Aerospace Computational Engineering MSc

Cranfield University

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 and instead employs 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, or other potential flow-based methods. This provides a robust way to capture the linear lift slope regime with additional non-linear effects such as occurrences of laminar separation bubbles and stall. In a similar manner, drag values are no longer based on correlations but rather modelled through transport equations, providing greater accuracy. Coupling CFD with an introduction to Aerodynamics, this module provides the basis for calculating force and moment coefficients and essential flight mechanics stability analysis is reviewed where these force and moment coefficients play a dominant role. Along the way, classical and advanced concepts in Computational Fluid Dynamics are reviewed, from CAD clean-up and meshing to solving and post-processing, including the use of high-performance computing (HPC) facilities to allow for large aerodynamic calculations to be performed.

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.

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

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

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). Students will learn how to build up various components of an aircraft using CATIA and OpenVSP 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. In addition, this module also explores elements of computational geometry, where students will work with and extend a simple Python-based CAD tool to generate parametric CAD components, which can be used in a parameterised computational workflow (e.g. for optimisation studies). Students will also work with CATIA's FEA and optimisation workbench, which can be used to construct and check structural components such as the wing, fuselage and empennage of an aircraft and optimise them to reduce weight and maximise stresses within the structure.

Software engineering is a fundamental tool for computational engineers, and this module exposes you to modern best practices in software engineering, in the context of computational aerospace applications. Python is used as the programming language, and you are first introduced to basic programming techniques with Python, as well as object-oriented programming, before exploring more advanced DevOps concepts such as version control and test-driven development. The successful computational engineer of tomorrow requires knowledge of solving partial differential equations (PDEs), solving sparse linear systems of equations, utilising high-performance computing, and optimising designs using algorithm-based and machine learning-based optimisation strategies. This will allow you to develop codes for bespoke aerodynamic and structural problems for specific PDEs, which are potentially solved using a high-performance computer. On the other hand, you will also be able to develop codes that take outputs from existing CAE solvers and apply either post-processing or optimisation to them. The computational engineering world has settled on Python as a common denominator, with many engineering packages offering scripting through a Python layer. This module will equip you with the knowledge and confidence to write Python code that connects various tools through efficient and bug-free code.

Mathematical modelling and simulation of unmanned aerial vehicles is a vital part of system development. Nowadays COTS components becoming more powerful and can multi-task and carry-out complex computations.You would need to learn the technical skills not only for the modelling and simulation but also the real-time implementation of the algorithms. The aims of this course is to provide you with the skills and knowledge necessary to model, simulate and then critically analyse the resultant non-linear motion of unmanned air vehicles using mainly Matlab/Simulink and target compile the algorithms on an embedded flight control system.

This module aims to introduce the fundamentals of dynamics and control for Unmanned Aircraft Systems (UAS). Dynamics-wise both fixed-wing and rotary UAS are covered, including effects of aero (servo) elasticity and introduction to tilt rotors and copters. From a control viewpoint focusing on linear control theory students understand its purpose, strengths and limitations, and relevant characteristics in the context of UAS control. Complemented with a case study on UAS dynamics and control, it provides the underpinning knowledge for the “UAS Modelling and Simulation” and “UAS Autonomous Vehicle Control Systems” modules.