Nuclear Product Engineering for Power Generation (Level 7)

This module explores the critical role of psychology and human factors in enhancing safety and quality within engineering environments. It emphasises the interdependence between safety and quality, examining both risk-based and performance-enhancing perspectives.  

Students will study Performance Influencing Factors (PIFs) at both individual (e.g. cognitive and psychological processes) and contextual levels (e.g. safety culture, leadership, systems integration).  

The module includes evaluation of tools for analysing PIFs' impact on risk and incidents, and a critical review of mitigation strategies, including those from High Reliability Organisations. Reflective and preparatory activities are integrated to support learning and assessment readiness. 

This module examines the essential role of materials in nuclear power plant design and operation. It focuses on how material structures and properties affect performance in nuclear environments, and how manufacturing processes are adapted to meet demanding conditions.  

Students will explore structural and functional materials - such as metals, carbon-based materials, and composites - through both theory and practical modelling. Key topics include degradation mechanisms, surface engineering innovations, and the use of advanced simulation tools to model microstructure evolution during thermo-mechanical processing.  

The module prepares students to tackle real-world challenges in nuclear materials engineering. 

This module equips students with in-depth knowledge of advanced manufacturing technologies in the nuclear sector, focusing on additive manufacturing and Hot Isostatic Pressing (HIP). It covers strategic, technical, and regulatory aspects of technology industrialisation, including process optimisation and roadmap development.  

Students will engage in theoretical learning and practical case studies to build collaboration skills with industry stakeholders.  

The module also addresses Intellectual Property, export controls, and the broader business environment influencing technology adoption. 

This module develops students' analytical and computational skills to leverage engineering data for better decision-making, faster product development, and improved safety. It covers data quality, mathematical and programming techniques (especially in Python), and introduces advanced tools like machine learning, large language models, and high-performance computing.  

Students will learn to build intelligent design workflows, use digital twins, and understand the full lifecycle of engineering data.  

The module also addresses AI standards, regulations, and ethics, using real-world case studies to highlight the importance of data-driven decisions in engineering. 

This module develops students' ability to design complex nuclear products using systems thinking and robust design methodologies. It emphasizes a holistic, innovation-driven approach that integrates business and customer needs within modern manufacturing contexts. 

Students will explore design for manufacture and assembly, concurrent engineering, and optimisation techniques specific to nuclear components; learn to use lifecycle data for informed, data-driven decisions; cultivate a systems engineering mindset to address emergent behaviours and remove innovation barriers; critically assess environmental and societal impacts, and balance product complexity with manufacturing efficiency and structural integrity.  

By the end of the module, students will be able to create design concepts using systems thinking; optimise decisions using lifecycle data; apply and evaluate robust design and DfX (Design for X) principles within manufacturing constraints. 

This module explores cutting-edge manufacturing processes with a focus on their application within the nuclear sector.  

Students will critically evaluate advanced machining, forging, and Power Beam welding techniques, considering industry constraints and the impact on product integrity.  

Emphasis is placed on the integration of statistical process controls to optimise manufacturing outcomes and ensure high-quality standards. 

This module examines the shift toward digital manufacturing in the nuclear industry, emphasizing the cultural, technological, and strategic changes needed for future-ready production.  

Students will explore digitalisation opportunities and challenges, including data quality, automation, and emerging technologies; digital capabilities across the product lifecycle, focusing on efficiency, cost reduction, and process simplification. 

Key topics such as: Model-Based Enterprise approaches, AI and machine learning integration; AR/VR applications in operations; foundations of digital capability, including software and data integrity; Industry 4.0 technologies like IIoT, robotics, and Manufacturing Execution Systems. 

By the end, students will be equipped to critically evaluate and contribute to digital manufacturing strategies for nuclear products. 

This module is designed to provide insight and learning into the enablers for ensuring effective factories for the manufacturing and assembly of nuclear power products for both today and future applications.  

Students will be equipped with the knowledge and skills to critically assess and apply industrial engineering principles in both low and high-volume manufacturing environments.  

Students will explore factory design and layout (FD&L) strategies, industrial engineering techniques, and the integration of automation and robotics to enhance productivity, efficiency, and compliance with customer and regulatory requirements. 

You will conduct an individual project addressing a real challenge within the Rolls-Royce Submarines business. You will propose and evaluate innovative solutions using the skills gained throughout the course.