Advances in Design, Manufacturing, and Dynamics of Complex Systems

Topic Information

Dear Colleagues,

The analysis and understanding of nonlinear systems are paramount for several reasons. Many systems in nature and engineering exhibit nonlinear behavior due to their inherent complexity, making it crucial to understand these systems accurately for predictive purposes. Linear models often fail to capture the complex response of real-world systems, necessitating the use of nonlinear models for more accurate representations. Moreover, nonlinear systems often exhibit emergent properties, where collective behavior leads to novel phenomena not observed in linear systems. Understanding these emergent properties is vital for various applications, including assessing system resilience and stability, designing effective passive and active control strategies, and tuning to the system’s best vibration mitigation or energy harvesting performance. Additionally, nonlinear analysis allows for the exploration of rich and complex dynamics and interactions, such as chaos, bifurcations, and self-organization, providing insights into underlying mechanisms across diverse fields. Therefore, advancements in nonlinear analysis have far-reaching implications for tackling real-world challenges and advancing scientific knowledge.

This Topic serves as a paramount platform for eminent experts, scholars, academics, young scientists, and industry professionals engaged in the interdisciplinary domain of complex engineered systems. It endeavors to showcase the cutting-edge advancements in the field, thereby establishing the forefront of research. With a primary emphasis on comprehending the intricacies of complex nonlinear systems in different areas of engineering and technology, the Topic’s aim is to explore their industrial applications, either existing or prospective, culminating in a substantial impact through the exchange of knowledge and technology transfer. Such focused activities hold the potential to catalyze transformative shifts in both academic discourse and industrial practice, fostering innovation, collaboration, and the development of novel solutions to real-world challenges. By facilitating the dissemination of pioneering research findings and fostering interdisciplinary dialogue, this Topic seeks to not only advance the current state of the art but also inspire new avenues of inquiry and discovery.

Prof. Dr. Abdessattar Abdelkefi
Prof. Dr. Riadh Elleuch
Dr. Daniil Yurchenko
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400	Submit
Drones drones	4.4	5.6	2017	21.7 Days	CHF 2600	Submit
Energies energies	3.0	6.2	2008	17.5 Days	CHF 2600	Submit
Journal of Manufacturing and Materials Processing jmmp	3.3	5.1	2017	14.7 Days	CHF 1800	Submit
Technologies technologies	4.2	6.7	2013	24.6 Days	CHF 1600	Submit

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Published Papers (6 papers)

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18 pages, 1403 KiB  

Open AccessArticle

Novel Energy-Aware 3D UAV Path Planning and Collision Avoidance Using Receding Horizon and Optimization-Based Control

by Gamil Ahmed and Tarek Sheltami

Drones 2024, 8(11), 682; https://doi.org/10.3390/drones8110682 - 19 Nov 2024

Viewed by 680

Abstract

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another [...] Read more.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another critical challenge is the limited flight time imposed by the onboard battery. This paper introduces a novel approach for energy-efficient three-dimensional online path planning for UAV formations operating in complex environments. We formulate the path planning problem as a minimization optimization problem, and employ Mixed-Integer Linear Programming (MILP) to achieve optimal solutions. The cost function is designed to minimize energy consumption while considering the inter-collision and intra-collision avoidance constraints within a limited detection range. To achieve this, an optimization approach incorporating Receding Horizon Control (RHC) is applied. The entire path is divided into segments or sub-paths, with constraints used to avoid collisions with obstacles and other members of the fleet. The proposed optimization approach enables fast navigation through dense environments and ensures a collision-free path for all UAVs. A path-smoothing strategy is proposed to further reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with high risk of collision. We compared our proposed approach against recent works, and the results illustrate that the proposed approach outperforms others in terms of UAV formation, number of collisions, and partial path generation time. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Figure 1

