The aim of the module is to provide a realistic and substantial team project experience in engineering design.

The aim of this module is for students to gain an understanding of structured methods as applied to product design.

The aim of the module is for the student to develop Masters' level skills (in principle and in practice) of engineering science applied to the design of products based on the processing and properties of polymers and polymer composites.

The aim of the module is to gain an understanding of modern concepts of Structural Integrity and Failure Analysis of engineering components using analytical, numerical and experimental techniques.

The aims of this module are to:

• Understand principles of, Noise, Vibration and Harshness (NVH) issues and refinement/palliations in automotive drive train systems.
• Develop advanced modelling techniques and methods of solution of drive train engineering problems.
• Develop fundamental analytical understanding of multi-body dynamics.
• Develop experimental skills for NVH measurement in powertrain application.

The aims of this module are to:

• Introduce students to digital twins, its applications, challenges and future directions.
• Introduce students to the role of engineering dynamics in the design of digital twins.
• Teach the fundamental principles and methods to tackle engineering problems where dynamics play an important role.
• Teach students the use of numerical tools as an engineering design means.
• Teach students how to employ dynamics for the design and update of digital twins based on a practical example.

The aim of the module is to develop students integrated systems problem-solving skills relevant to mechatronics, including control strategies for typical industrial automation equipment working with various actuators and sensors.

The aim of this module is to provide a fundamental understanding of the theory of hydrodynamic lubrication and classical Hertzian contact theory. An introduction is made to the mechanism of elastohydrodynamic lubrication. Important aspects of bio-tribology and nano-tribology are also briefly described.

The aim of this module is for the student to consider the potential of engineering solutions on the quest for sustainable development, to design and operate engineering systems to minimise the need for non-renewable energy and materials, to establish a circular use of resources, and to adhere to social and ethical standards.

The module will introduce and develop the concepts of seven Additive Manufacturing (AM) process categories. The module will emphasise the strengths and weaknesses of the various technologies and will highlight applications and case studies from the Additive Manufacturing (AM) industry.

The aims of this module is to:

• Introduce the drivers (technical, environmental, societal) on the engineering development of automotive powertrain due to legislation (e.g. emissions requirements in different global markets) and customer demands (e.g. vehicle attributes required for different vehicle usage cases).
• Develop an appreciation of how fundamental engineering science (thermofluids, dynamics, materials science) can be applied to an interdisciplinary application.
• Develop skills in searching, selecting and reviewing technical literature focused on automotive powertrain technologies.
• Challenge students to apply their existing foundational understanding of (mechanical) engineering to a greater range and depth suitable for a research or industrial environment.

The aim of the module is:

• To provide practical skills in the organisation, management and leadership of projects.
• To explore human behaviour in teams and practise professional competences appropriate to a career in engineering management.

The aim of the module is to enable students to use, evaluate, choose and implement CAE systems in the work environment.

The aim of the module is to introduce students from a diverse range of engineering backgrounds to both the opportunities and constraints of engineering practice in healthcare, medicine and medical device industry. The module will have a focus on products, design and manufacture, innovation and exploitation in a regulated industry on emerging health technology products.

The aim of the module is for students to gain an understanding of the science and materials relating to sports equipment design and manufacture and to appreciate the significance of this industry sector.

The aims of this module are to:

• further develop multidisciplinary engineering knowledge;
• gain an understanding of the principles of ballistics and rocket propulsion.

This module aims to provide an in­ depth understanding of how business and industry will have to adjust their products mix, production structures, supply chains, environmental protection policies, business models and corporate social responsibilities based on the three pillars of sustainable development.

The module will introduce and develop the concepts of Reverse Engineering (RE) and further investigate the concept of Additive Manufacturing (AM), emphasising the complexities of such manufacturing methods.

Non-contacting optical metrology is used to measure and monitor the performance of mechanical components ranging from microscopic parts to buildings and aerospace structures. The aim of this module is to equip students with an up-to-date understanding of optical metrology and its application in digital engineering.

This module aims to give students an in-depth understanding of the development and application of optical diagnostics for the study of fluid flows. Students will study the fundamentals of optics, the basic operating principles of lasers, sensors and detectors, and optical diagnostic techniques, such as flow visualisation, laser doppler anemometry, phase doppler anemometry and particle image velocimetry. The module will include theoretical analysis of optical systems, the study of practical optical diagnostics for fluid mechanics, hands-on laboratory exercises and data analysis.

The aim of this module is to extend student knowledge and capability in modern digital image processing methods for image capture, enhancement, segmentation, and analysis, using a wide range of current Machine Learning algorithms including Bayes, Support Vector Machines, Linear and Logistic Regression, Decision Trees, Random Forests, and convolutional neural networks (CNN).