Optimization Design of Composite Wing Structure of a Minitype Unmanned Aerial Vehicle

2010 ◽  
Vol 156-157 ◽  
pp. 1532-1536
Author(s):  
Yan Zhang ◽  
Fen Fen Xiong ◽  
Shu Xing Yang ◽  
Xiao Ning Mei
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Optimization Design ◽  
Optimal Solution ◽  
Element Model ◽  
Parametric Modeling ◽  
Aerial Vehicle ◽  
Wing Structure ◽  
Stain Analysis ◽  
Optimization Search ◽  
Composite Wing

The application of advanced composites on the aerocraft structure can significantly reduce the weight, and improve the aerodynamic and flight performances. In this work, optimization design of a composite wing structure of a minitype unmanned aerial vehicle (UAV) is implemented. The parametric finite element model is established using parametric modeling technique for stress and stain analysis. The global optimal solution is guaranteed by the proposed two-step optimization search strategy combing genetic algorithm (GA) and sequential quadratic programming (SQP).

scholarly journals Parametric Optimization of Load Bearing Elements for Composite Wing of an Unmanned Aerial Vehicle

2021 ◽  
Vol 346 ◽  
pp. 03093
Author(s):  
Naing Lin Aung ◽  
Oleg Tatarnikov ◽  
Phyo Wai Aung
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Safety Factor ◽  
Structural Elements ◽  
Ansys Software ◽  
Load Bearing ◽  
Aerial Vehicle ◽  
Wing Structure ◽  
Wing Skin ◽  
Composite Wing ◽  
The Ideal

This paper describes the optimizing results of structural elements of the composite wing of an unmanned aerial vehicle. The thickness and composite lay-up structure of load-bearing elements and wing skin are determined using the ANSYS software package. The optimal structure is presented using the Pareto set method of the “ideal center” basing on four criteria: minimum mass, deflection, normal stress, and maximum safety factor of the wing. Verification calculations were carried out to determine the safety factor of the load-bearing wing structure using a geometrically nonlinear model in FEMAP software.

scholarly journals Analysis of Firefly–Fuzzy Hybrid Algorithm for Navigation of Quad-Rotor Unmanned Aerial Vehicle

Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 48
Author(s):  
Brijesh Patel ◽  
Bhumeshwar Patle
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Fuzzy Controller ◽  
Optimal Solution ◽  
Obstacle Detection ◽  
Optimization Techniques ◽  
Multiple Features ◽  
Hybrid Controller ◽  
Aerial Vehicle ◽  
Single Controller ◽  
The Individual

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly–fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.

Structure Optimization Design for the Bed of Gear Hobbing Machine

2013 ◽  
Vol 765-767 ◽  
pp. 176-180
Author(s):  
Rong Chuang Zhang ◽  
Ao Xiang Liu ◽  
Jun Wang ◽  
Wan Shan Wang
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Element Model ◽  
Weighting Coefficient ◽  
Design Parameters ◽  
Analytic Hierarchy ◽  
Design Variables ◽  
Gear Hobbing ◽  
Vibration Modal ◽  
Hierarchy Process

In the optimization design of the gear hobbing machine bed, the finite element model is build and the static analysis and vibration modal analysis are performed. Then sensitivity analysis is used to gain the main design parameters which influence the bed property most. Furthermore, the multi-objective optimization design of the bed is performed in ANSYS Workbench with these design parameters as the design variables. At last, after all optimum proposals are showed up, Analytic Hierarchy Process is used to determine the weighting coefficient, and the most optimal solution is found out. As a result, the dynamic and static performances of the machine bed are improved under control of the machine bed mass.

Static aeroelastic optimisation to wing structural weight estimation of an extremely manoeuvrable UAV

2015 ◽  
Vol 119 (1218) ◽  
pp. 1033-1043
Author(s):  
L. Yi ◽  
Y. Jun ◽  
K. Bin
Keyword(s):  
Conceptual Design ◽  
Element Model ◽  
Empirical Method ◽  
Structure Design ◽  
Load Factor ◽  
Load Factors ◽  
Aerial Vehicle ◽  
Wing Structure ◽  
Aircraft Conceptual Design ◽  
The Finite Element Model

