Dr Daniel Bell

The long jump is a track and field event in which the athlete sprints down a runway and tries to leap as far as possible from a take-off line. To the best of our knowledge, there are no published studies on the aerodynamic impact of jump style, hairstyle and clothing on the long jump distance. This paper presents a numerical-physical model of the long jump flight. It allows to predict flight distance and the impact of jump style, hairstyle and clothing. It consists of five submodels: an existing model of the sprint before take-off, a computational fluid dynamics (CFD) model of different body postures in flight, a set of physical wind tunnel models for CFD validation, a full-scale wind tunnel manikin with different hairstyles and clothing and a numerical model of the flight trajectory. Jump style only impacts flight distance by 1 cm or less. Hairstyle and clothing however can cause drag to vary by more than 25% and flight distance by more than 10 cm, mostly by impacting the take-off speed. In the long term, long jump events might see the introduction of hair caps and low-drag clothing to reduce aerodynamic resistance and level the playing field.

The meniscal tissue is a layered material with varying properties influenced by collagen content and arrangement. Understanding the relationship between structure and properties is crucial for disease management, treatment development, and biomaterial design. The internal layer of the meniscus is softer and more deformable than the outer layers, thanks to interconnected collagen channels that guide fluid flow. To investigate these relationships, we propose an integrated approach that combines Computational Fluid Dynamics (CFD) with Image Analysis (CFD-IA). We analyze fluid flow in the internal architecture of the human meniscus across a range of inlet velocities (0.1 mm/s to 1.6 m/s) using high-resolution 3D micro-computed tomography scans. Statistical correlations are observed between architectural parameters (tortuosity, connectivity, porosity, pore size) and fluid flow parameters (Re number distribution, permeability). Some channels exhibit Re values of 1400 at an inlet velocity of 1.6 m/s, and a transition from Darcy’s regime to a non-Darcian regime occurs around an inlet velocity of 0.02 m/s. Location-dependent permeability ranges from 20-32 Darcy. Regression modelling reveals a strong correlation between fluid velocity and tortuosity at high inlet velocities, as well as with channel diameter at low inlet velocities. At higher inlet velocities, flow paths deviate more from the preferential direction, resulting in a decrease in the concentration parameter by an average of 0.4. This research provides valuable insights into the fluid flow behaviour within the meniscus and its structural influences. 3D models and image stack are available to download at https://doi.org/10.5281/zenodo.10401592

A novel calibration methodology is presented to accurately predict the fundamental characteristics of high-pressure fuel sprays for Gasoline Direct Injection (GDI) applications. The model was developed within the Siemens Simcenter STARCD 3D CFD software environment and used the Lagrangian–Eulerian solution scheme. The simulations were carried out based on a quiescent, constant volume, computational vessel to reproduce the real spray testing environment. A combination of statistic and optimisation methods was used for spray model selection and calibration and the process was supported by a wide range of experimental data. A comparative study was conducted between the two most commonly used models for fuel atomisation: Kelvin–Helmholtz/Rayleigh–Taylor (KH–RT) and Reitz–Diwakar (RD) break-up models. The Rosin–Rammler (RR) mono-modal droplet size distribution was tuned to assign initial spray characteristics at the critical nozzle exit location. A half factorial design was used to reveal how the various model calibration factors influence the spray properties, leading to the selection of the dominant ones. Numerical simulations of the injection process were carried out based on space-filling Design of Experiment (DoE) schedules, which used the dominant factors as input variables. Statistical regression and nested optimisation procedures were then applied to define the optimal levels of the model calibration factors. The method aims to give an alternative to the widely used trial-and-error approach and unveils the correlation between calibration factors and spray characteristics. The results show the importance of the initial droplet size distribution and secondary break-up coefficients to accurately calibrate the entire spray process. RD outperformed KH–RT in terms of prediction when comparing numerical spray tip penetration and droplet size characteristics to the experimental counterparts. The calibrated spray model was able to correctly predict the spray properties over a wide range of injection pressure. The work presented in this paper is part of the APC6 DYNAMO project led by Ford Motor Company.

Aerodynamic drag reduction is a critical part in the design of a novel electric, entry level, formula car due to the modest energy density provided by the contemporary Lithium-ion battery cells. In order to improve track performance, aerodynamic development must focus on components which do not generate considerable amount of downforce. Rotating front wheels are identified as the least aerodynamic part of the race car, since it is responsible for the third of the overall drag forces and producing moderate amounts of lift. In the present study, a parameterised wheel pod geometry is used to improve the overall aerodynamic performance of an open wheel race car. The model is driven by seven parameters, which entails huge flexibility of the bodywork design. First, an unsteady Computational Fluid Dynamics (CFD) simulation was developed and validated to visualise the oscillating flow behaviour and obtain averaged surface force measurements. In expectation of a highly turbulent and complex wheel wake structure, the traditional iterative optimisation method had to be excluded. The exploration of the optimal bodywork was executed by a single objective aerodynamic optimisation framework, coupled with CFD simulations. The objective target was to minimise overall drag force while the simulation must agree on the pre-set restrictions and leave downforce unharmed. SHERPA search algorithm was applied along with Kriging response surface surrogate modelling. The convergence of the objective history indicated acceptable fidelity of the predictions for the location of the global minimum of the 8-dimension design space. The race car equipped with the best design wheel pod generated 11.1% less overall drag, while aerodynamic efficiency was improved by 17.1%.