東華大學圖書館 |

Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery./
作者:	Ashtiani, Faraz.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	113 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Mechanical engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647608
ISBN:	9798544279143

Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery.
Ashtiani, Faraz.

Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 113 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--Clemson University, 2021.

This item must not be sold to any third party vendors.

This dissertation pushes on the boundaries of improving human performance through individualized models of fatigue and recovery, and optimization of human physical effort. To that end, it focuses on professional cycling and optimal pacing of cyclists on hilly terrains during individual and team time trial competitions. Cycling is an example of physical activity that provides nice interfaces for real-time performance measurement through inexpensive power meters, and for providing real-time feedback via a mobile displays such as smartphones. These features enable implementing a real-time control system that provides the optimal strategy to cyclists during a race.There has been a lot of work on understanding the physiological concepts behind cyclists' performance. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, can be continued until the nutrients end in the cyclist's body. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP.In this dissertation, we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of cyclists. We use experimental data from six human subjects to validate and calibrate our proposed dynamic models. The recovery of burned energy from AWC has a slower rate than expending it. The proposed models capture this difference for each individual subject. In addition to the anaerobic energy dynamics, maximum power generation of cyclists is another limiting factor on their performance. We show that the maximum power is a function of both a cyclist's remaining anaerobic energy and the bicycle's speed.These models are employed to formulate the pacing strategy of a cyclist during a time trial as an optimal control problem. Via necessary conditions of Pontryagin's Minimum Principle (PMP), we show that in a time trial, the cyclist's optimal power is limited to only four modes of maximal effort, no effort, pedaling at critical power, or pedaling at constant speed. To determine when to switch between these four modes, we resort to numerical solution via dynamic programming. One of the subjects is then simulated on four courses including that of the 2019 Duathlon National Championship in Greenville, SC. The simulation results show reduced time over experimental results of the self-paced subject who is a competitive amateur cyclist.Moreover, we expand our optimal control formulation from an individual time trial to a Team-Time-Trial (TTT) competition. In a TTT cyclists on each team follow each other closely. This enables the trailing cyclists to benefit from a rather significant reduction in drag force. Through a case study for a two-cyclist team, we show that the distance between the cyclists does not need to be variable and can be set at the minimum safe gap. We then use this observation to formulate the problem for an n-cyclist team. We propose a Mixed-Integer Non-Linear Programming (MI-NLP) formulation to determine the optimal positioning strategy in addition to optimal power and velocity trajectories for a three-cyclist team. The results show improved travel time compared to a baseline strategy that is popular among cycling teams.The results from this work highlight the potential of mathematical optimization of athletes' performance both prior and during a race. The proposed models of fatigue and recovery can help athletes better understand their abilities at their limits. To prepare for a race, cyclists can practice over the simulated elevation profile of the race on a stationary bicycle with the optimal strategies we proposed. Additionally, if allowed by the rules of a specific competition, a cyclist can be provided with real-time race strategies. There is also the potential for integrating the proposed algorithms into the available commercial smart stationary bicycles for improving well-being and training of the general public.

ISBN: 9798544279143Subjects--Topical Terms:

649730
Mechanical engineering.
Subjects--Index Terms:

Anaerobic Work Capacity

Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery.
LDR:05522nmm a2200445 4500 001 2351914
005 20221111113712.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798544279143
035 $a (MiAaPQ)AAI28647608
035 $a AAI28647608
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ashtiani, Faraz. $3 3691508
245 1 0 $a Optimal Pacing of Cyclists in a Time Trial Based on Experimentally Calibrated Models of Fatigue and Recovery.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 113 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Vahidi, Ardalan.
502 $a Thesis (Ph.D.)--Clemson University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a This dissertation pushes on the boundaries of improving human performance through individualized models of fatigue and recovery, and optimization of human physical effort. To that end, it focuses on professional cycling and optimal pacing of cyclists on hilly terrains during individual and team time trial competitions. Cycling is an example of physical activity that provides nice interfaces for real-time performance measurement through inexpensive power meters, and for providing real-time feedback via a mobile displays such as smartphones. These features enable implementing a real-time control system that provides the optimal strategy to cyclists during a race.There has been a lot of work on understanding the physiological concepts behind cyclists' performance. The concepts of Critical Power (CP) and Anaerobic Work Capacity (AWC) have been discussed often in recent cycling performance related articles. CP is a power that can be maintained by a cyclist for a long time; meaning pedaling at or below this limit, can be continued until the nutrients end in the cyclist's body. However, there is a limited source of energy for generating power above CP. This limited energy source is AWC. After burning energy from this tank, a cyclist can recover some by pedaling below CP.In this dissertation, we utilize the concepts of CP and AWC to mathematically model muscle fatigue and recovery of cyclists. We use experimental data from six human subjects to validate and calibrate our proposed dynamic models. The recovery of burned energy from AWC has a slower rate than expending it. The proposed models capture this difference for each individual subject. In addition to the anaerobic energy dynamics, maximum power generation of cyclists is another limiting factor on their performance. We show that the maximum power is a function of both a cyclist's remaining anaerobic energy and the bicycle's speed.These models are employed to formulate the pacing strategy of a cyclist during a time trial as an optimal control problem. Via necessary conditions of Pontryagin's Minimum Principle (PMP), we show that in a time trial, the cyclist's optimal power is limited to only four modes of maximal effort, no effort, pedaling at critical power, or pedaling at constant speed. To determine when to switch between these four modes, we resort to numerical solution via dynamic programming. One of the subjects is then simulated on four courses including that of the 2019 Duathlon National Championship in Greenville, SC. The simulation results show reduced time over experimental results of the self-paced subject who is a competitive amateur cyclist.Moreover, we expand our optimal control formulation from an individual time trial to a Team-Time-Trial (TTT) competition. In a TTT cyclists on each team follow each other closely. This enables the trailing cyclists to benefit from a rather significant reduction in drag force. Through a case study for a two-cyclist team, we show that the distance between the cyclists does not need to be variable and can be set at the minimum safe gap. We then use this observation to formulate the problem for an n-cyclist team. We propose a Mixed-Integer Non-Linear Programming (MI-NLP) formulation to determine the optimal positioning strategy in addition to optimal power and velocity trajectories for a three-cyclist team. The results show improved travel time compared to a baseline strategy that is popular among cycling teams.The results from this work highlight the potential of mathematical optimization of athletes' performance both prior and during a race. The proposed models of fatigue and recovery can help athletes better understand their abilities at their limits. To prepare for a race, cyclists can practice over the simulated elevation profile of the race on a stationary bicycle with the optimal strategies we proposed. Additionally, if allowed by the rules of a specific competition, a cyclist can be provided with real-time race strategies. There is also the potential for integrating the proposed algorithms into the available commercial smart stationary bicycles for improving well-being and training of the general public.
590 $a School code: 0050.
650 4 $a Mechanical engineering. $3 649730
650 4 $a Bioengineering. $3 657580
650 4 $a Mathematics. $3 515831
650 4 $a Physiology. $3 518431
650 4 $a Sports management. $3 3423935
650 4 $a Kinesiology. $3 517627
653 $a Anaerobic Work Capacity
653 $a Critical Power
653 $a Cycling
653 $a Optimal control
653 $a Optimization
653 $a Team Time Trial
653 $a Muscle fatigue
653 $a Racing strategy
690 $a 0548
690 $a 0202
690 $a 0405
690 $a 0575
690 $a 0430
690 $a 0719
710 2 $a Clemson University. $b Mechanical Engineering. $3 1023734
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0050
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647608