Our Projects

The opposed-piston 2-stroke (OP2S) engine has historically been applied to aircraft propulsion as well as engines for power generation and rail traction with great success. More recently, Achates Power have shown the potential of the OP2S engine for automotive applications. The low surface area to volume ratio of the combustion chamber in OP2S engines, combined with its lack of a cylinder head, results in lower heat losses yielding high exhaust gas energy, making it an ideal candidate for turbocharging, as well as increased brake thermal efficiency. However, due to the requirement for a positive delta pressure across the cylinder at all operating points (intake manifold pressure must be higher than exhaust manifold pressure) to ensure the scavenging performance of 2-stroke engines, crankcase scavenging is typically used instead as, unlike a turbocharger-driven charging system, it guarantees a positive delta pressure gradient at all operating points. Nevertheless, other scavenging systems, such as a supercharger in conjunction with a turbocharger, have been shown to provide effective scavenging performance whilst utilising the otherwise wasted exhaust gas energy. Moreover, the use of a combined supercharger/turbocharger charging system with an OP2S architecture provides greater flexibility in the air-fuel-ratio control and exhaust temperature management, whereas conventional 4-stroke engines are expected to require the use of cylinder deactivation or other thermal management strategies to meet the low emissions standards. Furthermore, the use of electrically assisted turbochargers not only increases this flexibility but also provides a means of extracting excess work from the turbine by turbocompounding, whilst simplifying the intake air path.

The purpose of this work is to investigate a novel pressure-balanced free-piston engine concept. An OP2S engine model will first be adapted from prior work with a view to understanding the effects of crank phasing and port geometries on gas dynamics. A Libertine free-piston engine will then be used to inform and verify a linear generator engine model constructed using a similar geometrical arrangement as the Libertine engine. Having completed this work, a verified free-piston OP2S engine model can be developed using learnings from the prior work. This model will yield a greater understanding of the capabilities of the conceptual engine arrangement whilst also providing insight into the intrinsically linked relationships between the mechanical and electrical subsystems. The model could also be used to assist in the design of a prototype of the concept engine, the manufacture of which would be dependant on time and funding.

Indrek's  research project addresses a significant problem in the field of propulsion: the need for cleaner, more efficient liquid fuels. The project focuses on modelling the evaporative behaviour of methanol, a type of alcohol, chosen due to its unique properties such as a high-octane number, auto-ignition temperature, heat of vaporization, embedded oxygen, and excellent lean burn properties. These characteristics can lead to cleaner, more efficient combustion, and thus, reduced emissions while improving performance.

The high evaporative cooling effects of methanol, attributed to its high heat of vaporization, are a key focus. The project aims to develop and improve overall understanding and low-dimensional models of methanol's evaporative behaviour by conducting experimental data collection on a test engine fuelled with various methanol blends under various operating conditions and numerical experiments for additional, high-fidelity data. By better understanding and predicting the evaporative characteristics of methanol, this research seeks to enhance fuel efficiency and engine performance while simultaneously reducing emissions in marine engines. Ultimately, these findings could contribute to helping the UK achieve its net-zero targets by 2050 and be potentially applied to larger marine applications and other transport sectors, such as automotive and aviation, in the future.

Organisations that employ large numbers of people (above 1000 employees) generate and attract trips that, otherwise, would not be made. Commuting generates 5% of the UK’s year total emissions [1] while business air travel accounted for 154 million Mt CO2 globally in 2019 [2]. 

Large employers, aware of the impact of transport in the generation of GHG emissions as well as congestion and pollution, have started to implement policies and interventions to promote sustainable modes of transport among their employees. This is a significant opportunity for public/private collaboration to achieve Net Zero by 2050. But organisational policies do not always translate into changes of behaviours. Previous research suggests that people tend to accept policy if they perceive it as effective and fair, or if they feel like they had been part of the decision-making process[3]. 

Are individuals more inclined to abide by restrictive rules when they participate in the process of creating them? The normative idea of public engagement in decisionmaking is well studied in the context of Government-citizen relationship, but not so much in other spheres like the workplace. This research explores the role of deliberation, co-creation and trust in the effectiveness of restrictive travel-related rules in the workplace, providing new insights for policy-makers in public and private organisations alike. 

1.            Mobilityways, Zero carbon commuting - the business case, in The CBI, T. CBI, Editor. 2022.

2.            Transport & Environment, Travel Smart: Benchmarkin global corporate flyers on leadership towards purposeful travel. 2022: https://www.transportenvironment.org/.

3.            CAST, Motivating low-carbon behaviours in the workforce - Insights from Cornwall Council. 2023, Centre for Climate Change and Social Transformations: CAST Briefings.

​Cryogenics is a branch of science studying materials that undergoes phase change between -150 and -273 Degrees Celsius. Most popular examples for cryogenic materials are Oxygen,Nitrogen,  Helium and Hydrogen.

