Numerical Study of Blended Winglet Geometry Variations on Unmanned Aerial Vehicle Aerodynamic Performance

Pertiwi, Fungky Dyan; Wahjudi, Arif

doi:10.12962/j25807471.v6i1.12317

Cited by 4 publications

(4 citation statements)

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“…Several studies have evaluated the performance of blended and cant winglets [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. In most cases, the design is quite simple.…”

Section: Spikeletmentioning

confidence: 99%

“…Balaji et al (2022) [21] compared various winglet shapes on a cambered airfoil and reported that the NACA 6321 airfoil with a 95° blended winglet achieved the highest aerodynamic efficiency due to the optimized angle for drag reduction. Pertiwi et al (2022) [22] used a neural network to predict UAV performance with blended winglets and found that the cant angle had the most significant influence on the lift-to-drag ratio and affected the relationship between the wingtip shape and the cant angle. Al Khafaji et al (2023) [23] used computational analysis and reported that a 60° cant angle, 20% winglet span, and 5° angle of attack yielded the best lift-to-drag ratio for their winglet design.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

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Experimental Study of the Effect of Different Winglet Configurations on The Lift and Drag Characteristics of BWB Aircraft

Ishrak,

Shuvo,

Tasnim

et al. 2024

Preprint

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Sustainable aviation is currently a major concern since people are constantly relying on aircraft for transport over long distances in a short amount of time. Newer technologies are constantly being developed to enhance the aerodynamic efficiency of existing aircraft and improve fuel conservation. Winglets are one such technology. Our research focuses on how different winglet designs affect the lift and drag characteristics of blended-wing body aircrafts; the concept of these designs is currently being researched and compared to that of the base model with no winglet. The models were subjected to both computational and experimental analyses at three different velocities and six angles of attack to determine the lift and drag values resulting from various winglet configurations. The results showed that Split Fence had the best performance in both computational and experimental analyses, with an L/D of 10.608 at a 5° angle of attack during the CFD analysis compared to 5.52 for the base model. The Spikelet and the Spike Scimitar Winglet also showed high aerodynamic efficiency. The C-Winglet, however, showed poorer performance than did the other winglets. The insights from this research can help optimize winglet designs and explore the design of BWB aircraft, leading to substantial reductions in fuel consumption and carbon emissions in the aviation industry.

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Section: Spikeletmentioning

confidence: 99%

Section: Literature Review and Research Gapmentioning

confidence: 99%

Experimental Study of the Effect of Different Winglet Configurations on The Lift and Drag Characteristics of BWB Aircraft

Ishrak,

Shuvo,

Tasnim

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, an examination was conducted to assess the impact of the winglet's offset on the wing's aerodynamic performance. Geometry design uses XFLR5 assistance by choosing winglet factors in the form of offset and cant angle because XFLR5 has limitations in making winglet designs [31]. XFLR5 uses XFOIL data in two dimensions to calculate lift, drag, pitching moment, and pressure coefficient [32].…”

Section: Winglet Geometry Design With Xflr5mentioning

confidence: 99%

Optimization of offset and cant angle winglet on remote control airplane using Taguchi – Particle swarm optimization

Junipitoyo,

Suranto Putro,

Pertiwi

et al. 2023

JMES

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Incorporating winglets into aircraft has been empirically proven to notably improve aerodynamic efficiency by reducing vortex-induced effects at the wingtips. However, conducting comprehensive investigations necessitates the exploration of numerous winglet factors and value variations. This study pertains to remote control aircraft winglets, focusing on manipulating cant angle and offset factors across four distinct values. Two primary objectives guide this research: firstly, the maximization Cl/Cdmax, and secondly, the minimization Cd₀. The Taguchi experimental design is employed to randomize the variations in offset and cant angle values systematically. These variations are then used to generate pivotal regression values in the subsequent particle swarm optimization (PSO). The variance analysis evaluates the impact of winglet-related variables on each research goal. Additionally, the winglet design incorporates the open-source XFLR5 software, an accessible resource for aeromodelling clubs in Indonesia. XFLR5 enables the investigation of aerodynamic parameters across various angles of incidence and plays a crucial role in this research. The results of this study reveal that the Taguchi method yields two distinct combinations of factor values, aligning with the two primary research objectives. Conversely, particle swarm optimization generates a combination that effectively addresses both objectives. A comparative analysis of the winglet factor combinations from Taguchi and PSO underscores the greater efficiency of the PSO method in optimizing winglet variations for two distinct objectives.

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“…The application of the BPNN in their study helped them to correctly estimate the speed of the vehicle. Pertiwi et al [21] employed a BPNN to predict the ratio of lift to drag coefficients at different angles of attack. They trained their model using three input parameters, namely the tip chord, winglet height and cant angle, and one output.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation

et al. 2022

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The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil are usually forecasted using the numerical solution of the Reynolds-averaged Navier–Stokes equation. However, very heavy computational expenses and lengthy progression intervals are associated with this method. Advancements in computational power and efficiency throughout the present era have considerably reduced these costs; however, for many practical applications, performing numerical simulations is still a very computationally expensive and time-consuming task. The application of machine learning techniques has seen a sharp rise in various fields over recent years, including fluid dynamics, and they have proved their worth. In the present study, a famous machine learning model that is known as the back-propagation neural network was implemented for the prediction of the aerodynamic coefficients of airfoils. The most important aerodynamic properties of the coefficient of lift and the coefficient of drag were predicted by providing the model with the name, flow Reynolds number, Mach number and the angle of attack of the airfoils with respect to the incoming flows as input parameters. The dataset for the current study was obtained by performing CFD simulations using the RANS-based Spalart–Allmaras turbulence model on four different NACA series airfoils under varying aerodynamic conditions. The data that were obtained from the CFD simulations were divided into two subsets: 70% were used as training data and the remaining 30% were used as validation and testing data. The BPNN showed promising results for the prediction of the aerodynamic coefficients of airfoils under different conditions. An RMSE value of 3.57 10−7 was achieved for the best performance validation case with 28 epochs when there were 10 neurons in the hidden layer. The regression plot also depicted a close to perfect fit between the predicted and actual values for the regression curves.

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Numerical Study of Blended Winglet Geometry Variations on Unmanned Aerial Vehicle Aerodynamic Performance

Cited by 4 publications

References 0 publications

Experimental Study of the Effect of Different Winglet Configurations on The Lift and Drag Characteristics of BWB Aircraft

Experimental Study of the Effect of Different Winglet Configurations on The Lift and Drag Characteristics of BWB Aircraft

Optimization of offset and cant angle winglet on remote control airplane using Taguchi – Particle swarm optimization

Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation

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