Contamination of vehicle rear surfaces is a significant issue for customers. Along with being unsightly, it can degrade the performance of rear camera systems and lighting, prematurely wear rear screens and wipers, and transfer soil to customers moving goods through the rear tailgate. Countermeasures, such as rear camera wash or automated deployment add expense and complexity for OEMs. This paper presents a rear surface contamination model for a fully detailed SUV based on the use of a highly-resolved time-accurate aerodynamic simulation realised through the use of a commercial Lattice-Boltzmann solver, combined with Lagrangian Particle Tracking to simulate droplet advection and surface water dynamics via a thin film model. Droplet break-up due to aerodynamic shear is included, along with splash and stripping from the surface film. The effect of two-way momentum coupling is included in a sub-set of simulations.The simulations are qualitatively validated in terms of surface contamination distribution against full scale (climatic) wind tunnel experiments using a UV fluorescent dye in water introduced onto dynamometer rollers.Three different contamination source configurations are examined: front tyres only, rear tyres only and all four tyres. The simulation is seen to recover the experimental rear surface contamination distribution, along with the trend in differences between these cases.The rear tyres are seen to be the dominant source, with front wheel spray and front wheel rotation only making small relative contributions.Two-way momentum coupling is seen to influence the rear surface contamination distribution.

Cooling drag, typically known as the difference in drag coefficient between open and closed cooling configurations, has traditionally proven to be a difficult flow phenomenon to predict using computational fluid dynamics. It was seen as an academic yardstick before the advent of grille shutter systems. However, their introduction has increased the need to accurately predict the drag of a vehicle in a variety of different cooling configurations during vehicle development. This currently represents one of the greatest predictive challenges to the automotive industry due to being the net effect of many flow field changes around the vehicle. A comprehensive study is presented in the paper to discuss the notion of defining cooling drag as a number and to explore its effect on three automotive models with different cooling drag deltas using the commercial CFD solvers; STARCCM+ and Exa PowerFLOW. The notchback DrivAer model with under-hood cooling provides a popular academic benchmark alongside two fully-engineered production cars; a large saloon (Jaguar XJ) and an SUV (Land Rover Range Rover). Initially three levels of spatial discretization were used with three steady-state RANS solvers (k-ɛ realizable, k-ω SST and Spalart-Allmaras) to ascertain whether previous work using RANS on the large saloon studying cooling flows could be replicated on other vehicle shapes. For both the full-production vehicles, all three turbulence models were capable of predicting the cooling drag delta within 5 counts (0.005 C d). However, the DrivAer model was much more sensitive to both changes in turbulence models and mesh sizes. For the SA turbulence model only the drag coefficient was well predicted, for the other two RANS models no amount of grid refinement allowed the models to correctly predict the flow field. It was seen when comparing the k-ɛ realizable and SA turbulence models the difference in cooling drag was attributed to the rear of the vehicle. This highlighted that despite similar drag values from the cooling package, the cooling deltas were very different, suggesting that cooling drag cannot be thought of as open-closed drag with the addition of drag due to the cooling package. Further work on the DrivAer model expanded on the RANS simulations utilizing the eddy-resolving methods, IDDES and LBM, as validation cases. Oscillations which were seen in the SA and k-ω SST RANS turbulence models were shown to be of similar levels to those in the transient methods indicating a pseudo-unsteadiness present in the steady-state solvers and the importance of resolving it. Drag and lift coefficient absolute values were compared showing that only the IDDES method with sliding wheels and LBM method could obtain physical results for the majority of the tested criteria.

The aerodynamic efficiency of an elite cyclist is often evaluated and optimised using either one or a combination of field testing, wind-tunnel testing and numerical simulation. This study focuses on the processes and limitations involved in using a body scan to produce an accurate geometry for input to numerical simulation, with validation through drag comparisons from wind-tunnel tests and vortical wake-flow features reported in previous experimental studies. Transitional Shear Stress Transport Reynolds-Averaged Navier-Stokes simulations based on the scanned geometry were undertaken for a 180 ° half crank cycle at 15 ° increments. The sectional drag force contributions of 23 body subparts are presented, documenting the contribution and variation of each body/cycle component over the cycle. These methods are evaluated and the limitations of the approaches are discussed. The results from the numerical simulation and the wind tunnel measured drag force were very similar, differing by approximately 1%–7% for various crank angles.

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Aerodynamics is an important factor affecting cyclist performance, as at the elite level 90% of rider energy is used to overcome aerodynamic drag. As such, much effort has been channeled into understanding the detailed flow around cyclists, since small gains can produce large rewards. Previous studies have shown that cycling aerodynamic drag is sensitive to leg position during the pedaling cycle; however, a systematic analysis comparing the impact of leg position between different riding postures is yet to be undertaken. To address this question, we compare the impact of leg position for two elite-level riding postures: the standard sprint and pursuit body positions. The comparison shows that the effect of leg position on drag is not consistent between the two riding postures, as the altered flow associated with different leg positions is influenced by the wakes from and proximity of other upstream or nearby components, such as the arms. This study reveals the inter-relationship between leg position and riding posture; and suggests that the flow associated with varied leg position should include surrounding geometrical components to obtain and understand the full aerodynamic impact. Practically, the results are valuable for optimizing the posture and improving skin-suit design for drag minimization.

Background Movement is a core mechanism through which animals interact with their environment. GPS telemetry is a popular approach used to investigate animal movement, providing access to both the spatial and temporal behavioral patterns exhibited by an individual or population. However, while some species are easily tracked through traditional GPS attachment methods (such as GPS collars or backpacks), other species such as the North American beaver (Castor canadensis) present unique challenges given their fusiform shape and tapered neck. Results We tested three different GPS transmitter attachment methods (tail-mounted, lower back glued-on, and upper back glued-on) for beavers over two seasons to determine which treatment was most effective in terms of retention time (RT, total number of days a transmitter remains attached) and GPS fix success rate (FSR, % of successful fixes vs. attempted) and investigated to what degree various factors (season, sex, and age class) affected these results. We then evaluated whether the data collected were sufficient for identifying home-ranging behavior (when an individual begins to display restricted space use and range residency). We found transmitters attached to the lower back during the fall to be the top performing treatment, having a similar mean FSR (51.59%) to upper back attachments in fall, but a significantly greater average RT (42.8 days). Of the 23 individuals included in the home-ranging behavior analysis, all but two had sufficient data for identifying home-ranging behavior. Conclusions Our tests show that glued-on GPS tags can provide up to 2 months of fine-scale relocation data in a safe and effective manner. This allows the opportunity to answer novel questions regarding movement patterns of beavers and other semi-aquatic mammals.