Numerical Simulation of Flow over Bionic Airfoil

Hao, Lishu; Gao, Yongwei; Wei, Binbin; Song, Ke

doi:10.1155/2021/5556463

Cited by 5 publications

(4 citation statements)

References 21 publications

(26 reference statements)

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“…As a supporting argument in this study, the Cl-Cl baseline data were compared with previous studies (Hao et al, 2021). The results of the data comparison are shown in Figure 8.…”

Section: Resultsmentioning

confidence: 59%

Analysis of the Use of Bio Flap on NACA 4415 with Numerical Methods

Julian,

Siswanto,

Wahyuni

et al. 2023

ASIIMETRIK

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This study was conducted using the Computational Fluid Dynamics (CFD) method using the Reynolds Averaged Navier Stokes (RANS) approach. The type of airfoil used in this study is the asymmetry NACA 4415 airfoil type. In this paper, computational tests were carried out on the airfoil with the addition of bionic flaps on its trailing edge. This study's update tests three variations of the Reynolds number: Re = 106, Re = 5 × 105, and Re = 3 × 105. The airfoil test was carried out at AoA 0°–25°. The addition of bionic flaps causes a decrease in lift performance at low AoA, but at high AoA, it can increase lift performance on airfoils. In addition, adding a bionic flap on the airfoil can delay the occurrence of a stall. At AoA 10°–13°, the Cd of the three variations of the Reynolds number experiences an increase in performance. Then, from this computational test, the resulting Coefficient moment (Cm) is a pitch down because the torque is below zero.

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“…As a supporting argument in this study, the Cl-Cl baseline data were compared with previous studies (Hao et al, 2021). The results of the data comparison are shown in Figure 8.…”

Section: Resultsmentioning

confidence: 59%

Analysis of the Use of Bio Flap on NACA 4415 with Numerical Methods

Julian,

Siswanto,

Wahyuni

et al. 2023

ASIIMETRIK

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“…The results show that the flap can provide an average increase in lift of around 20% at AoA 20° (Rosti, Omidyeganeh and Pinelli, 2018). Then, another study examined the Bionic flap with varying angles on the HQ17 airfoil at Re = 106, the results showed that the bionic flap was quite effective in increasing the lift to the drag ratio at AoA is more than 7° (Hao et al, 2021). Various types of passive flow control devices have been discussed with the addition or modification of flaps to improve aerodynamic performance; this is in line with what was carried out in this study to examine airfoils with the addition of gurney flaps on the trailing edge of the airfoil.…”

Section: Introductionmentioning

confidence: 96%

Numerical Analysis of Gurney Flap Impact on NACA 4415 Airfoil Aerodynamics Performance

James Julian,

Mirza Fauzan Lukiano,

Fitri Wahyuni

et al. 2024

ASIIMETRIK

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Improving airfoil aerodynamic performance is an essential aspect of aerodynamic technology. The use of passive flow control is one way to enhance the aerodynamic performance of the airfoil. The influence of using gurney flaps as passive flow control was explored through the CFD approach employing the RANS control equations with the k-epsilon turbulence model. The airfoil model utilized in this investigation was the NACA 4415 operating at a Reynolds number of 1×106. This study explored three different variations of flap height, namely 0.5%, 1%, and 2% of chord length. The outcomes showed that adding gurney flaps showed quite positive results in increasing the lift and drag performance of NACA 4415. An airfoil with a 0.5%c height flap has an average percentage increase in Cl of 12%, followed by a height flap of 1%c, which is 23%, and a percentage Cl of 37% for a height flap of 2%c. Meanwhile, each variation in height flap affected the increase in Cd. A height flap of 0.5%c increased Cd with an average percentage of 2%, while a height flap of 1%c increased the percentage of Cd by 4% and 6% for a height flap of 2%c. Moreover, visualization of fluid flow with pressure and velocity contours given at AoA 12º to determine the effect on the increase in Cland Cd in NACA 4415.

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“…In addition to the common single-element airfoils, multi-element airfoils have garnered considerable attention across various disciplines. Numerical investigations using CFD methods have explored the aerodynamic performance of bionic airfoils equipped with trailing edge flaps [13]. These studies have highlighted the significant impact of flap angle and position on the airfoil's aerodynamic performance.…”

Section: Introductionmentioning

confidence: 99%

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

Zhang,

Hu,

Shi

et al. 2024

Aerospace

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Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

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Numerical Simulation of Flow over Bionic Airfoil

Cited by 5 publications

References 21 publications

Analysis of the Use of Bio Flap on NACA 4415 with Numerical Methods

Analysis of the Use of Bio Flap on NACA 4415 with Numerical Methods

Numerical Analysis of Gurney Flap Impact on NACA 4415 Airfoil Aerodynamics Performance

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

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