Physics of road cycling and the three jerseys problem

Cohen, Caroline; Brunet, Emmanuel; Roy, J; Clanet, Christophe

doi:10.1017/jfm.2020.1022

Cited by 6 publications

(8 citation statements)

References 37 publications

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“…Table 4 shows the profile of the diagnostic test that was tested on class XI high school students who had received dynamic fluid material. The concept of HOTS questions (higher order thinking skill) with thinking levels from C3 to C5 is used to diagnose students' misconceptions [44,36]. Based on the three-tier diagnostic test instrument that was tested, the results were obtained in Table 5.…”

Section: Resultsmentioning

confidence: 99%

The Profile of Misconceptions Using Three Tier Diagnostic Test on Dynamic Fluid

Aliyah

Sudibyo

Suyatno

et al. 2022

J. Phys.: Conf. Ser.

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Dynamic fluids have a stationary flow, where the volume of fluid flowing in each cross-section of the pipe in a specific time interval is the same even though the pipe is widening or narrowing but the fluid velocity is different. When a fluid moves through a section where the velocity and height above the surface change, the pressure in the fluid changes. Understanding led to errors in understanding the concept, so this research was carried out to identify the profile of misconceptions in a dynamic fluid material. Students misconceptions were detected using a three-tier diagnostic test with data analysis using the Certainty of Response Index (CRI). This research is quantitative descriptive research. The respondents of this study were students of class X of senior high school who had received dynamic fluid material. Based on the results of this research, showed that students experienced moderate category misconceptions on dynamic fluid material with the highest level of misconceptions, namely the concept of continuity equation analysis, and the lowest misconception in the analysis of the concept of water flow discharge.

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Section: Resultsmentioning

confidence: 99%

The Profile of Misconceptions Using Three Tier Diagnostic Test on Dynamic Fluid

Aliyah

Sudibyo

Suyatno

et al. 2022

J. Phys.: Conf. Ser.

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“…Heuristic fits for power-to-mass ratios may be employed using rider-specific data [15]. The development is the distance travelled in one rotation of the pedals, the product of the wheel perimeter and gear ratio [13].…”

Section: Forces On a Ridermentioning

confidence: 99%

“…The equations for position (15), velocity (16), work done (17), and the transition between pedalling and braking (20) comprise the coupled and nonlinear governing equations of the system. The forces, given by ( 6), ( 7), ( 8), (10), and ( 13), depend on position s, speed u , and an external factor, the road geometry.…”

Section: Equations Of Motionmentioning

confidence: 99%

“…For road cycling, weakly undulating courses in one dimension allow for analysis of riding dynamics, incorporating physiology for energy expenditure [14]. A further model considers the time evolution of the energy balance between kinetic and potential energy for external forces of mechanical power, drag, and rolling resistance [15]. This approach allows for comparison of pre-defined strategies for racing on different routes, but does not include braking at turns.…”

Section: Introductionmentioning

confidence: 99%

“…On a three-dimensional route, pacing and cornering strategies may be modelled based on differential geometry relating bicycle heading, lateral position on the road, road curvature, and velocity. Pedalling rates to maintain constant power output may be adjusted via gears to account for varying elevation [15,16]. Optimal control techniques are used to minimise time, steering, and power [17].…”

Section: Introductionmentioning

confidence: 99%

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Modelling road cycling as motion on a curve

Nee

Herterich

2022

Sports Eng

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We present a mathematical model of road cycling on arbitrary routes using the Frenet–Serret frame. The route is embedded in the coupled governing equations. We describe the mathematical model and numerical implementation. The dynamics are governed by a balance of forces of gravity, drag, and friction, along with pedalling or braking. We analyse steady-state speed and power against gradient and curvature. The centripetal acceleration is used as a control to determine transitions between pedalling and braking. In our model, the rider looks ahead at the curvature of the road by a distance dependent on the current speed. We determine such a distance (1–3 s at current speed) for safe riding and compare with the mean power. The results are based on a number of routes including flat and downhill, with variations in maximum curvature, and differing number of bends. We find the braking required to minimise centripetal acceleration occurs before the point of maximum curvature, thereby allowing acceleration by pedalling out of a bend.

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Exploratory analysis of cumulative fatigue derived from volume and intensity indicators in stage races of professional cycling

Machado,

De la Fuente,

Javaloyes

et al. 2024

Preprint

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Objective: Here, we discuss cumulative fatigue based on volume and power output from 12 professional male cyclists during two consecutive editions of the Giro d'Italia. Methods: Volume and power output were recorded and described according to time at different intensity zones based on power output (Z0 lower to Z7 higher). Correlations, principal component analysis (PCA), Gaussian clustering, and two-way ANOVA were performed (type error I of 5%). Results: The higher intensity zones elicited higher power output in those shorter stages (R2 = 0.54). In contrast, the lower intensity zones were predominant in longer stages. The time spent in Z1 to Z3 (r = 0.67, 0.84, and 0.73) correlated more with stage's volume duration than time in Z4 to Z7 (r = 0.48, 0.44, and 0.51, and 0.38). The normalized volume declined between stages 2 to 4, 8 to 10, 13 to 15, and 18 to 19. Negative slopes of time spent in Z4 and Z6 occurred one or two stages before Z1 presented negative slopes. In contrast, positive slopes of higher intensity zones were observed with a negative slope of Z1. Different clusters distributions of time volume to complete the grand tour were found (p<0.05). Finally, average power in Z1, Z2, Z3, Z4, Z5, Z6, and Z7 explained 63.62%, 18.20%, 8.12%, 5.84%, 2.98%, 1.20%, and 0.02% of the total variance in the normalized time volume, respectively. Volume and power zone data can recognize cumulative fatigue and performance recovery during a grand tour. Rest days favored the performance recovery, mostly the second rest day.

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Physics of road cycling and the three jerseys problem

Abstract: Abstract

Cited by 6 publications

References 37 publications

The Profile of Misconceptions Using Three Tier Diagnostic Test on Dynamic Fluid

The Profile of Misconceptions Using Three Tier Diagnostic Test on Dynamic Fluid

Modelling road cycling as motion on a curve

Exploratory analysis of cumulative fatigue derived from volume and intensity indicators in stage races of professional cycling

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