Figure 1
<p>Path planning diagram of RHC.</p> Full article ">Figure 2
<p>Block diagram of the proposed energy-efficient path planning approach.</p> Full article ">Figure 3
<p>Path smoothing strategy.</p> Full article ">Figure 4
<p>Two-dimensional view of UAV paths for different scenarios.</p> Full article ">Figure 5
<p>Three-dimensional view of UAV path planning, showing the effectiveness of the proposed SEA.</p> Full article ">Figure 6
<p>Two- and three-dimensional views of UAV path planning and normalized energy consumption for scenario 1: (<b>a</b>) two-dimensional view, (<b>b</b>) three-dimensional view, and (<b>c</b>) normalized energy consumption.</p> Full article ">Figure 7
<p>Two- and three-dimensional views of UAV path planning and normalized energy consumption for scenario 2: (<b>a</b>) two-dimensional view, (<b>b</b>) three-dimensional view, and (<b>c</b>) normalized energy consumption.</p> Full article ">Figure 8
<p>Minimum distance from UAVs to dynamic obstacles during different missions.</p> Full article ">Figure 9
<p>Average normalized energy consumption over 30 runs for two different obstacle configurations: fifteen static obstacles, and fifteen static obstacles plus fifteen dynamic obstacles.</p> Full article ">

22 pages, 38355 KiB  

Open AccessArticle

Novel Design and Computational Fluid Dynamic Analysis of a Foldable Hybrid Aerial Underwater Vehicle

by Guangrong Chen, Lei Yan, Ao Cao, Xinyuan Zhu, Hongbo Ding and Yuxiang Lin

Drones 2024, 8(11), 669; https://doi.org/10.3390/drones8110669 - 12 Nov 2024

Viewed by 782

Abstract

Hybrid Aerial Underwater Vehicles (HAUVs), capable of operating effectively in both aerial and underwater environments, offer promising solutions for a wide range of applications. This paper presents the design and development of a novel foldable wing HAUV, detailing the overall structural framework and [...] Read more.

Hybrid Aerial Underwater Vehicles (HAUVs), capable of operating effectively in both aerial and underwater environments, offer promising solutions for a wide range of applications. This paper presents the design and development of a novel foldable wing HAUV, detailing the overall structural framework and key design considerations. We employed fluid simulation software to perform comprehensive hydrodynamic and aerodynamic analyses, simulating the vehicle’s behavior during aerial flight, underwater navigation, water entry and exit, and surface gliding. The motion characteristics under different speed and angle conditions were analyzed. Additionally, a physical prototype was constructed, and experimental tests were conducted to evaluate its performance in both aerial and underwater environments. The experimental results confirmed the vehicle’s ability to seamlessly transition between air and water, demonstrating its viability for dual-environment operations. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Figure 1

