MEng Aerospace Engineering
City St George's, University of London
Entry requirements
A level: BBB (including Mathematics) IB: 30 points with Higher Level Mathematics at grade 5 OR Standard Level Mathematics at grade 7 AND Higher Level Physics/Biology/Chemistry at grade 5 and minimum of grade 5 in Standard Level English.
About this course
MEng in Aerospace Engineering, focusing on the analysis and design of aerospace vehicles and their systems. Core topics include mathematical modelling, engineering design, programming, data analysis, signal processing, and artificial intelligence and machine learning. The course includes an individual project and a group design project integrating engineering solutions across multiple disciplines. An optional placement year is available, with previous placements at BAE Systems, Rolls-Royce, and Airbus.
Modules
- Professional Placement & Career Development module
- The Engineer in Society - Social Responsibility
- Engineering Design 1
- Introduction to Mechanics of Materials and Manufacturing
- Electronics 1
- Introduction to Programming
- Engineering Science
- Mathematics 1
- Introduction to Thermodynamics and Fluid Mechanics
- The Engineer in Society - Sustainability and Circular Economy
- Mathematics 2
- Engineering Design 2 - Aerospace
- Fluid Mechanics
- Structures and Materials
- Thermodynamics
- Mechatronics and Systems
- Data Analysis for Engineers
- Individual Project
- The Engineer in Society - Zero Emissions Aerospace
- Composite Analysis and Manufacturing
- Aerospace Propulsion
- Aerodynamics
- Flight Mechanics and Control
- Structural Analysis and Finite Element Method
- Design Group Project – Aerospace
- The Engineer in Society – Aerospace Resilience
- Computational Fluid Dynamics
- Structural Dynamics and Aeroelasticity
- Advanced Aerodynamics
- Zero Emissions Vehicle Propulsion Technology
- Robotics Imaging and Vision
- Unmanned Aerial Vehicles
- Composite Assembly and Joining
- Sustainable Airworthiness and Maintenance
- Introduction to Entrepreneurship
- Sustainable Thermal Power Generation
- Machine Learning
- Digital Communication and Signal Processing