Cryogenics have been widely employed as a part of multiple industries such as but not limited to: Aviation, Automotive, Medical and Storage industries. Examples of utilisation cases can be listed as: Rocket fuel and pre-conditioner, Fuel cell propulsion systems, cryosurgery and refrigeration units for cold cargo.

Leidenfrost effect is a physical phenomenon where, a liquid on a hot surface that is above its boiling point produces a vapour layer that acts as an insulation between the liquid and the hot surface. The vapour layer produced acts as an insulation limiting the heat transfer rate between the liquid and the surface. By utilising surface properties, the flow of Leidenfrost Droplets can be sustained which can be described as "Self-propelled Leidenfrost Droplets".

Due to the increasing consciousness around global warming and the request of reducing transportation emissions, propulsion systems are required to be more efficient and less polluting then ever. In support of this, commercial aviation and automotive companies have been seeking alternative propulsion solutions one of which is fuel cells which are powered by cryogenics resulting in clean propulsion without harmful emissions. Because cryogenic systems so far have only been used in specialised and limited life cycle applications in propulsion such as rocket fuels, long term effects and systems level applications in long term must be explored in order to commercialise such systems. Examples of such propulsion systems have been brought to market by multiple OEMs with the price being the largest penalty as well as the complexity, storage and weight challenges surrounding fuel cell systems.

The study will aim to explore the capability of utilising Self-propulsion of Cryogenics inside a Pipe mostly aimed at fuel delivery systems in order to understand the boiling characteristics and physical interactions with the internal pipe surface. The Self-propulsion inside the pipe is aimed to be achieved by the introduction of in-pipe structures to manipulate the flow during the Leidenfrost regime of the Cryogenic liquids to be utilised.

Onur's PhD study is planned to be conducted both in practical experimentation and simulation in order to verify and test varying conditions and flow regimes. Initially, the testing will begin with liquid nitrogen as a working fluid which will then be changed to liquid hydrogen. The reason for the  utilisation of liquid nitrogen at the start of the study is to understand the cryogenic working environment and the challenges attached to it. Simulation studies will be employed in order to understand flow regimes that are not possible to replicate using practical means and to verify the results of the practical experimentation.

Fuel cells are devices that use hydrogen and oxygen to produce electricity without burning them. They are clean and efficient sources of energy for many applications, such as cars and buses. One type of fuel cell is called a proton exchange membrane fuel cell (PEMFC). It has a special membrane that allows protons (positive hydrogen atoms) to pass through it, while electrons (negative particles) go around it. This creates an electric current that can power a device. To understand and improve how PEMFCs work, we need to consider many factors that affect them, such as temperature, humidity, pressure, material thickness, stress, and resistance. These factors can change how well the fuel cell performs and how long it lasts. We want to find the best combination of these factors for different situations.

One way to do this is to create computer models of PEMFCs that can mimic their real behaviour. We can then test different scenarios and see how the fuel cell reacts. This helps us to fine-tune our models and make them more accurate and realistic. This also saves us time and money, as we don’t need to do as many physical experiments. By doing this, we can learn more about how PEMFCs work and how to make them better. We can also make them more suitable for different uses and environments. This will help us to develop and use PEMFCs more widely and effectively. This is important for the future of fuel cell technology and clean energy.

Electric vehicles (EVs) play a key role in decreasing the carbon footprint of the mobility sector. Their high upfront cost, limited range and slow charging speed are however a barrier to increased EV uptake. Reducing the cost and improving the EV Lithium-ion (Li-ion) battery could reduce these barriers.

There is however limited knowledge in the safe operation and degradation rate of Li-ion batteries. This is largely due to the complex electrochemical mechanisms not being well understood. Furthermore, the large operating envelope (temperature, charging speed etc.) over its lifetime require resource intensive testing to parameterize semi-empirical models. The battery is therefore operated very conservatively, resulting in oversizing the battery and sub-optimal operating conditions resulting in inefficiencies and higher costs.

Johannes's PhD aims to provide optimal testing strategies and accurate modelling (with a focus of degradation) of Li-ion batteries in order to provide information to facilitate more efficient operating strategies (e.g. fast charging). This will be achieved by a combination of advanced design of experiments (DOE), modelling and machine learning.

The initial part of the PhD will focus on building a model structure which is based on a semi-physical neural network. The accuracy of this model will then be assessed using existing battery data in literature and data provided by the industrial partner. An experimental test campaign will then be designed and implemented, in an attempt to efficiently parameterize the battery models. The resultant battery models would then provide important information to improve the safe operation range of the battery.

Most of today’s devices and electric vehicles rely on lithium-ion batteries due to their balanced performance and cost. However, they come with critical safety concerns: lithium-based compounds, which store substantial energy in a compact form, are prone to overheating and even explosion under certain conditions, such as physical impact, rapid temperature change, or exposure to air. Furthermore, lithium mining and production are inefficient, adding environmental challenges.