Figure 1
<p>Total schematic diagram of hybrid aerial underwater vehicle.</p> Full article ">Figure 2
<p>The overall configuration of hybrid aerial underwater vehicle.</p> Full article ">Figure 3
<p>Operational flow chart of hybrid aerial underwater vehicle.</p> Full article ">Figure 4
<p>Control flow chart of hybrid aerial underwater vehicle.</p> Full article ">Figure 5
<p>Four wing airfoils.</p> Full article ">Figure 6
<p>Curves of lift, drag, pitching moment and lift-to-drag ratio for four airfoil types with respect to angle of approach.</p> Full article ">Figure 7
<p>Deployed state diagram of foldable wing mechanism.</p> Full article ">Figure 8
<p>Folded state diagram of foldable wing mechanism.</p> Full article ">Figure 9
<p>X-type Rudder mechanism.</p> Full article ">Figure 10
<p>Simplified 3D model for the aerial flight state.</p> Full article ">Figure 11
<p>The aerodynamic parameters versus angle of attack. (<b>a</b>) Coefficient of lift varies with angle of attack; (<b>b</b>) Coefficient of drag varies with angle of attack; (<b>c</b>) Coefficient of pitching moment varies with angle.</p> Full article ">Figure 12
<p>The pressure cloud of the vehicle in aerial flight state.</p> Full article ">Figure 13
<p>Velocity cloud of the front and rear wings sections of the vehicle in aerial flight state.</p> Full article ">Figure 13 Cont.
<p>Velocity cloud of the front and rear wings sections of the vehicle in aerial flight state.</p> Full article ">Figure 14
<p>Simplified 3D model of the underwater submerged state.</p> Full article ">Figure 15
<p>The hydrodynamic parameters versus angle of attack. (<b>a</b>) Coefficient of lift varies with angle of attack; (<b>b</b>) Coefficient of drag varies with angle of attack; (<b>c</b>) Coefficient of pitching moment varies with angle of attack.</p> Full article ">Figure 16
<p>The pressure cloud of the vehicle in underwater submerged state.</p> Full article ">Figure 16 Cont.
<p>The pressure cloud of the vehicle in underwater submerged state.</p> Full article ">Figure 17
<p>Velocity cloud of the front and rear wings sections of the vehicle in underwater submerged state.</p> Full article ">Figure 18
<p>Air-to-water fluid simulation results.</p> Full article ">Figure 19
<p>The impact of air-to-water at different velocity. (<b>a</b>) The impact of air-to-water at 20 m/s; (<b>b</b>) The impact of air-to-water at 30 m/s.</p> Full article ">Figure 20
<p>Water-to-air fluid simulation results.</p> Full article ">Figure 21
<p>Drag force and pitching moment at different speeds in near water surface state. (<b>a</b>) Coefficient of drag curve at 3 m/s; (<b>b</b>) Coefficient of pitching moment curveat 3 m/s; (<b>c</b>) Coefficient of drag curve at 4 m/s; (<b>d</b>) Coefficient of pitching moment curveat 4 m/s.</p> Full article ">Figure 21 Cont.
<p>Drag force and pitching moment at different speeds in near water surface state. (<b>a</b>) Coefficient of drag curve at 3 m/s; (<b>b</b>) Coefficient of pitching moment curveat 3 m/s; (<b>c</b>) Coefficient of drag curve at 4 m/s; (<b>d</b>) Coefficient of pitching moment curveat 4 m/s.</p> Full article ">Figure 22
<p>The center of mass displacement at different velocities in near water surface state. (<b>a</b>) The total displacement diagram of center of mass; (<b>b</b>) The horizontal displacement diagram of center of mass; (<b>c</b>) The vertical displacement diagram of center of mass.</p> Full article ">Figure 23
<p>Vehicle center of mass displacement at different angles in near water surface state. (<b>a</b>) The total displacement diagram of center of mass; (<b>b</b>) The horizontal displacement diagram of center of mass; (<b>c</b>) The vertical displacement diagram of center of mass.</p> Full article ">Figure 24
<p>The near water surface fluid simulation results at the attack angle −15° and velocity 20 m/s.</p> Full article ">Figure 25
<p>The near water surface fluid simulation results at the attack angle 0° and velocity 20 m/s.</p> Full article ">Figure 26
<p>Prototype of the foldable hybrid aerial underwater vehicle. (<b>a</b>) Prototype of the foldable hybrid aerial underwater vehicle in deployed state; (<b>b</b>) Prototype of the foldable hybrid aerial underwater vehicle in folded state.</p> Full article ">Figure 27
<p>The feasibility validation of aerial flight.</p> Full article ">Figure 27 Cont.
<p>The feasibility validation of aerial flight.</p> Full article ">Figure 28
<p>The feasibility validation of underwater submerging.</p> Full article ">

24 pages, 9637 KiB  

Open AccessArticle

Determining Quasi-Static Load Carrying Capacity of Composite Sandwich Rotor Blades for Copter-Type Drones

by Chien Wei Jan and Tai Yan Kam

Drones 2024, 8(8), 355; https://doi.org/10.3390/drones8080355 - 30 Jul 2024

Viewed by 824

Abstract

The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity [...] Read more.

