CFD simulations of spoked wheel aerodynamics in cycling: Impact of computational parameters

Malizia, Fabio; Montazeri, Hamid; Blocken, Bje Bert

doi:10.1016/j.jweia.2019.103988

Cited by 27 publications

(19 citation statements)

References 34 publications

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“…The ground velocity was set to the same as the inlet velocity. The wheel rotation was modeled with the hybrid MRF-RW approach [rotating wall (RW)] [20]. The rotational velocity applied to the cells inside the MRF volume and on the wheel components outside this volume (tire, outer rim and outer hub) was equal to ω = U/r.…”

Section: Boundary Conditions and Rotation Modeling Approachmentioning

confidence: 99%

“…An overview on cycling wheel aerodynamics was provided in [7,8], whereas specific wind tunnel (WT) and computational fluid dynamics (CFD) studies on cycling wheels are reported in Tables S1 and S2 in the supplementary material, respectively. The majority of these studies have been performed on isolated wheels and they focused on comparisons of the drag of different wheels [6,[9][10][11][12][13][14][15], on the aerodynamic drag reduction by adding external elements like claddings or splitter plates [16,17], on the aerodynamic influence of the tire width to rim width ratio [18], the impact of wheel rotation on the aerodynamic drag [16,19], the influence of the rotational moment on the total power required to move the wheel [11] and the influence of computational parameters and the type of wheel/ground contact modeling to the aerodynamics of spoked wheels [20][21][22]. Only a few studies have focused on the aerodynamics of non-isolated cycling wheels, including those by Godo et al [23], Barry et al [24] and Petrone et al [25].…”

Section: Introductionmentioning

confidence: 99%

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Impact of wheel rotation on the aerodynamic drag of a time trial cyclist

2021

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Aerodynamic drag is the main resistive force in cycling at high speeds and on flat terrain. In wind tunnel tests or computational fluid dynamics simulations, the aerodynamic drag of cycling wheels is often investigated isolated from the rest of the bicycle, and sometimes in static rather than rotating conditions. It is not yet clear how these testing and simulating conditions influence the wheel aerodynamic performance and how the inclusion of wheel rotation influences the overall measured or computed cyclist drag. This study presents computational fluid dynamics simulations, validated with wind tunnel tests, that indicate that an isolated static spoked front wheel has a 2.2% larger drag area than the same wheel when rotating, and that a non-isolated static spoked front wheel has a 7.1% larger drag area than its rotating counterpart. However, rotating wheels are also subjected to the rotational moment, which increases the total power required to rotate and translate the wheel compared to static conditions where only translation is considered. The interaction with the bicycle frame and forks lowers the drag area of the front wheel by 8.8% for static and by 12.9% for the rotating condition, compared to the drag area of the isolated wheels. A different flow behavior is also found for static versus rotating wheels: large low-pressure regions develop from the hub for rotating wheels, together with a lower streamwise velocity region inside the circumference of the wheel compared to static wheels. The results are intended to help in the selection of testing/simulating methodologies for cycling spoked wheels.

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Section: Boundary Conditions and Rotation Modeling Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of wheel rotation on the aerodynamic drag of a time trial cyclist

2021

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“…The computational grids were hybrid hexahedral-tetrahedral grids based on grid convergence analysis (not reported here) and on grid generation guidelines [16][17][18][19][20][21][22][23][24]. They had a wall-adjacent cell size of 20 µm (= 0.02 mm) and 40 layers of prismatic cells near the cyclist and motorcycle surfaces to accurately resolve the thin boundary layer including the laminar sublayer (Fig.…”

