Publications

Natural fiber composites have gained attention as sustainable alternatives to their synthetic counterparts due to their biodegradability and renewable origins. However, their heterogeneous properties often lead to higher failure rates, making reliable quality assessment crucial. Non-destructive evaluation (NDE) therefore plays a key role in assessing these materials. While significant progress has been made in applying machine learning to NDE, further research is needed to assess its effectiveness with natural fiber-reinforced polymers (NFRPs).

To address this gap, this study investigates the use of machine learning, particularly convolutional neural networks (CNNs), to improve the defect detection process in NFRPs. For this purpose, flax/epoxy composite laminates subject to diverse damage scenarios were manufactured and scanned using phased array ultrasonic testing (PAUT). Three distinct datasets were created: the raw data, the raw data processed using the Hilbert transform, and reconstructed images derived from the raw data using Principal Component Analysis (PCA). These datasets were used to fine-tune separate pre-trained ResNet50 models to evaluate and compare their performance in classifying the images and distinguishing between those containing visible defects and those without. 

Experimental results showed that the proposed imaging system can accurately detect and classify a significant range of material defects in NFRPs of diverse dimensions, size and in-depth location through the laminate. Furthermore, the results highlight also the potential of CNN-based methods in automating and enhancing defect detection in NFRPs, offering a pathway to more reliable and efficient inspection of these materials.

Passenger car carbon footprints are highly sensitive to future energy systems, a factor often overlooked in life cycle assessment. We use a time-dependent prospective life cycle assessment to enhance carbon footprints under four 1.5–3.0 °C decarbonisation pathways for electricity, fuel, and hydrogen from an energy-based integrated assessment model.

Across 5000 comparative cases, battery electric vehicles consistently have the lowest carbon footprints compared to hybrid, plug-in hybrid, and fuel-cell vehicles. For example, battery electric vehicles show an average 32 to 47% lower footprint than hybrid combustion in 3.0 °C and 1.5 °C climate-compatible futures, respectively. This is driven by greater projected decarbonisation of electricity compared to fossil-dominated fuels and hydrogen. Battery electric vehicles meaningfully retain their advantage for mileages over 100,000 km, even in regions with carbon-intensive electricity since these are anticipated to decarbonise the most. 

Although our study supports battery electric vehicles as the most reliable climate-mitigation option for passenger cars, reducing their high manufacturing footprint remains important.

This paper provides an overview of the application of reconfigurable windings in traction motors and their prospective benefits for electric vehicles. Methods of implementation discussed are series–parallel, star-delta, and tapped windings with series-parallel configurations having the greatest potential for system performance improvements.

The review provides valuable insights into the impact on e-drive system mass and volume, showing significant improvements to torque and power densities of approximately 40-80% can be achieved. Other investigations targeting motor efficiency show total motor loss reductions of approximately 30-60% are also possible.

The published literature on the topic tends to be case-specific, indicating that the generalisation of the technology applicability is lacking, with a particular need for design tools and methodologies early in the propulsion system design stage, to explore how the technology could be best applied.

This is also highlighted by the system level impacts shown in a case study on the Nissan Leaf drive, with series-parallel reconfigurable windings resulting in a 41.67% inverter current reduction while maintaining baseline performance characteristics. 

Other key challenges to the technology uptake include the cost and complexity of the reconfiguration device, however If these can be overcome, and the design advantages better understood, traction drives with reconfigurable windings show potential in reducing the size, weight, and cost of the electric machines, while improving their efficiency and power density.

1. Most football fans acknowledge the scientific consensus on climate change (84%) and are worried about climate impacts (81%), yet they underestimate how many fans share their beliefs.

2. This misperception is linked to ‘climate silence’ – not talking about climate. Most fans converse about climate change at least several times a year with friends, family, and colleagues, but fall silent with other football fans. Climate is not seen as an appropriate conversation topic here due to stigma and social norms.

3. Initiatives like Pledgeball – that engage people on climate action through sport – can help break ‘climate silence’ and change social norms. Fans who made a climate-related pledge reported talking about climate change more frequently, with pledging itself being a talking point.

4. There is a strong appetite among fans for meaningful climate leadership by the football sector – 82% of respondents want their clubs to do more on climate.

Next-generation aero-engine compressors will operate with overall pressure ratios exceeding 70:1. This will require shorter compressor blades, presenting a challenge to the designer when predicting tip clearance and efficiency. Buoyancy-induced flow within co-rotating compressor discs drives the heat transfer that determines rotor expansion and the resulting blade-tip clearance. This inherently unstable flow is influenced by the radial temperature distribution of the discs, rotational speed, as well as enthalpy and momentum exchange with an axial throughflow of cooled air at low radius.

Due to the rotation of the engine compressor, this throughflow may become swirled, altering the temperature, mass exchange and swirl within the rotating cavity. The University of Bath Compressor Cavity Rig has been adapted to introduce pre-swirl into the axial throughflow by passing it through rotating holes. The effects of inlet swirl have been characterised in terms of Rossby and Reynolds numbers.

Measurements of disc temperature, shroud heat flux and unsteady pressure in the rotating frame of reference are used to quantify the effects of ingestion (entrainment) of fluid into the cavity.

The unsteady dynamics and rotation of the core relative to the disc have been measured in both the stationary and rotating frames of reference with consistent results. A single correlation between shroud Nusselt and Grashof numbers has been established, effectively capturing the impact of swirl, Rossby number and free convection.