The development of light composite rotor blades with acceptable load carrying capacity is an essential issue to be dealt with in the design of relatively large copter-type drones. In this paper, a method is established to determine the quasi-static blade load carrying capacity which is vital to drone reliability. The proposed method, which provides a systematic procedure to determine blade load carrying capacity, consists of three parts, namely, a procedure to determine the distributed quasi-static blade aerodynamic load via the Blade Element Momentum (BEM) approach, a finite element-based failure analysis method to identify the actual blade failure mode, and an optimization method to determine the actual blade load carrying capacity. The experimental failure characteristics (failure mode, failure thrust, failure location) of two types of composite sandwich rotor blades with different skin lamination arrangements have been used to verify the accuracy of the theoretical results obtained using the proposed load carrying capacity determination method. The skin lamination arrangement for attaining the optimal blade-specific load carrying capacity and the blade incipient rotational speed for safe drone operation has been determined using the proposed method. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Figure 1
<p>Quadcopter drone.</p> Full article ">Figure 2
<p>Rotor blade: (<b>a</b>) Dimensions; (<b>b</b>) NACA 4418 Airfoil showing skin and core.</p> Full article ">Figure 3
<p>Lamination arrangement of composite sandwich blade (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 3 Cont.
<p>Lamination arrangement of composite sandwich blade (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 4
<p>Blade element.</p> Full article ">Figure 5
<p>Elemental airfoil aerodynamics.</p> Full article ">Figure 6
<p>Experimental setup for rotor blade thrust measurement.</p> Full article ">Figure 7
<p>Iterative procedure for updating vertical uplifting force.</p> Full article ">Figure 8
<p>Locations of blade elements and resultant thrust.</p> Full article ">Figure 9
<p>Rotational speed vs. rotor blade thrust.</p> Full article ">Figure 10
<p>Finite element mesh for composite sandwich blade. (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 11
<p>Iterative procedure for updating incipient failure rotational speed.</p> Full article ">Figure 12
<p>Finished rotor blade product.</p> Full article ">Figure 13
<p>Experimental setup.</p> Full article ">Figure 14
<p>Experimental failure pattern of composite blade (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 15
<p>Failure analysis results for Type 1 blade under resultant thrust. (<b>a</b>) Failure index for Maximum stress criterion (Failure location: x = −0.48, y = 10.99). (<b>b</b>) Failure index for Tsai–Wu criterion (Failure location: x = −0.48, y = 10.59). (<b>c</b>) Buckling mode shape (Failure location: x = −0.323, y = 10.7).</p> Full article ">Figure 15 Cont.
<p>Failure analysis results for Type 1 blade under resultant thrust. (<b>a</b>) Failure index for Maximum stress criterion (Failure location: x = −0.48, y = 10.99). (<b>b</b>) Failure index for Tsai–Wu criterion (Failure location: x = −0.48, y = 10.59). (<b>c</b>) Buckling mode shape (Failure location: x = −0.323, y = 10.7).</p> Full article ">Figure 16
<p>Failure analysis results for Type 2 blade under resultant thrust (<b>a</b>) Failure index for Maximum stress criterion (Failure location: x = 0.12, y = 3.5). (<b>b</b>) Failure index for Tsai–Wu criterion (Failure location: x = 0.12, y = 3.5). (<b>c</b>) Buckling mode shape (Failure location: x = −0.398, y = 4.88).</p> Full article ">Figure 16 Cont.
<p>Failure analysis results for Type 2 blade under resultant thrust (<b>a</b>) Failure index for Maximum stress criterion (Failure location: x = 0.12, y = 3.5). (<b>b</b>) Failure index for Tsai–Wu criterion (Failure location: x = 0.12, y = 3.5). (<b>c</b>) Buckling mode shape (Failure location: x = −0.398, y = 4.88).</p> Full article ">Figure 17
<p>Failure index distribution of composite sandwich blade under elemental thrusts, drag forces, and centrifugal force. (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 17 Cont.
<p>Failure index distribution of composite sandwich blade under elemental thrusts, drag forces, and centrifugal force. (<b>a</b>) Type 1 blade, (<b>b</b>) Type 2 blade.</p> Full article ">Figure 18
<p>Effects of Region 2 length on blade load carrying capacity and weight.</p> Full article ">Figure 19
<p>Effects of Region 2 on failure rotational speed and specific load carrying capacity.</p> Full article ">Figure 20
<p>Relation between blade rotational speed and displacement.</p> Full article ">