Section: Computational Settings and Parametersmentioning

confidence: 99%

Impact of a motorcycle on cyclist aerodynamic drag in parallel and staggered arrangements

et al. 2021

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Cycling races contain a multitude of motorcycles for various activities including television broadcasting. During parts of the race, these motorcycles can ride in close proximity of cyclists. Earlier studies focused on the impact of a nearby motorcycle on cyclist drag for in-line arrangements. It was shown that not only a motorcycle in front of a cyclist but also a motorcycle closely behind a cyclist can substantially reduce cyclist drag. However, there appears to be no information in the scientific literature about the impact of the motorcycle on cyclist drag for parallel and staggered arrangements. This paper presents wind tunnel measurements of cyclist drag for 32 different parallel and staggered cyclist-motorcycle arrangements. It is shown that the parallel arrangement leads to a drag increase for the cyclist, in the range of 5 to about 10% for a lateral distance of 2 to 1 m. The staggered arrangement can lead to either a drag increase or a drag decrease, where the latter is about 2% for most positions analyzed. For one of the parallel arrangements, computational fluid dynamics simulations were performed to provide insight into the reasons for the drag increase. A cyclist power model was used to convert the drag changes into potential time gains or losses. Compared to a lone cyclist riding at a speed of 46.8 km/h (13 m/s) on level road in calm weather, the time loss by a drag increase of 10%, 4% and − 2% was 2.16, 0.76 s and − 0.80 s per km, respectively. These time differences are large enough to influence the outcome of cycling races.

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“…Zdravkovich, 1992;Greenwell et al, 1995;Tew and Sayers, 1999), whereas flow topology data remains undocumented to the best of the authors' knowledge. Such data is readily available from computational fluid dynamics (CFD) analysis, which presents an active line of research in the context of cycling wheel aerodynamics (Godo et al, 2010;Malizia et al, 2019;Malizia and Blocken, 2020b). Authors developing numerical tools for the aerodynamic analysis of cycling wheels, however, have to rely on the experimentally documented force data for validation of their simulations.…”

Section: Introductionmentioning

confidence: 99%

Tracer-based 3D surface reconstruction

Jux¹,

Scarano²,

Sciacchitano³

2021

ISPIV21

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The continuous advancements of particle imaging techniques for flow field measurements have led to imaging systems and processing approaches matching the demands for 3D velocimetry at large scale (Schanz et al., 2016; Discetti and Coletti, 2018). Often, the flow past an object immersed in the fluid is of key interest, and in some cases the experimentalist exploits the velocimetry data for analysis of the near-surface flow properties such as pressure. It follows that knowledge of the object shape and position is essential. For 2D studies, the issue of identifying the fluid-solid interface often reduces to detection of the intensity gradient resulting from the light sheet striking the object. The latter task is well explored, with a variety of methods providing the object interface in the measurement plane (Canny, 1986; Malik et al., 2001; Gui et al., 2003, among others). These approaches, however, are not applicable in volumetric studies where the illumination is diffuse. A frequently applied alternative in fluid-structure-interaction studies is a dual-measurement approach, where a second measurement system tracks the object shape (e.g., Acher et al., 2019; Zhang et al., 2019) The complexity of operating two measurement systems may not be affordable however, motivating the development of 3D interface detection methods that rely solely on the flow imaging system. Particle imaging based interface detection approaches in 3D have been addressed from various perspectives, containing the detection of fluid-fluid interfaces. Examples utilizing tomographic PIV measurements include the studies of Adhikari and Longmire (2012), Im et al. (2014), and Ebi and Clemens (2016). The two latter examples identify the fluid-solid interface by discriminating a seeded phase (the fluid) from a void phase (the solid). The present work is inspired by this principle, but it assumes a discrete 3D particle distribution as obtained from a generic particle tracking algorithm such as IPR by Wieneke (2012), or “Shake-The-Box” by Schanz et al. (2016), as a foundation. Summarizing, this work aims to detect the surface of a solid object immersed in a seeded flow, solely based on the spatial distribution of flow tracers as recorded by a generic 3D PTV measurement.

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CFD simulations of spoked wheel aerodynamics in cycling: Impact of computational parameters

Cited by 27 publications

References 34 publications

Impact of wheel rotation on the aerodynamic drag of a time trial cyclist

Impact of wheel rotation on the aerodynamic drag of a time trial cyclist

Impact of a motorcycle on cyclist aerodynamic drag in parallel and staggered arrangements

Tracer-based 3D surface reconstruction

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