CYBER-PHYSICAL SYSTEMS AND DIGITAL TWINS - 2027/8

Module Overview

This module explores the frontier of engineering operations: the integration of AI with live monitoring systems to transition engineered assets and systems from reactive to predictive states.

You will master the core principles of Sensing, Reasoning, and Acting that define Cyber-Physical Systems (CPS) and Digital Twins. The module is built around two primary activities: a Comparative Analysis Activity, where you critically compare two recent academic papers or technical report on applications of cyber-physical systems, and an Independent Group Project, where your team will implement a functional Digital Twin. By bridging the gap between raw sensor data and automated operational response, you will develop the skills necessary to manage, optimize, and predict the behaviour of complex engineered systems in real-time.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2027/8 academic year.

Module provider

School of Engineering

Module Leader

HAGEN-ZANKER Alex (Sch of Eng)

Number of Credits: 30

ECTS Credits: 15

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

N/A

Module content

The curriculum focuses on the operational life of an asset, or wider system of assets:

• Sensing & Multi-Scale Data Synchronisation: Implementing sensor fusion for holistic data acquisition across diverse scales¿from localised strain gauges to wide-area remote sensing and satellite imagery. This includes managing synchronisation latency in Digital Twins and leveraging Edge Computing for real-time processing of distributed environmental data (e.g., city-wide air quality networks).
• Reasoning & System-Level Closed-Loop Control: Developing real-time surrogate modelling and feedback architectures that allow complex systems to sense both their internal state and the external environment. Applications focus on dynamic traffic control orchestration and autonomous grid management where the AI must reason across high-dimensional, interconnected datasets.
• Acting & Prognostic Operations: Strategies for Demand Response, large-scale Structural Health Monitoring (SHM), and the transition from reactive diagnostics to proactive prognostic management. This covers the deployment of AI-driven interventions in critical infrastructure to prevent failures and optimise resource allocation in real-time.
• The Integrated CPS Lifecycle: Replicating advanced research on AI-CPS integration and executing a team-led project focused on a live, data-driven solution¿bridging the gap between localised mechanical assets and large-scale urban or environmental monitoring systems.

Assessment pattern

Alternative Assessment

It is not possible to complete the group assignments of the module without substantial participation. Without substantial participation in group work the next opportunity to take part in the assessment is when the module is offered to the next cohort of students. For students who have substantially participated in the group work there is the following alternative assessment: ¿ Group Project Presentation: Alternatively assessed by video recording of individual presentation following personalised brief. ¿ Group Project Report: Alternatively assessed by individual report following personalised brief. ¿ Design Review Contributions: Alternatively assessed by individual reflective report following personalised brief.

Assessment Strategy

The assessment strategy is designed to:

• Demonstrate versatility and technical excellence across the physical and digital domain.
• Challenge and nurture the ability to navigate the sensing, reasoning and acting control loop.

• Comparative Literature Analysis: Reporting and comparing in high technical detail the existing CPS/Digital Twin methodology from two academic publications or technical reports. (LO1, LO2, LO4)
• Group Project Report & Defense: A comprehensive team-led project involving the design and implementation of a Digital Twin. Each member contributes a specialized technical section and must collaboratively defend the overall system logic in a professional presentation. (LO3, LO5)

• Design Reviews: Structured sessions where teams present and discuss progress and challenges with peers and tutors.
• Verbal Feedback Loops: Real-time feedback provided during workshops on all aspect of the cyber-physical feedback loop.

Module aims

• Introduce the foundational principles of Cyber-Physical Systems and the architectural requirements of high-fidelity Digital Twins.
• Develop expertise in the "Sensing-Reasoning-Acting" cycle, focusing on how AI-driven feedback loops optimise system performance.
• Foster professional collaboration through structured group projects that mirror industrial R&D environments.
• Enable the critical evaluation of AI outputs when integrated with high-stakes, live-monitoring infrastructure.

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to:

• Develop in-depth appreciation of the nuts and bolts of cyber-physical integration.
• Provide a team challenge that draws on physical skill of sensors and instrumentation, digital engineering skills of analysis and control and physical actuation.
• Demonstrate synchronised and balanced skills in the computer lab as well as the physical workshop, and the indoor or outdoor environment.
• Learn from your own efforts as well that of others through sharing practice, peer review and peer support.

The learning and teaching methods include:

• Integrative Lectures: Focused on the high-level architecture of Cyber-Physical Systems, exploring closed-loop feedback logic and the synchronisation of physical and digital assets and networks.
• Independent Study: Individual study focused on in-depth analysis of advanced applications from current academic literature.
• System Integration Workshops: Hands-on lab sessions dedicated to building functional "Twins" through the integration of instrumentation, live data streams, edge computing, and AI-driven control logic.
• Project-Based Learning (PBL): Intensive team-led development of an end-to-end Digital Twin, mirroring a professional R&D environment from initial scoping to final deployment.
• Seminar-based Design Reviews providing a platform to present progress and challenges, in a supportive "critical friend" environment of peer and academic convener support and feedback.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: ENGM329

Other information

The School of Engineering is committed to developing graduates with strengths in Employability, Digital Capabilities, Global and Cultural Capabilities, Sustainability, and Resourcefulness and Resilience. This module is designed to allow students to develop knowledge, skills, and capabilities in the following areas:

Employability: Prepares graduates for high-level roles in Smart Infrastructure and Industry 4.0. The module mirrors the professional need for engineers who can manage live, data-driven operational assets and systems.

Digital Capabilities: Elevates proficiency in real-time data integration, edge computing, and the deployment of "living" AI models within active engineering environments.

Global and Cultural Capabilities: Analyses the societal implications of autonomous systems and the global standards for data security and ethical AI in public infrastructure.

Sustainability: Promotes resource-efficient operations by using AI to optimize demand response and load shifting, significantly reducing the carbon footprint of active systems.

Resourcefulness and Resilience: Develops the ability to troubleshoot live data streams and sensor failures, building the technical resilience needed to maintain "high-availability" digital twins.