19 pages, 8777 KiB  

Open AccessArticle

Development of a Body Weight Support System Employing Model-Based System Engineering Methodology

by Alberto E. Loaiza, Jose I. Garcia and Jose T. Buitrago

Technologies 2024, 12(8), 118; https://doi.org/10.3390/technologies12080118 - 23 Jul 2024

Viewed by 1962

Abstract

Partial body weight support systems have proven to be a vital tool in performing physical therapy for patients with lower limb disabilities to improve gait. Developing this type of equipment requires rigorous design process that obtains a robust system, allowing physiotherapy exercises to [...] Read more.

Partial body weight support systems have proven to be a vital tool in performing physical therapy for patients with lower limb disabilities to improve gait. Developing this type of equipment requires rigorous design process that obtains a robust system, allowing physiotherapy exercises to be performed safely and efficiently. With this in mind, a “Model-Based Systems Engineering” design process using SysML improves communication between different areas, thereby increasing the synergy of interdisciplinary workgroups and positively impacting the development process of cyber-physical systems. The proposed development process presents a work sequence that defines a clear path in the design process, allowing traceability in the development phase. This also ensures the observability of elements related to a part that has suffered a failure. This methodology reduces the integration complexity between subsystems that compose the partial body weight support system because is possible to have a hierarchical and functional system vision at each design stage. The standard allowed requirements to be established graphically, making it possible to observe their system dependencies and who satisfied them. Consequently, the Partial Weight Support System was implemented through with a clear design route obtained by the MBSE methodology. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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<p>Flux diagram of the global development of the BWS in SysML form. (<b>A</b>) General requirements diagram. (<b>B</b>) System layout diagram. (<b>C</b>) Use Case Diagram. (<b>D</b>) Pack diagram. (<b>E</b>) General structure of the system.</p> Full article ">Figure 2
<p>Flux diagram of the BWS mechanism development in SysML form. (<b>A</b>) Mechanism requirements diagram. (<b>B</b>) Mechanism layout diagram. (<b>C</b>) Mechanism transmission system. (<b>D</b>) Mechanism architecture. (<b>E</b>) Internal block diagram of the mechanism. (<b>F</b>) Mechanism parameters.</p> Full article ">Figure 3
<p>Flux diagram of the BWS chassis development in SysML form. (<b>A</b>) Chassis requirements diagram. (<b>B</b>) Chassis layout diagram. (<b>C</b>) Chassis CAD model. (<b>D</b>) Proof cases for the chassis. (<b>E</b>) Final model of the chassis.</p> Full article ">Figure 4
<p>Flux diagram of the BWS power system development in SysML form. (<b>A</b>) Power system requirements diagram. (<b>B</b>) Power system layout diagram. (<b>C</b>) Power system use cases. (<b>D</b>) BWS power system circuit. (<b>E</b>) Power system internal block diagram. (<b>F</b>) Power system sequence diagram.</p> Full article ">Figure 5
<p>Flux diagram of the BWS control system development in SysML form. (<b>A</b>) Control system requirements diagram. (<b>B</b>) Control system layout diagram. (<b>C</b>) Control system use cases. (<b>D</b>) Control system configuration. (<b>E</b>) Control system internal block diagram. (<b>F</b>) Control system sequence diagram.</p> Full article ">Figure 6
<p>Flux diagram of the BWS controller development in SysML form. (<b>A</b>) Controller requirements diagram. (<b>B</b>) Controller layout diagram. (<b>C</b>) Typical control loop. (<b>D</b>) Model simulation of the typical control loop.</p> Full article ">Figure 7
<p>BWS system implementation.</p> Full article ">Figure 8
<p>Comparison between the BWS system chassis and the implemented one.</p> Full article ">

