Aerodynamic benefits of drafting in speed skating: Estimates from in-field skater's wakes and wind tunnel measurements

Terra, Wouter; Spoelstra, Alexander; Sciacchitano, Andrea

doi:10.1016/j.jweia.2023.105329

Cited by 7 publications

(9 citation statements)

References 16 publications

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“…This approach proves particularly suitable for comparing aerodynamic variations among athletes under different speeds and wind conditions. For instance, the majority of recent studies investigating the drafting effect of humans in motion employ static models, encompassing a range of sports including cycling, running, swimming, and cross-country skiing [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. While static models cannot replicate all the intricacies and dynamics of real motion, they can still yield valuable insights and guidance through a thorough analysis of comprehensive experimental data and computational models.…”

Section: Numerical Simulation Methods Based On Computational Fluid Dy...mentioning

confidence: 99%

“…Drafting formation strategies are widely employed in various summer sports, such as cycling [1][2][3][4][5], swimming [6,7], and winter sports, such as short-track speed skating [8,9], speed skating [10,11], and cross-country skiing [12,13]. Rundell (1996) [8] conducted a study on drafting in short-track speed skating and reported positive effects on metabolic activity, heart rate, and lactate response.…”

Section: Introductionmentioning

confidence: 99%

“…Rundell (1996) [8] conducted a study on drafting in short-track speed skating and reported positive effects on metabolic activity, heart rate, and lactate response. Terra (2023) [10] evaluated the aerodynamic benefits of Appl. Sci.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Aerodynamic Drag Reduction Analysis of Race Walking Formations Based on CFD Numerical Simulations and Wind Tunnel Experiments

Zhang,

Ke,

Hong

2023

Applied Sciences

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Drafting formations have been long recognized as highly effective for reducing drag and enhancing athletic performance, particularly in race walking events. The precise spacing and positioning of the race walkers are critical to optimizing the effectiveness of drafting. In this study, drag reduction in 15 drafting formations is investigated using wind tunnel experiments and CFD numerical simulations. The results show excellent consistency in drag reduction rate between the two methods, with differences being within 10%. This can be attributed to spacing replacing body shape differences as the primary factor influencing drag reduction. Optimal double, triple, and quadruple drafting formations produce the same results in both the wind tunnel experiments and CFD simulation, resulting in drag reductions of 67%, 66%, and 81% (wind tunnel) and 65%, 72%, and 85% (CFD). The sources of drag differences in the two methods are discussed from various aspects. The flow field obtained through CFD analysis is used to examine the mechanism of drag reduction, revealing that drafting formations have a significant shielding effect on incoming air, which reduces the number and speed of airflow impacting the core race walker. This shielding effect is identified as the primary cause of drag reduction. Using an empirical model for mechanical power output, optimal double, triple, and quadruple drafting formations enhance sports economy (4.4–5.7%), speed (3.61–4.67%), and performance (173.8–223.3 s) compared to race walking alone. The findings can serve as a reference for race walkers’ positioning strategies and provide insights for considering drafting formations in various running events.

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Section: Numerical Simulation Methods Based On Computational Fluid Dy...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Aerodynamic Drag Reduction Analysis of Race Walking Formations Based on CFD Numerical Simulations and Wind Tunnel Experiments

Zhang,

Ke,

Hong

2023

Applied Sciences

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“…In recent years, the Ring of Fire system has been used to evaluate the drag of transiting objects such as spheres, cyclists and ice skaters (Terra el al (29), Spoelstra et al (25), Spoelstra et al (27), Terra et al (30)). The drag was computed by applying the conservation of momentum (Mohebbian and Rival, (17)) in a control volume enclosing the transiting object.…”

Section: Drag Calculationmentioning

confidence: 99%

On-Road Vehicle Aerodynamics with the Ring-of-Fire Concept

Hüttig,

Kühn,

Gericke

et al. 2024

Preprint

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This paper presents the first full-scale particle image velocimetry (PIV) measurements to analyze the complete flow field of a car under real driving conditions. The Ring of Fire (RoF) measurement concept, introduced by Terra et al. (Exp Fluids 58:83, 2017 https://doi.org/10.1007/s00348-017-2331-0), is adapted to automotive demands to validate CFD simulations for further improvements of vehicle aerodynamics. The experiment consists of a tunnel setup, where neutrally buoyant helium-filled soap bubbles are used as flow tracers and are illuminated by two high-speed lasers; four high-speed cameras captured the particles motion in two separate Stereo-PIV-configurations with fields of view of 1.3 x 0.6 m2 and 2.8 x 2.2 m2. Data for a Volkswagen up!, while driving on a test track at a constant speed of 33.33 m/s was acquired and processed with standard multi-pass PIV algorithms, in order to quantify the flow field and estimate limits of the described measurement principle for on-road car aerodynamics. The resulting velocity fields are compared with CFD-simulations, showing agreement for the here considered cases within 9.65 - 6.97%, based on the root-mean-square error between the experimental and the numerical results. Furthermore, drag calculation from the obtained velocity fields based on moment conservation is performed and the percent difference to wind tunnel measurements reaches values below 3.0%.

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“…The segmentation model demonstrates robustness across various objects, accurately detecting their position, area, and shape with solely textual inputs. Textual inputs are a) A flying owl[1], b) A cyclist and a bike[7], c) A skater[14], d) Particles[15], require fine-tuning similar to Appendix B, e) Sharks[16], f) A hydrofoil close to a wall[17].…”

mentioning

confidence: 99%

Practical Object and Flow Structure Segmentation using Artificial Intelligence

KHOJASTEH,

de Water,

Westerweel

2024

Preprint

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This paper explores integrating artificial intelligence (AI) segmentation models, particularly the Segment Anything Model (SAM), into fluid mechanics experiments. SAM's architecture, comprising an image encoder, prompt encoder, and mask decoder, is investigated for its application in detecting and segmenting objects and flow structures. Additionally, we explore the integration of natural language prompts, such as BERT, to enhance SAM's performance in segmenting specific objects. Through case studies, we found that SAM is robust in object detection in fluid experiments. However, segmentations related to flow properties, such as scalar turbulence and bubbly flows, require fine-tuning. To facilitate the application, we have established a repository (\href{https://github.com/AliRKhojasteh/Flow_segmentation}{github.com/AliRKhojasteh/Flow\_segmentation}) where models and usage examples can be accessed.

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Aerodynamic benefits of drafting in speed skating: Estimates from in-field skater's wakes and wind tunnel measurements

Cited by 7 publications

References 16 publications

Aerodynamic Drag Reduction Analysis of Race Walking Formations Based on CFD Numerical Simulations and Wind Tunnel Experiments

Aerodynamic Drag Reduction Analysis of Race Walking Formations Based on CFD Numerical Simulations and Wind Tunnel Experiments

On-Road Vehicle Aerodynamics with the Ring-of-Fire Concept

Practical Object and Flow Structure Segmentation using Artificial Intelligence

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