Abstract Estimating the wing structural weight of an extremely manoeuvrable Unmanned Aerial Vehicle (UAV) during conceptual design has proven to be a significant challenge due to its high load factor (the ratio of an aircraft lift to its weight). The traditional empirical method relies on existing statistical data of previously built aircraft, then is inadequate for the innovative UAV structure design which can endure extremely manoeuvrable load (load factor is greater than 9g). In this paper, the finite element model for wing structure of an extremely manoeuvrable UAV with foreplane was built, and the structural weight was estimated by static aeroelastic optimisation considering structural strength and buckling constraints. The methodology developed here is only consisted of three components, which is much less than that for existing method, thus the procedure developed here sacrificed some accuracy, but it’s faster and more suitable for aircraft conceptual design. It was validated by the overlap between the weights given by the methodology, and the results from empirical equations when the load factors are less than 9g. Through the analysis procedure developed, the wing structural weights of the extremely manoeuvrable UAV were given under different load cases (load factor changes from 5g to 12g).

scholarly journals A Comparative Assessment of Different Modeling Algorithms for Estimating Leaf Nitrogen Content in Winter Wheat Using Multispectral Images from an Unmanned Aerial Vehicle

2018 ◽  
Vol 10 (12) ◽  
pp. 2026 ◽  
Author(s):  
Hengbiao Zheng ◽  
Wei Li ◽  
Jiale Jiang ◽  
Yong Liu ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Unmanned Aerial Vehicle ◽  
Processing Speed ◽  
Parametric Modeling ◽  
Comparative Assessment ◽  
Transfer Model ◽  
Leaf Nitrogen Content ◽  
Multispectral Imagery ◽  
Aerial Vehicle ◽  
Non Parametric

Unmanned aerial vehicle (UAV)-based remote sensing (RS) possesses the significant advantage of being able to efficiently collect images for precision agricultural applications. Although numerous methods have been proposed to monitor crop nitrogen (N) status in recent decades, just how to utilize an appropriate modeling algorithm to estimate crop leaf N content (LNC) remains poorly understood, especially based on UAV multispectral imagery. A comparative assessment of different modeling algorithms (i.e., simple and non-parametric modeling algorithms alongside the physical model retrieval method) for winter wheat LNC estimation is presented in this study. Experiments were conducted over two consecutive years and involved different winter wheat varieties, N rates, and planting densities. A five-band multispectral camera (i.e., 490 nm, 550 nm, 671 nm, 700 nm, and 800 nm) was mounted on a UAV to acquire canopy images across five critical growth stages. The results of this study showed that the best-performing vegetation index (VI) was the modified renormalized difference VI (RDVI), which had a determination coefficient (R2) of 0.73 and a root mean square error (RMSE) of 0.38. This method was also characterized by a high processing speed (0.03 s) for model calibration and validation. Among the 13 non-parametric modeling algorithms evaluated here, the random forest (RF) approach performed best, characterized by R2 and RMSE values of 0.79 and 0.33, respectively. This method also had the advantage of full optical spectrum utilization and enabled flexible, non-linear fitting with a fast processing speed (2.3 s). Compared to the other two methods assessed here, the use of a look up table (LUT)-based radiative transfer model (RTM) remained challenging with regard to LNC estimation because of low prediction accuracy (i.e., an R2 value of 0.62 and an RMSE value of 0.46) and slow processing speed. The RF approach is a fast and accurate technique for N estimation based on UAV multispectral imagery.

Selection of the Optimal Load Bearing Wing Structure Scheme for an Unmanned Aerial Vehicle

2020 ◽  
pp. 89-95
Author(s):  
Lin Aung Naing ◽  
W.A. Phyo ◽  
O.V. Tatarnikov
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Element Model ◽  
Limiting Factor ◽  
Load Bearing ◽  
Optimal Load ◽  
Location Parameters ◽  
Optimization Parameters ◽  
Aerial Vehicle ◽  
Structure Scheme ◽  
Calculated Model

This article presents the results of optimization of the load bearing structure of the wing of an unmanned aerial vehicle. The criteria and optimization parameters were considered, respectively: the minimum wing mass, number of spars and ribs, location parameters of the spars and ribs, and thickness of the wing's load bearing elements. The maximum wing deflection was considered as a limiting factor. The calculated model took into account the change in the thickness of the spars along the direction of the wingspan, while the thickness of the skin and ribs was considered as constant. The optimal load bearing scheme of the wing was selected by the criterion of the minimum wing mass based on the maximum permissible deflection of the wing. Calculations of the stress-strain state of the wing were performed using a finite element model in the FEMAP software package.

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