The performance of electric machines for automotive applications is characterised by a high transient torque capability for low speed tractability and a large speed range of high energy conversion efficiency to achieve a desirable vehicle range. Inevitably, these conflicting requirements will introduce a compromise in the design process of electric machines and drives, generally resulting in heavier machines and overrated drive specifications.

This paper discusses the principles of reconfigurable windings, explaining how altering winding connections directly influences key machine parameters like flux linkage, inductance, and resistance. It details the necessary switchgear for series-parallel winding reconfiguration, highlighting potential advantages such as enhanced fault tolerance and emergency braking capabilities. A prototype in-wheel motor with series-parallel reconfigurable windings, developed as part of the EM-TECH Horizon Europe project, is presented.

Simulation results using the Artemis MW130 driving cycle demonstrate that an efficiency-optimized gear shifting strategy can achieve a 1.57% reduction in energy consumption and a speed range extension greater than 50% compared to a fixed winding configuration. This highlights the potential of reconfigurable windings to improve the range of Battery Electric and Hybrid Electric Vehicles (BEVs/HEVs) by reducing drive cycle losses across various speed regions.

Hydrogen combustion can decarbonise difficult-to-abate sectors. However, practical deployment depends on reliable prediction of combustion behaviour under transient conditions, which contrasts with the steady-state experiments typically used for combustion mechanism development. This study presents a fully optimised H2-NOx mechanism, calibrated against 118 fundamental combustion datasets containing 1695 datapoints, which shows significant improvements in the prediction of ignition onset in an internal combustion engine with nitric oxide injection into the intake system.

In contrast to prior single-objective approaches, this study introduces a fundamentally new approach to chemical kinetic mechanism optimisation, which leverages a Multi-Objective Particle Swarm Optimisation framework on a High-Performance Computing platform. The framework simultaneously balances accuracy and consistency across datasets, explicitly incorporates experimental uncertainty, and evaluates all candidate mechanisms with full chemical simulations. Prediction accuracy is quantified using the normalised root mean square error (nRMSE) to experimental measurements and the proportion of predictions within experimental uncertainty limits. Relative to the best existing mechanism, the optimised model achieves a 35 % reduction in nRMSE and a 19 % increase in the number of predictions within uncertainty bounds, demonstrating improved predictive performance for fundamental combustion targets.

When the optimised mechanism was applied to autoignition timing in a Homogeneous Charge Compression Ignition engine, significant improvements were found for data with nitric oxide. Nevertheless, the overall accuracy in autoignition prediction is insufficient for practical applications, indicating that transient engine conditions are not adequately represented by steady-state datasets. These findings underscore that even fully optimised mechanisms based solely on fundamental experiments will not deliver high-accuracy predictions under real-world, transient conditions and integration of transient combustion data into future development of chemical mechanisms is recommended.

Positive attitudes towards digital twins: participants generally view digital twins positively and think that they could have a positive impact in the near-term.

High levels of trust: Participants reported high levels of trust in digital twins, even for applications that are not currently feasible. This suggests that there may be a tendency for over-trust that organisations should be aware of.

Beware of over-trust: organisations should be aware of potential over-trust in digital twins and ensure that users have realistic expectations of what a Digital Twin can and cannot do. Ways to do this include ‘grounding’ discussions of the Digital Twin in details about its capabilities and how it was developed and validated.

Predicting the state of health (SoH) of Lithium-ion battery cells is essential for optimizing their utilization. Although electrochemical degradation models and data-driven approaches are promising for SoH estimation, they often rely on experiments with repeated idealized cycles and extended relaxation periods, leading to non-robust models for dynamic real-world conditions.

This study presents a novel investigation on how different cycling sequences (ageing history) influence future degradation rates and SoH estimation. In a unique experimental design, four identical automotive-grade NMC pouch cells were initially subjected to different accelerated cycling conditions, followed by common protocols until the degradation curve passed its knee point.

A detailed analysis was conducted on capacity evolution, swelling and apparent capacity fade. Results indicate that ageing history must be considered in SoH modelling, to achieve robustness under dynamic conditions and improve system safety. Under some conditions, the knee point onset was delayed by at least 50%, while in others, it did not occur at all. Additionally, reversible short-term capacity fade is strongly dependent on the cell’s state, with fresh cells exhibiting a drop of approximately 15% which gradually decreases to around 1% by the end of the test campaign. 

These findings highlight key considerations for transitioning from laboratory-based to real-world applicable SoH models. Finally swelling shows strong correlation with SoH (R² = 0.97), and is therefore a valuable alternative SoH indicator, especially in online in-service estimation techniques where controlled capacity checks under repeatable conditions are not available.

Introduction Poor housing quality associates with risk for depression. However, previous research often lacks consideration of socioeconomic status (SES) baseline depressive symptoms and biological processes, leading to concerns of confounding and reverse causation.

Methods In a sample of up to 9669 adults, we investigated cross-sectional and longitudinal associations between housing quality (assessed at age 28, 1-year and 2 year follow-ups) and depressive symptoms (at four intervals between enrolment and 18-year follow-up). In subsamples (n=871, n=731), we investigated indirect effects via DNA methylation.

Results Poor housing quality associated with depressive symptoms cross-sectionally (beta range: 0.02–0.06) after controlling for SES and other factors. Longitudinally, this association persisted at the ~2 year, but not the ~18-year follow-up period. Indirect effects (β=0.002–0.012) linked to genes related to ageing, obesity and brain health.

Conclusion These results highlight poor housing quality as a risk factor for depression and the potential role of DNA methylation in this association.