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Item Open Access Optimising vehicle performance with advanced active aerodynamic systems(Taylor and Francis, 2025-01-01) Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Siampis, Efstathios; Brighton, JamesThis study investigates the performance potential of advanced active aerodynamic systems on high-performance vehicles. Static and active aerodynamic configurations, including asymmetrically actuated systems, are evaluated to identify performance gains and the mechanisms driving these improvements. Vehicle performance is optimised using a minimum lap time simulation framework, which utilises a transient vehicle dynamics model and CFD-derived aerodynamic data. Results indicate that configurations with greater aerodynamic adaptability enhance acceleration, braking, cornering, and straight-line performance, yielding notable lap time reductions compared to a static aerodynamic configuration. The asymmetrically controlled aerodynamic configuration achieves the highest lap time reduction of approximately 0.92 s (0.76%) due to its ability to modulate downforce both longitudinally and laterally. Optimal control strategies show that aerodynamic elements are actuated to balance vertical tyre load shifts resulting from load transfer, prioritising downforce on underloaded tyres in demanding scenarios like braking, cornering, and acceleration. Additionally, optimal design parameters for the brake, torque and roll stiffness distributions shift rearward as configurations provide greater control of aerodynamic loads on the rear axle. Overall, this research demonstrates the performance advantages of active aerodynamic systems and offers insights into the mechanisms underlying these enhancements, establishing a foundation for further innovations in the field.Item Open Access Fusion vs. Isolation: evaluating the performance of multi-sensor integration for meat spoilage prediction(MDPI, 2025-05-01) Heffer, Samuel; Anastasiadi, Maria; Nychas, George-John; Mohareb, FadyHigh-throughput and portable sensor technologies are increasingly used in food production/distribution tasks as rapid and non-invasive platforms offering real-time or near real-time monitoring of quality and safety. These are often coupled with analytical techniques, including machine learning, for the estimation of sample quality and safety through monitoring of key physical attributes. However, the developed predictive models often show varying degrees of accuracy, depending on food type, storage conditions, sensor platform, and sample sizes. This work explores various fusion approaches for potential predictive enhancement, through the summation of information gathered from different observational spaces: infrared spectroscopy is supplemented with multispectral imaging for the prediction of chicken and beef spoilage through the estimation of bacterial counts in differing environmental conditions. For most circumstances, at least one of the fusion methodologies outperformed single-sensor models in prediction accuracy. Improvement in aerobic, vacuum, and mixed aerobic/vacuum chicken spoilage scenarios was observed, with performance enhanced by up to 15%. The improved cross-batch performance of these models proves an enhanced model robustness using the presented multi-sensor fusion approach. The batch-based results were corroborated with a repeated nested cross-validation approach, to give an out-of-sample generalised view of model performance across the whole dataset. Overall, this work suggests potential avenues for performance improvements in real-world, minimally invasive food monitoring scenarios.