33 pages, 75264 KiB  

Open AccessArticle

Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization

by Abraham Vadillo Morillas, Jesús Meneses Alonso, Alejandro Bustos Caballero and Cristina Castejón Sisamón

Appl. Sci. 2024, 14(14), 6260; https://doi.org/10.3390/app14146260 - 18 Jul 2024

Viewed by 640

Abstract

Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, [...] Read more.

Topology optimization has become a popular tool for designing optimal shapes while meeting specific objectives and restrictions. However, the resulting shape from the optimization process may not be easy to manufacture using typical methods like machining and may require interpretation and validation. Additionally, the final shape depends on chosen parameters. In this study, we conduct a sensitivity analysis of the main parameters involved in 3D topology optimization—penalization and filter radius—focusing on the density-based method. We analyze the features and characteristics of the results, concluding that a machinable and low interpretable part is not an attainable result in by-default topology optimization. Therefore, we propose a new method for obtaining more manufacturable and easily interpretable parts. The main goal is to assist designers in choosing appropriate parameters and understanding what to consider when seeking optimized shapes, giving them a new plug-and-play tool for manufacturable designs. We chose the density-based topology optimization method due to its popularity in commercial packages, and the conclusions may directly influence designers’ work. Finally, we verify the study results through different cases to ensure the validity of the conclusions. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Figure 1
<p>Simplified flowchart of the density-based topology optimization process.</p> Full article ">Figure 2
<p>Graphical representation of the SIMP interpolation power rule for penalization values between 1 and 100.</p> Full article ">Figure 3
<p>Study load case and design domain.</p> Full article ">Figure 4
<p>Element directly accessible for a tool detection process. In blue, solid phase; in green, the element under analysis in coordinates (<span class="html-italic">x</span>,<span class="html-italic">y</span>). Red arrows indicate hypothetical tool trajectory.</p> Full article ">Figure 5
<p>Element accessible for a tool through a neighbor detection process. In blue, solid phase; in green, the element under analysis in coordinates (<span class="html-italic">x</span>,<span class="html-italic">y</span>). Red arrows indicate hypothetical tool trajectory.</p> Full article ">Figure 6
<p>Comparison of the iterations and time per iteration in every TO. (<b>a</b>) Sim. ID vs. Iterations graph: dots are the discrete data, and the blue line represents the corrected data. (<b>b</b>) Sim. ID vs. Mean Iteration Time: dots are the discrete data, and the blue line represents the corrected data.</p> Full article ">Figure 7
<p>Comparison of the measure of non-discreteness and machinability in every TO. (<b>a</b>) Non-Discreteness vs. Sim. ID: dots are the discrete data, and the blue line represents the corrected data. (<b>b</b>) Machinability vs. Sim. ID: dots are the discrete data, and the blue line represents the corrected data.</p> Full article ">Figure 8
<p>Comparison of machinability and measure of non-discreteness. Dots are the discrete data, and the blue line represents the corrected data.</p> Full article ">Figure 9
<p>Comparison of Sim. ID and objective function results. Dots are the discrete data, and the blue line represents the corrected data.</p> Full article ">Figure 10
<p>Flowchart of the TO method, with the machining filter highlighted in pink for clarity of later explanations. Coloured in blue, the Optimality Criteria loop is showcased. In pink, the machining filter step is represented.</p> Full article ">Figure 11
<p>Machining filter, step by step, in x direction. Yellow arrows represent the distance of filtered element to the next non-machinable element in x direction. Green arrows represent the element from whom the filtered element adopts its new density. (<b>a</b>) Initial blueprint design. (<b>b</b>) Highlighted in pink, the elements covered by the filter radii. (<b>c</b>) First column filtering. (<b>d</b>) Design after first column filtering. (<b>e</b>) Element detection for subsequent columns (second column represented). (<b>f</b>) Filtering of subsequent columns (second column represented). (<b>g</b>) Design after second column filtering. (<b>h</b>) Final design after filtering.</p> Full article ">Figure 11 Cont.
<p>Machining filter, step by step, in x direction. Yellow arrows represent the distance of filtered element to the next non-machinable element in x direction. Green arrows represent the element from whom the filtered element adopts its new density. (<b>a</b>) Initial blueprint design. (<b>b</b>) Highlighted in pink, the elements covered by the filter radii. (<b>c</b>) First column filtering. (<b>d</b>) Design after first column filtering. (<b>e</b>) Element detection for subsequent columns (second column represented). (<b>f</b>) Filtering of subsequent columns (second column represented). (<b>g</b>) Design after second column filtering. (<b>h</b>) Final design after filtering.</p> Full article ">Figure 12
<p>Geometrical parameters of the ellipsoid for obtaining its radius from the Cartesian coordinates of the elements being analyzed.</p> Full article ">Figure 13
<p>OC routine with the machining filter highlighted in pink and the filtering steps colored in green.</p> Full article ">Figure A1
<p>Superior perspective view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A1 Cont.
<p>Superior perspective view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A2
<p>Inferior perspective view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A2 Cont.
<p>Inferior perspective view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A3
<p>Lateral view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A3 Cont.
<p>Lateral view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A4
<p>Front view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">Figure A4 Cont.
<p>Front view of the different validation cases. (<b>a</b>) Standard case. (<b>b</b>) Heaviside step filter. (<b>c</b>) Ellipsoid-shaped filter. (<b>d</b>) Machining filter. (<b>e</b>) Ellipsoid-shaped filter + Heaviside step filter. (<b>f</b>) Machining filter + ellipsoid-shaped filter. (<b>g</b>) Machining filter + ellipsoid-shaped filter + changes in adaptive design variable method [<a href="#B40-applsci-14-06260" class="html-bibr">40</a>].</p> Full article ">

