take place in person or online.

We believe that this setup allows you to personally and professionally manage your time between work, study and family commitments, whilst also working towards achieving a Master's degree.

How to apply

Click on the ‘Apply now’ button below to start your online application.

See our Application guide for information on our application process and entry requirements.