This has sparked a demand for next-generation batteries using alternative materials to improve safety, efficiency, and sustainability. Although various experimental designs for new batteries exist, research often stops at initial testing, with limited investigation into their underlying chemical behaviours. This lack of insight hampers our ability to predict performance and manage risks effectively.

Eymen's PhD research aims to address these gaps through mathematical modelling, beginning with a thorough review of existing battery models, emerging battery chemistries, and key safety and performance factors. I’ll then develop a mathematical model specifically for next-gen battery cells, embedding it in COMSOL and other tools to simulate their chemical and thermal behaviours. By applying this model to real-world scenarios, such as electric vehicles or drones, I will conduct performance analyses to assess potential risks, such as thermal propagation and overpressure from chemical reactions. The final stage will involve validating these models through experimental data, enabling us to reduce the need for extensive physical testing and propose effective safety measures for future battery designs.

Object tracking using cameras is a hot research topic with many practical uses, from video surveillance and self-driving cars to analyzing crowd behavior and understanding traffic scenes. The idea is to use one or multiple cameras to follow and identify the location of objects, like people or cars, across several video frames. While this sounds straightforward, it's quite challenging due to factors such as changes in lighting, camera angles, and objects blocking each other.

In recent times, the use of multiple cameras for surveillance has grown due to the availability of affordable, high-quality cameras and powerful computers. Multi-camera systems can offer more comprehensive tracking compared to a single camera, but they also bring additional challenges. For example, ensuring all cameras are in sync, dealing with objects that get blocked from view, and handling changes in how an object looks from different angles.

Yuqiang's project aims to tackle these challenges by enhancing existing methods and introducing a new framework based on machine learning. The goal is to make tracking objects across multiple cameras more accurate and dependable, ultimately contributing to the betterment of real-world applications such as smarter city management and improved traffic flow.

Decarbonisation of transport is a major challenge facing nations worldwide. There have been targets put in place by governments to limit the use and sale of petrol-powered automotive vehicles by 2035, meaning that it is becoming increasingly important to develop and improve on existing technologies to power automotives without the use of fossil fuels. One such method to power automotives is using fuel cells to generate electricity from hydrogen or hydrogen rich molecules. Current fuel cells operate at around 40-60% efficiency in conversion from fuel to electricity which is not enough to one day act as a replacement for petrol in automotives. There are various methods which can aid in improving the efficiency of fuel cells and one of these is the focus of this research. By increasing the surface area of the commonly used platinum catalyst layer in the fuel cell the rate of the hydrogen oxidation and oxygen reduction reactions that occur in fuel cells can be increased, therefore improving its efficiency. This method utilises growing platinum nanostructures inside a lipid template to create the high surface area structure. This also has the benefit of using less platinum and reducing the cost the material used in the catalyst layer. The research will build upon previous PhD students work in which the lipid phytantriol was used as a template to create the nanostructured platinum and will focus in the optimisation of the structure of the platinum in order to produce the highest surface area.

Data from the World Health Organisation (WHO) shows approximately 1.3 million people die annually from road crashes, which are identified as the leading cause of death for children and young adults. In the UK, there were 24,530 people killed or seriously injured in 2021 according to the estimation of the Department for Transport (DfT). Besides concerns on the road safety aspect, road traffic crashes cost most countries 3% of their gross domestic product, leading to considerable financial loss to individuals, their families, and the entire nation.

Meanwhile, various studies prove that human error was the sole factor in more than 50% of road accidents, and was a contributing factor in over 90%. Commonly seen human errors such as drowsy driving, distracted driving, and chemical impairment caused by alcohol or drugs form part of today’s road traffic system, threatening everyone’s life safety. However, the current development in autonomous driving can’t fully mitigate this issue since the takeover by a human driver is still needed before the SAE level 5 is reached, which is decades away. Propelled by societal pressure and legislation, Driver Monitoring System (DMS) was introduced by car manufacturers to tackle this long-existing problem, combining driver behaviour obtained from a camera and driving behaviour from the vehicle itself to determine the driver’s state. Despite the effectiveness of existing commercial systems, the lack of direct measurement remains a challenge to further improve the accuracy. On the other hand, the feasibility of extracting physiological information such as vital signs based on non-contact approaches in the lab environment has been proven.

Therefore, the focus of Gengqian's project is the development of a novel non-contact driver monitoring system for attentiveness detection via radar, camera, or ultrasonic sensors. Firstly, physiological information is obtained by signal processing and then compared with the ground truth from body-attached sensors to develop a robust non-contact vital sign monitoring system. On this basis, extracted features such as heart rate, respiratory rate, skin temperature, and body movements are combined with observations from real-world driving experiments and brain activity measured by EEG to develop a new model of driver attentiveness. For example, a reduction in heart rate, respiratory rate, or blink rate could be good indicators of low attentiveness.