25 pages, 10580 KiB  

Open AccessArticle

Aerodynamic Hinge Moment Characteristics of Pitch-Regulated Mechanism for Mars Rotorcraft: Investigation and Experiments

by Qingkai Meng, Yu Hu, Wei Wei, Zhaopu Yao, Zhifang Ke, Haitao Zhang, Molei Zhao and Qingdong Yan

Drones 2024, 8(7), 277; https://doi.org/10.3390/drones8070277 - 21 Jun 2024

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Abstract

The precise regulation of the hinge moment and pitch angle driven by the pitch-regulated mechanism is crucial for modulating thrust requirements and ensuring stable attitude control in Martian coaxial rotorcraft. Nonetheless, the aerodynamic hinge moment in rotorcraft presents time-dependent dynamic properties, posing significant [...] Read more.

The precise regulation of the hinge moment and pitch angle driven by the pitch-regulated mechanism is crucial for modulating thrust requirements and ensuring stable attitude control in Martian coaxial rotorcraft. Nonetheless, the aerodynamic hinge moment in rotorcraft presents time-dependent dynamic properties, posing significant challenges for accurate measurement and assessment for such characteristics. In this study, we delve into the detailed aerodynamic hinge moment characteristics associated with the pitch-regulated mechanism of Mars rotorcraft under a spectrum of control strategies. A robust computational fluid dynamics model was developed to simulate the rotor’s aerodynamic loads, accompanied by a quantitative hinge moment characterization that takes into account the effects of varying rotor speeds and pitch angles. Our investigation yielded a thorough understanding of the interplay between aerodynamic load behavior and rotor surface pressure distributions, leading to the creation of an empirical mapping model for hinge moments. To validate our findings, we engineered a specialized test apparatus capable of measuring the hinge moments of the pitch-regulated mechanism, facilitating empirical assessments under replicated atmospheric conditions of both Earth and Mars. The result indicates aerodynamic hinge moments depend nonlinearly on rotational speed, peaking at a 0° pitch angle and showing minimal sensitivity to pitch under 0°. Above 0°, hinge moments decrease, reaching a minimum at 15° before rising again. Simulation and experimental comparisons demonstrate that under Earth conditions, the aerodynamic performance and hinge moment errors are within 8.54% and 24.90%, respectively. For Mars conditions, errors remain below 11.62%, proving the CFD model’s reliability. This supports its application in the design and optimization of Mars rotorcraft systems, enhancing their flight control through the accurate prediction of aerodynamic hinge moments across various pitch angles and speeds. Full article

