Design of Low Altitude Long Endurance Solar-Powered UAV Using Genetic Algorithm

Bakar, Abu; Li, Ke; Liu, Haobo; Xu, Zhou; Wen, Dongsheng

doi:10.3390/aerospace8080228

Cited by 21 publications

(15 citation statements)

References 27 publications

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“…To generate the airfoil database, 15 low Reynolds number airfoils were considered. These airfoils are recommended for low-altitude solar-powered UAVs in the literature [37]. These airfoils were parameterized using The results of CFD and XFOIL were in good agreement with experimental data, except for very high angles of attack.…”

Section: Data Generationmentioning

confidence: 72%

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Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Bakar

Liu

et al. 2022

Aerospace

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The airfoil is the prime component of flying vehicles. For low-speed flights, low Reynolds number airfoils are used. The characteristic of low Reynolds number airfoils is a laminar separation bubble and an associated drag rise. This paper presents a framework for the design of a low Reynolds number airfoil. The contributions of the proposed research are twofold. First, a convolutional neural network (CNN) is designed for the aerodynamic coefficient prediction of low Reynolds number airfoils. Data generation is discussed in detail and XFOIL is selected to obtain aerodynamic coefficients. The performance of the CNN is evaluated using different learning rate schedulers and adaptive learning rate optimizers. The trained model can predict the aerodynamic coefficients with high accuracy. Second, the trained model is used with a non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization of the low Reynolds number airfoil at a specific angle of attack. A similar optimization is performed using NSGA-II directly calling XFOIL, to obtain the aerodynamic coefficients. The Pareto fronts of both optimizations are compared, and it is concluded that the proposed CNN can replicate the actual Pareto in considerably less time.

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Section: Data Generationmentioning

confidence: 72%

“…To generate the airfoil database, 15 low Reynolds number airfoils were considered. These airfoils are recommended for low-altitude solarpowered UAVs in the literature [37]. These airfoils were parameterized using CST, using a fourth-order polynomial, which gave ten parameters: five each for the lower and upper surfaces.…”

Section: Data Generationmentioning

confidence: 99%

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Bakar

Liu

et al. 2022

Aerospace

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“…GA needs to set more initial parameters, and its selected values are dependent on manual experience. The final results obtained are random and uncertain, with poor reliability and slow convergence [34]. PSO is simpler, requires fewer initial parameters to be set, has high reliability [35], and is more advantageous for cases with multiple variables, non-linearity, discontinuity, and non-integrability features compared to COA and GA.…”

Section: Network Optimization Of Cfdr Model Based On Bdnn a Cfdr Mode...mentioning

confidence: 99%

A Capability Fitting and Data Reconstruction Model Based on Particle Swarm Optimization-Bidirectional Deep Neural Network for Search and Rescue System of Systems

Gao

Liu

Niu³

et al. 2023

IEEE Access

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Search and rescue (SAR) is an important part of joint operations and a key support for combat effectiveness. Because of the complex composition of the SAR system of systems (SoS), sensitivity analysis method is usually used to carry out sensitivity analysis of SoS capability, so as to determine the main design indicators affecting SoS capability. Reliable sensitivity analysis results are often based on the analysis for sufficient data. However, the SAR SoS capability is affected by many factors and there are numerous design indicators. Even if a small number of design points are selected for each design indicator, tens of thousands of test schemes will be formed, and carrying out all simulation tests will bring huge workload and time cost. To solve this contradiction, this paper introduces a bidirectional deep neural network (BDNN), and takes advantage of its better self-learning and adaptive features and unique structure to train the existing test data, Through strong feature extraction ability of BDNN, the network model between the design indicator and capability indicator is formed, namely, the capability fitting and data reconstruction (CFDR) model, so that the implicit relationship between the two is fixed into the model. In the training process, the number of hidden layers and neurons in each hidden layer, and the amount of training data are explored according to the training effect, so as to obtain a better parameter combination. In order to avoid introducing large cumulative errors accumulated during BDNN pre-training into DNN, particle swarm optimization (PSO) was introduced to optimize weight parameters and avoid large training errors being transmitted to deep neural network (DNN). Meanwhile, three basic functions were used to verify the strong global optimization and convergence abilities of the BDNN after optimized by the PSO (PSO-BDNN). Finally, the new test scheme is applied to the CFDR model to obtain the SoS capability value. The reconstructed data obtained from the CFDR model based on BDNN and PSO-BDNN respectively were compared with the simulation test data.The results show that the reconstruction accuracy of the CFDR model based on the PSO-BDNN is greatly improved than that of the BDNN. And the feasibility of this model as a reconstruction data generation model and the effectiveness of this model as an analysis data extension method applied to the sensitivity analysis of insufficient data to obtain reliable analysis results are verified.INDEX TERMS Capability fitting and data reconstruction model, bidirectional deep neural network, particle swarm optimization, search and rescue, SoS capability sensitivity analysis.The associate editor coordinating the review of this manuscript and approving it for publication was Mitra Mirhassani .

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“…A few months such as continuous air surveillance, communication relay, and meteorological investigations [12].…”

Section: Endurancementioning

confidence: 99%

A Computational Determination of a Nozzle Activated Fixed-Wing Uav

Saraçyakupoğlu

DELİBAŞ

ÖZÇELİK

2022

International Journal of 3D Printing Technologies and Digital Industry

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This paper proposes new methods and strategies for the propulsion of a mini Unmanned Aerial Vehicle (UAV). Generally, the new technology batteries, fossil fuels, and hybrid systems are used to propel the UAVs. In this paper, experimental research regarding the usage of compressed air has been carried out. In total 13 nozzles have been installed to the fuselage of the UAV in terms of gaining 3-axis movement during flight. At the end of the manuscript, a MATLAB (Mathworks, US) program is provided in terms of calculating the maximum force and the flying time with the gained force. The UAV, whose design is presented, will be ideal for both defense and border security tasks, as it can move over the vehicle, has extremely high portability, and has a small physical footprint.

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Design of Low Altitude Long Endurance Solar-Powered UAV Using Genetic Algorithm

Cited by 21 publications

References 27 publications

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

Multi-Objective Optimization of Low Reynolds Number Airfoil Using Convolutional Neural Network and Non-Dominated Sorting Genetic Algorithm

A Capability Fitting and Data Reconstruction Model Based on Particle Swarm Optimization-Bidirectional Deep Neural Network for Search and Rescue System of Systems

A Computational Determination of a Nozzle Activated Fixed-Wing Uav

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