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Record W2192780170 · doi:10.28985/jsc.v4i2.194

Validity of Track Aero System to assess aerodynamic drag in professional cyclists

2015· article· en· W2192780170 on OpenAlexaboutno aff
Anthony Bouillod, Julien Pinot, Andy Froncioni, F. Grappe

Bibliographic record

VenueJournal Of Science & Cycling · 2015
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
Fundersnot available
KeywordsDragAerodynamic dragAerodynamicsDrag coefficientMathematicsPosition (finance)SimulationPhysicsComputer scienceMechanics

Abstract

fetched live from OpenAlex

Purpose : Aerodynamic drag is the main resistance (80- 90 %) among the total resistive forces (RT, N) opposing motion on level ground in cycling. It is important to decrease it to improve the performance in time- trial (TT). In order to decrease the aerodynamic resistance, it is important to evaluate it accurately (Debraux et al., 2011). Even if the wind tunnel is considered as a reference due to its reliability and sensitivity in detecting small changes in aerodynamic drag, this technique is very expensive and presents some methodological limitations. Garcia- Lopez et al. (2014) demonstrated the validity of velodrome tests for evaluating aerodynamic drag. Following the evolution of technology in sport science, a Track Aero System (TAS, Alphamantis, Montreal, Canada) has been recently created to display the aerodynamic drag coefficient (ACd, m2) in real- time. The aim of this study was to compare the measurement of ACd between the TAS and the classical indirect method (Grappe et al., 1997; Garcia- ‐ Lopez et al., 2014). Methods : 9 professional road cyclists exercised with their personal TT bike on a covered velodrome (Roubaix, France). Three sets of measurements were performed in different positions: 1) reference position, 2) position after a first change and 3) position after a second change (to try to again decrease ACd). For each position, the cyclists performed an incremental exercise at different speeds (V), performing a total of 37 laps. ACd was determined from the classical indirect method with the RT ‐ V2 linea regression(Grappe et al., 1997) and from the TAS. RT was determined from the measurement of PO (W) and speed (m.s ‐ 1) with a SRM powermeter (SRM Dura Ace 9000, Schoberer Rad Messtechnich, Julich, Germany). The TAS combined wireless timing measurements, the cyclists' speed and power sensors. Moreover, it takes into account ride height, drive train efficiency and the track geometry. Results : For each tested position, a significant difference was found between the ACd measured with the TAS and the indirect method (p < 0.001). Fig. 1 shows that ACd was underestimated with the indirect method. The mean value for the 3 tested positions was lower (- 13.6 %, p < 0.001) with the indirect method (0.199 ± 0.013 m2) compared to the TAS (0.226 ± 0.010 m2).The ACd was not significantly affected by the position changes. However, a strong correlation (r = 0.94, p < 0.001) was found between the ACd changes in the two methods. The mean change was similar between the TAS and the indirect method ( ‐ 1.6 ± 3.1 % vs. ‐ 1.5± 2.8 % respectively). Conclusions : This is the first study that analyse the validity and sensitivity of the TAS for assessing ACd during real-- ‐ time cycling locomotion on velodrome. The results demonstrate a high sensitivity with the TAS and the indirect method to detect small changes in aerodynamic position. Other studies performed with portable power meters have also demonstrated a high sensitivity in detecting major/minor changes in aerodynamic positions (Grappe et al., 1997; Garcia ‐ Lopez et al., 2014). Additionally, the results show that the indirect method underestimated ACd. The TAS computed the energy stored in the curve using track geometry. Indeed, in the velodrome curves, the centrifugal force increases V and decreases PO. This induces a decrease in ACd with the indirect method while the TAS took into account this physical phenomenon.The TAS is a very valuable and relevant system that allows the ACd measurements in real time with a high validity and sensitivity.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.108
GPT teacher head0.389
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2015
Admission routes1
Has abstractyes

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