Competition to bring increasingly capable vehicles to market has never been more intense. Development times are reducing, vehicle complexity is increasing and global market pressures demand that engineering teams make the right decisions earlier than ever. At Lotus Engineering, advanced simulation and objective development methods are combined with decades of vehicle dynamics experience to establish the fundamental character and capability of a vehicle long before the final stages of physical development.

But what happens when the simulation says the car is good? This presentation explores the critical relationship between objective engineering and expert subjective assessment: when the driver enters the development process, what an expert vehicle assessor is actually evaluating, and how subjective observations are translated back into engineering parameters and physical changes. Using the Lotus approach to vehicle dynamics development, John will explore why this human-in-the-loop development remains essential in extracting the last 5% — transforming a technically capable vehicle into one that communicates with its driver, inspires confidence and, ultimately, is engineered For the Drivers.

An Exclusive Fireside Chat with Willem Toet, F1 Aerodynamics Expert & Director, Aero Research Partners.

Multi-physics simulation and machine learning can extend engineering design beyond geometry towards earlier prediction of product performance. This talk will explore how design choices, material behaviour, and manufacturing conditions interact to influence the properties of the finished product, and how digital models can help engineers understand these relationships before committing to repeated physical prototyping and testing. The broader workflow connects design, simulation, prediction, validation, and refinement, enabling more informed engineering decisions and more targeted physical validation.

Offshore wind is set to become the backbone of Europe's clean energy system, requiring the deployment of thousands of turbines and thousands of kilometres of supporting electrical infrastructure. This unprecedented investment presents a significant opportunity for UK engineering and manufacturing businesses, with UK offshore wind supply chain markets targeted by the UK Offshore Wind Industrial Growth Plan (IGP) expected to be worth £92 billion annually by 2040.

This session will explore the priority areas identified as growth opportunities by UK Government and industry through the IGP, highlighting where support and investment are being targeted across the offshore wind sector. Attendees will gain insight into the guidance, programmes, and resources available to help UK engineering firms position themselves within these strategically important markets. The talk will also examine the technical and commercial realities of developing products for offshore wind applications. From designing large-scale structures and electrical systems capable of operating reliably for decades in some of the world's harshest environments, to understanding how engineering decisions influence customer value, project costs, and competitiveness, success requires much more than technical excellence alone.

Modern engineering systems increasingly face failure modes driven by stochastic physical phenomena that are difficult to detect using conventional signal processing techniques. DC arc faults are a representative example, as their highly variable behaviour, dependence on operating conditions, and transient nature make reliable detection extremely challenging. To address this challenge, we developed a data-driven fault detection framework based on an extensive experimental dataset and machine learning-assisted parameter optimisation, enabling robust identification of arc-fault signatures under realistic operating conditions.

A key challenge was translating algorithms originally developed in MATLAB into a deployable embedded solution. The final implementation runs in real time on a standard STM32 microcontroller without the use of dedicated AI accelerators, requiring extensive model optimisation, memory reduction, and adaptation to hardware constraints while maintaining detection performance. Using real-world DC arc fault applications from both the residential and aerospace sectors, this presentation demonstrates how complex physical phenomena can be transformed into practical, robust, and cost-effective embedded protection systems.

For most of the space age, spacecraft have been designed around a simple assumption: everything must be manufactured, assembled and tested on Earth before surviving launch as a complete system. Emerging capabilities in robotic servicing, in-space assembly and in-space manufacturing are beginning to challenge that assumption, creating new possibilities for how spacecraft and large structures are designed, built and operated.

This presentation will explore three areas where engineering design is beginning to move beyond the constraints of launch. First, how designing spacecraft for in-space servicing could enable repair, upgrade and life extension; second, how designing specifically for robotic assembly could enable structures larger and more adaptable than can be launched conventionally; and finally, how manufacturing directly in space could exploit the space environment and ultimately remove some launch constraints altogether. Drawing on current developments and practical engineering challenges, the talk will consider what engineers need to do differently when launch is no longer the end of the manufacturing process.

Standard systems engineering and product development processes treat requirements decomposition, prototype development stages, and design for excellence, including compliance, reliability, service and manufacturing as sequential activities. Competitive platform cadence (12–18 month generations) doesn't allow that, so these requirements have to be developed and traded off concurrently against a shared architecture and requirements baseline. This talk covers how systems engineering makes that concurrent work, across key focus areas.