(This article belongs to the Topic Advances in Design, Manufacturing, and Dynamics of Complex Systems)

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Figure 1

Figure 1
<p>Basic working principle of rotorcraft: (<b>a</b>) structural composition of rotor flight system; (<b>b</b>) principle diagram of rotor pitch angle change.</p> Full article ">Figure 2
<p>Determination of rotor blade airfoil: (<b>a</b>) clf5605 airfoil; (<b>b</b>) blade chord length; and (<b>c</b>) blade geometric twist angle.</p> Full article ">Figure 3
<p>Rotor blade airfoil parameterization and three-dimensional modeling: (<b>a</b>) airfoil parameterization; (<b>b</b>) rotor three-dimensional modeling.</p> Full article ">Figure 4
<p>Configuration of computational domain.</p> Full article ">Figure 5
<p>Grid division of computational domain: (<b>a</b>) stationary domain; (<b>b</b>) rotating domain; and (<b>c</b>) rotor and local mesh.</p> Full article ">Figure 6
<p>Relationship between rotor aerodynamic performance and pitch angle: (<b>a</b>) relationship between lift coefficient and pitch angle; (<b>b</b>) relationship between torque coefficient and pitch angle; and (<b>c</b>) relationship between lift-to-drag ratio.</p> Full article ">Figure 7
<p>Rotor aerodynamic hinge moment calculation analysis. (<b>a</b>) The relationship between the aerodynamic hinge moment and blade pitch angle; (<b>b</b>) the relationship between the aerodynamic hinge moment and rotor speed.</p> Full article ">Figure 8
<p>The wing profile at 0.75<span class="html-italic">R</span>.</p> Full article ">Figure 9
<p>Force distribution on the wing profile.</p> Full article ">Figure 10
<p>Rotor blade aerodynamic hinge moment analysis at 0.75<span class="html-italic">R</span>.</p> Full article ">Figure 11
<p>The pressure distribution maps on the upper and lower surfaces of the wing profile at 0.75<span class="html-italic">R</span>.</p> Full article ">Figure 11 Cont.
<p>The pressure distribution maps on the upper and lower surfaces of the wing profile at 0.75<span class="html-italic">R</span>.</p> Full article ">Figure 12
<p>The airflow velocity maps on the upper and lower surfaces of the wing profile at 0.75<span class="html-italic">R</span>.</p> Full article ">Figure 13
<p>Test rig system.</p> Full article ">Figure 14
<p>Pitch angle measurement method.</p> Full article ">Figure 15
<p>Principle of pneumatic hinge moment test.</p> Full article ">Figure 16
<p>Earth environment rotor aerodynamic performance analysis.</p> Full article ">Figure 17
<p>The rotor aerodynamic performance test experiment simulating the Martian environment.</p> Full article ">Figure 18
<p>Analysis of rotor aerodynamic performance under simulated Martian environment.</p> Full article ">Figure 19
<p>Process of coupled simulation between Fluent and multibody dynamics.</p> Full article ">Figure 20
<p>Comparison and analysis of aerodynamic hinge moment test and simulation.</p> Full article ">Figure 21
<p>Comparative analysis of calculation results and literature [<a href="#B30-drones-08-00277" class="html-bibr">30</a>].</p> Full article ">

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