Prediction of passive and active drag in swimming

Webb, A.; Banks, Joseph; Phillips, Christopher W.G.; Hudson, Dominic A.; Taunton, D.J.; Turnock, S.R.

doi:10.1016/j.proeng.2011.05.063

Cited by 17 publications

(35 citation statements)

References 5 publications

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“…There are a set of mathematical models that can be used to have some insight about a vessel hydrodynamics (e.g., sailing, canoeing, kayaking, surfing, rowing). Recently, that set of mathematical models was adapted to human swimming [ 13 ]. However, it remains to be shown how valid and reliable these procedures are.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming

et al. 2015

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The aim of this study was to compare the swimming hydrodynamics assessed with experimental and analytical procedures, as well as, to learn about the relative contributions of the friction drag and pressure drag to total passive drag. Sixty young talented swimmers (30 boys and 30 girls with 13.59±0.77 and 12.61±0.07 years-old, respectively) were assessed. Passive drag was assessed with inverse dynamics of the gliding decay speed. The theoretical modeling included a set of analytical procedures based on naval architecture adapted to human swimming. Linear regression models between experimental and analytical procedures showed a high correlation for both passive drag (Dp = 0.777*Df+pr; R2 = 0.90; R2 a = 0.90; SEE = 8.528; P<0.001) and passive drag coefficient (CDp = 1.918*CDf+pr; R2 = 0.96; R2 a = 0.96; SEE = 0.029; P<0.001). On average the difference between methods was -7.002N (95%CI: -40.480; 26.475) for the passive drag and 0.127 (95%CI: 0.007; 0.247) for the passive drag coefficient. The partial contribution of friction drag and pressure drag to total passive drag was 14.12±9.33% and 85.88±9.33%, respectively. As a conclusion, there is a strong relationship between the passive drag and passive drag coefficient assessed with experimental and analytical procedures. The analytical method is a novel, feasible and valid way to gather insight about one’s passive drag during training and competition. Analytical methods can be selected not only to perform race analysis during official competitions but also to monitor the swimmer’s status on regular basis during training sessions without disrupting or time-consuming procedures.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming

et al. 2015

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“…A large body of research has focused on quantifying the passive drag of a swimmer [2]. However, there has been less focus on quantifying the magnitude of wave resistance which contributes to the total passive drag that acts on a swimmer: 1. experimental techniques using a flume or towing swimmers or mannequins [15,1,13], 2. numerical simulations [16], and 3. analytical methods drawing from naval architectural techniques [17,13,14]. Of specific interest 90 is how these methods are employed to investigate the influence of wave resistance on the passive drag of a swimmer.…”

Section: Passive Resistance Of Swimmersmentioning

confidence: 99%

Quantifying the wave resistance of a swimmer

Dickson

Turnock

Hudson

et al. 2020

Preprint

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Quantifying the wave resistance of a swimmer as a function of depth assists in identifying the optimum depth for the glide phases of competition. Previous experiments have inferred how immersed depth influences the drag acting on a swimmer [1], but have not directly quantified the magnitude of wave resistance. This research experimentally validates the use of thin-ship theory for quantifying the wave resistance of a realistic swimmer geometry. The drag and wave pattern of a female swimmer 5 mannequin were experimentally measured over a range of depths from 0.05m to 1.00m at a speed of 2.50 m/s. Numerical simulations agree with experiment to confirm that there were negligible reductions in wave resistance below a depth of 0.40m. Larger swimming pool dimensions are shown to be significant at reducing wave resistance at speeds above 2.0 m/s and depths below 0.40m.Truncating the swimmer's body at the upper thigh increases the wave resistance at speeds below 10 2.0m/s but is not significant at higher speeds, indicating that the upper body is the main contributor to the wave system. Numerical experiments indicate that rotating the shoulders towards the surface is more influential than the feet, demonstrating the impact of the upper body on wave resistance.

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“…They also used k- model in 3D-CFD numerical simulation for calculating wall shear stresses near body and showed that the values will be increased in rigid body regions like head and shoulders of swimmer. Prediction of passive and active drag in swimming has been done by Webb (2011). He combined the empirical methods and theoretical analysis to predict passive resistance in the speed range up to 2 ms -1 and the results have been compared with experimental tests.…”

Section: Introductionmentioning

confidence: 99%

Experimental and Numerical Investigation of Drag Force at High Speed Diver Motion in Different Depths from Free Surface

Sadeghizadeh¹,

Saranjam²,

Kamali³

2017

JAFM

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In this study, the drag force exerted on swimmer body is investigated by computational fluid dynamic (CFD) and then the results validity are compared with towing tank experiments. The main target of this research is swimmer thruster power evaluation to reach the high speed motion underwater. In the last decade, the low speed swimmer motion has been studied by researchers for sport purposes and improvement of diving time record, while in this research authors have tried to increase swimmer speed up to 8 ms-1 by use of electrical thruster system. The numerical simulations have been done by computational fluid dynamics considering three dimensional two phase turbulent flow in the base of Volume of Fluid (VOF) method. The geometry of the swimmers' bodies was generated by 3-D scanning and image modeling of real swimmers in experimental diving test. Different turbulent models could be applied in specific case but the favorable one (k- method) is selected to estimate the forces on the swimmers. The experiments were programed by five swimmers and one mannequin for different depths and speeds. The numerical simulation results showed a good agreement with the experimental output data up to 8 ms-1 but more speed test results were not accessible because of lab limitations. The results showed whatever swimmer go to deeper depths up to certain value the drag force exerted to him will be reduced. This work introduced asseveration about maximum swimmer speed test worldwide that is twice the other work heretofore.

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Prediction of passive and active drag in swimming

Cited by 17 publications

References 5 publications

A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming

A Comparison of Experimental and Analytical Procedures to Measure Passive Drag in Human Swimming

Quantifying the wave resistance of a swimmer

Experimental and Numerical Investigation of Drag Force at High Speed Diver Motion in Different Depths from Free Surface

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