Drafting Effect in Cycling: On-Site Aerodynamic Investigation by the ‘Ring of Fire’

Spoelstra, Alexander; Mahalingesh, Nikhil; Sciacchitano, Andrea

doi:10.3390/proceedings2020049113

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…Ring of Fire (RoF) Sciacchitano et al (2015), Terra et al (2017), Terra et al (2018), Spoelstra et al (2018a), Spoelstra et al (2018b), Spoelstra et al (2019), Spoelstra et al (2020), Spoelstra et al (2021), Hüttig et al (2023), Hüttig et al (2024), Spoelstra et al (2023) is a novel quantitative measurement technique that can provide insights into the flow topology of full-scale, transiting objects and determine their aerodynamic drag. It is a non-intrusive, large-scale, stereoscopic PIV measurement technique that can be implemented for a full three-dimensional flow field description.…”

Section: Ring Of Firementioning

confidence: 99%

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Elrefaie,

Hüttig,

Gladkova

et al. 2024

Preprint

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We introduce Recurrent All-Pairs Field Transforms for Stereoscopic ParticleImage Velocimetry (RAFT-StereoPIV). Our approach leverages deep optical flowlearning to analyze time-resolved and double-frame particle images from on-sitemeasurements, particularly from the 'Ring of Fire,' as well as from wind tunnelmeasurements for real-time aerodynamic analysis. A multi-fidelity datasetcomprising both Reynolds-Averaged Navier-Stokes (RANS) and Direct NumericalSimulation (DNS) was used to train our model. RAFT-StereoPIV outperforms allPIV state-of-the-art deep learning models on benchmark datasets, with a 68% error reduction on the validation dataset, Problem Class 2, and a 47% errorreduction on the unseen test dataset, Problem Class 1, demonstrating itsrobustness and generalizability. In comparison to the most recent works in thefield of deep learning for PIV, where the main focus was the methodologydevelopment and the application was limited to either 2D flow cases or simpleexperimental data, we extend deep learning-based PIV for industrialapplications and 3D flow field estimation. As we apply the trained network tothree-dimensional highly turbulent PIV data, we are able to obtain flowestimates that maintain spatial resolution of the input image sequence. Incontrast, the traditional methods produce the flow field of~16 × lower resolution. We believe that this study brings the field of experimentalfluid dynamics one step closer to the long-term goal of having experimentalmeasurement systems that can be used for real-time flow field estimation.

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Section: Ring Of Firementioning

confidence: 99%

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Elrefaie,

Hüttig,

Gladkova

et al. 2024

Preprint

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“…The improvement of the performance of cyclists and their equipment has been the aim of many past research efforts (e.g., [3,[8][9][10][11][12][13][14][15][16][17]). Several studies compared the aerodynamic performance of different types of wheels including spoked, tri-spoke, and disk wheels, either isolated (e.g., [5,7,[18][19][20][21][22][23][24]) or together with the rest of the bicycle (e.g., [25,26]).…”

Section: Introductionmentioning

confidence: 99%

Impact of Disc Wheel Geometry on Aerodynamic Performance: A Computational Fluid Dynamics Investigation

Malizia

Blocken

2022

Journal of Fluids Engineering

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Aerodynamic drag is the main resistive force in cycling at high speeds and on flat terrain, so reducing it is critical to improve cyclist performance. Aerodynamic comparisons have been made in the past between different types of wheels, and disc wheels were often the best performers. However, to the best of our knowledge, there are no studies in the available literature on how modifications to the disc wheel geometry can improve its performance. Therefore, this paper studies and compares the aerodynamics of disc wheels with flat side discs and with curved side discs (lenticular). Moreover, semi-lenticular front wheels with an asymmetrical shape are introduced. All but one of the simulated (semi )lenticular wheels perform better than the flat disc wheels at all yaw angles, e.g., the maximum CD reduction was 5.5%, 10.9% and 87.5% at 0°, 4° and 8° yaw angle, respectively. Semi-lenticular wheels provide a lower CD at medium and large yaw angles compared to corresponding symmetric lenticular wheels. Moreover, the large influence of the ratio of tire width to wheel width on the aerodynamic drag of cycling wheels is confirmed. These results will help riders in their wheels choice and will help manufacturers to design future disc wheels.

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Cyclist aerodynamics through time: Better, faster, stronger

Malizia

Blocken

2021

Journal of Wind Engineering and Industrial Aerodynamics

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Drafting Effect in Cycling: On-Site Aerodynamic Investigation by the ‘Ring of Fire’

Cited by 3 publications

References 11 publications

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Real-time and On-site Aerodynamics using Stereoscopic PIV and Deep Optical Flow Learning

Impact of Disc Wheel Geometry on Aerodynamic Performance: A Computational Fluid Dynamics Investigation

Cyclist aerodynamics through time: Better, faster, stronger

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