東華大學圖書館 |

Wing Trajectory Optimization and Modelling for Flapping Flight.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Wing Trajectory Optimization and Modelling for Flapping Flight./
作者:	Bayiz, Yagiz E.
面頁冊數:	1 online resource (131 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
標題:	Kinematics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29049966click for full text (PQDT)
ISBN:	9798209937135

Wing Trajectory Optimization and Modelling for Flapping Flight.
Bayiz, Yagiz E.

Wing Trajectory Optimization and Modelling for Flapping Flight. - 1 online resource (131 pages)

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--The Pennsylvania State University, 2021.

Includes bibliographical references

Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic surfaces. However, flapping-wing aerodynamics are characterized by highly unsteady and three-dimensional flows difficult to model or control, and accurate aerodynamic force predictions often rely on expensive computational or experimental methods. As a result, optimal flapping wing trajectories are often difficult to identify. Moreover, the vast wing trajectory space available to flapping fliers renders this optimization problem even more arduous.This dissertation aimed to develop the necessary tools to pursue flapping wing trajectory optimization through modeling, optimization, machine learning, and robotics. To achieve this goal, first, a dimensionless and multi-objective wing trajectory optimization framework based on a quasi-steady aerodynamic model was developed. With this framework, the family of optimal wing trajectories maximizing lift generation and minimizing power consumption was identified together with the corresponding Pareto fronts. This optimization was repeated at various Reynolds numbers (Re, from 100 to 8000) and aspect ratios (from 2 to 8) to reveal the sensitivity of the optimal wing trajectories and Pareto fronts to these control variables. These results were later compared with the performance of rotary wings. This study showed that the rotary flight is more power-efficient when the lift requirement is low, whereas the flapping flight is more capable and efficient in generating a high lift. Furthermore, it was also observed that as Reynolds number drops, the flapping wings become more and more preferable compared to the rotary wings. Next, a policy gradient algorithm was implemented on a dynamically scaled robotic wing to train the robot to (locally) optimal wing trajectories for flapping wings at the low Re. This model-less learning scheme avoided the issues observed in model-based trajectory optimization, and it was applied to two distinct scenarios. The first scenario was designed as an efficiency maximization problem for wing trajectories with simple parameterization and two degrees of freedom. In order to investigate the effects of stroke amplitude on the maximal efficiency, the wing was trained repeatedly with various prescribed stroke amplitudes while Re was kept constant. It was observed that as stroke amplitudes increased, the optimum efficiency increased. In the second application, a lift maximization problem at Re =1200 hovering flight was solved. In comparison to the first problem, this application included all three degrees of freedom of the wing kinematics in the learning problem and allowed the significant amount of trajectory space available to flapping fliers. Additionally, the locomotion control was performed by a central pattern generator (CPG) network. The CPG provided a biologically inspired means to generate rhythmic wing trajectories, enabling the application of the algorithms to even more complex problems and reducing the time span of the learning experiments by improving the sample generation speed. The results implied that the deviation from the stroke plane, which was often overlooked in the literature on wing kinematics optimization, might play an important role in lift generation. These studies were among the first to demonstrate.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798209937135Subjects--Topical Terms:

571109
Kinematics.
Index Terms--Genre/Form:

542853
Electronic books.

Wing Trajectory Optimization and Modelling for Flapping Flight.
LDR:04767nmm a2200397K 4500 001 2354137
005 20230324111208.5
006 m o d
007 cr mn ---uuuuu
008 241011s2021 xx obm 000 0 eng d
020 $a 9798209937135
035 $a (MiAaPQ)AAI29049966
035 $a (MiAaPQ)PennState_24644yeb3
035 $a AAI29049966
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Bayiz, Yagiz E. $3 3694481
245 1 0 $a Wing Trajectory Optimization and Modelling for Flapping Flight.
264 0 $c 2021
300 $a 1 online resource (131 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
500 $a Advisor: Cheng, Bo.
502 $a Thesis (Ph.D.)--The Pennsylvania State University, 2021.
504 $a Includes bibliographical references
520 $a Flying animals resort to fast, large-degree-of-freedom motion of flapping wings, a key feature that distinguishes them from rotary or fixed-winged robotic fliers with limited motion of aerodynamic surfaces. However, flapping-wing aerodynamics are characterized by highly unsteady and three-dimensional flows difficult to model or control, and accurate aerodynamic force predictions often rely on expensive computational or experimental methods. As a result, optimal flapping wing trajectories are often difficult to identify. Moreover, the vast wing trajectory space available to flapping fliers renders this optimization problem even more arduous.This dissertation aimed to develop the necessary tools to pursue flapping wing trajectory optimization through modeling, optimization, machine learning, and robotics. To achieve this goal, first, a dimensionless and multi-objective wing trajectory optimization framework based on a quasi-steady aerodynamic model was developed. With this framework, the family of optimal wing trajectories maximizing lift generation and minimizing power consumption was identified together with the corresponding Pareto fronts. This optimization was repeated at various Reynolds numbers (Re, from 100 to 8000) and aspect ratios (from 2 to 8) to reveal the sensitivity of the optimal wing trajectories and Pareto fronts to these control variables. These results were later compared with the performance of rotary wings. This study showed that the rotary flight is more power-efficient when the lift requirement is low, whereas the flapping flight is more capable and efficient in generating a high lift. Furthermore, it was also observed that as Reynolds number drops, the flapping wings become more and more preferable compared to the rotary wings. Next, a policy gradient algorithm was implemented on a dynamically scaled robotic wing to train the robot to (locally) optimal wing trajectories for flapping wings at the low Re. This model-less learning scheme avoided the issues observed in model-based trajectory optimization, and it was applied to two distinct scenarios. The first scenario was designed as an efficiency maximization problem for wing trajectories with simple parameterization and two degrees of freedom. In order to investigate the effects of stroke amplitude on the maximal efficiency, the wing was trained repeatedly with various prescribed stroke amplitudes while Re was kept constant. It was observed that as stroke amplitudes increased, the optimum efficiency increased. In the second application, a lift maximization problem at Re =1200 hovering flight was solved. In comparison to the first problem, this application included all three degrees of freedom of the wing kinematics in the learning problem and allowed the significant amount of trajectory space available to flapping fliers. Additionally, the locomotion control was performed by a central pattern generator (CPG) network. The CPG provided a biologically inspired means to generate rhythmic wing trajectories, enabling the application of the algorithms to even more complex problems and reducing the time span of the learning experiments by improving the sample generation speed. The results implied that the deviation from the stroke plane, which was often overlooked in the literature on wing kinematics optimization, might play an important role in lift generation. These studies were among the first to demonstrate.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Kinematics. $3 571109
650 4 $a Coordinate transformations. $3 3684063
650 4 $a Vortices. $3 3681507
650 4 $a Genetic algorithms. $3 533907
650 4 $a Aerodynamics. $3 532569
650 4 $a Optimization. $3 891104
650 4 $a Insects. $3 516323
650 4 $a Correlation analysis. $3 3684579
650 4 $a Reynolds number. $3 3681436
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Computer science. $3 523869
650 4 $a Engineering. $3 586835
650 4 $a Fluid mechanics. $3 528155
650 4 $a Mechanics. $3 525881
650 4 $a Physics. $3 516296
655 7 $a Electronic books. $2 lcsh $3 542853
690 $a 0538
690 $a 0800
690 $a 0984
690 $a 0537
690 $a 0204
690 $a 0346
690 $a 0605
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The Pennsylvania State University. $3 699896
773 0 $t Dissertations Abstracts International $g 83-10B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29049966 $z click for full text (PQDT)