Numerical and experimental studies of hydrodynamic performance of bionic leading-edge tubercle airfoil

Guo, Chunyu; Zhang, Zuotian; Cao, Xu-xiang; Wu, Tiecheng; Su, Yumin

doi:10.1007/s42241-019-0068-3

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…These vortices can suppressed flow diversion thus inhibit the cavitation inception and delay the thrust breakdown. Guo Chunyu et al [10] carried out experimental and numerical computational studies on seven types of hydrofoils with different bionic leading edge parameters based on NACA 0020 hydrofoils, and obtained the laws of lift drag performance and hydrofoil parameters. Chen Liu et al [11] investigated the trend of the evolution of the convex-knot vortex series based on NACA 63(4)-021 hydrofoils by numerical computational methods, and clarified the effect of the convex-knot flow results on the chordal and spreading development of the hydrofoil cavitation.…”

Section: Introductionmentioning

confidence: 99%

Investigations on cavitation suppression of bionic water-jet impeller

Wang,

Wan,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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Bionic impeller blades can improve cavitation performance of waterjets. A Special kind of bionic impeller with non-smooth leading edge is designed to improve the NPSH (net positive suction head) performance of waterjets. Based on the cavitation experiment of the waterjet with original impeller, a reliable CFD method using the commercial software StarCCM+ is verified. The detailed flow field of the bionic blades is obtained by simulating the viscous flow field. The stream line, pressure distribution and vortex reveal the mechanism of bionic blades flow adjustment. The influence of bionic impeller factors, which are the amplitude, the interval and the phase, on cavitation performance is systematically studied. The area and volume of the blade cavitation pattern is used to characterize the blade cavitation performance. An efficient method of cavitation pattern recognition is proposed to determine the information of the blade cavitation pattern accurately. A feasible cavitation-adjustment method is proposed to improve the hydraulic performance by optimizing the cavitation distribution and strength.

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Section: Introductionmentioning

confidence: 99%

Investigations on cavitation suppression of bionic water-jet impeller

Wang,

Wan,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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“…Analysis of Ćow past airfoils has many practical applications in aerodynamics (Ismail et al 2015;Singh et al 2022;Gao et al 2017) and hydrodynamics (Guo et al 2019;Karim et al 2014;Sener and Aksu 2022), such as the design of air vehicles, wind turbines, fans, rudders, and aircraft (Lin et al 2013). The airfoil shape is responsible for producing lift and drag for 1…”

Section: Introductionmentioning

confidence: 99%

Predicting aerodynamic characteristics of airfoils using artificial neural network

Hasan,

Faisal,

Zakaria

et al. 2024

Preprint

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In this study, an artificial neural network (ANN)-based method is proposed to predict the aerodynamic characteristics of airfoils, such as NACA 0012, NACA 0015, NACA 0018, NACA 0021, and NACA 0025, approximating the flow around airfoils as a function of the Reynolds number (Re), angle of attack (α), airfoil coordinates (X, Y ), and predicting the lift coefficient (CL) and drag coefficient (CD) without using extensive software packages. Wind turbine data were obtained for CL and CD for different α (0◦ ≤ α ≤ 180◦ ) and different values of Re (104 ≤ Re ≤ 107 ). An ANN model was trained to achieve a root mean square error (RMSE) of less than 0.12 and 0.025 for CL and CD, respectively. For CL and CD, the RMSE of the trained model used to evaluate the new data was less than 0.09 and 0.12, respectively. Subsequently, the results were validated in a two dimensional numerical domain using RANS-CFD simulations and experimental data, showing that the proposed ANN approach is in good agreement for predicting the stall shape and aerodynamic characteristics at an angle of attack (α) ranging from (0◦ ≤ α ≤ 30◦ ).

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“…In aerospace, airfoils such as ailerons, elevators, and flaps are essential control surfaces on aircraft wings, enabling the adjustment of lift and drag forces and facilitating directional movement [3][4][5] . Similarly, in marine engineering, airfoils serve as control surfaces on rudders and fins, allowing for precise maneuvering of ships and submarines, with the ship rudder being a prominent example [6][7][8][9] .…”

Section: Introductionmentioning

confidence: 99%

Fast Aerodynamics Prediction of Wedge Tail Airfoils Using Multi-head Perceptron Network

Hasan,

Rahaman,

Zakaria

2024

Arab J Sci Eng

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The study aimed to predict the flow fields and aerodynamic coefficients of wedge tail airfoils using a multi-head perceptron (MHP) network and classical machine learning (ML) algorithms, including k-nearest neighbors (KNN), decision tree (DT), and random forest (RF). These predictions were based on airfoil sections, x-y grid coordinates, Mach number, and angle of attack, eliminating the need to solve the Navier-Stokes (NS) equations. The database required for training the MHP network and the ML models were generated using the Ansys solver, which solved the NS equations. The results of the models were compared, and it was observed that the MHP network consistently outperformed the others in terms of prediction accuracy. The R 2 score of these models on the test data was found to be close to 1. Additionally, the prediction process demonstrated a significant speed improvement of 125 times compared to conventional computational fluid dynamics (CFD) techniques. Once the training was completed, the flow fields and aerodynamic coefficients for a wedge tail airfoil could be obtained within seconds.

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Numerical and experimental studies of hydrodynamic performance of bionic leading-edge tubercle airfoil

Cited by 10 publications

References 14 publications

Investigations on cavitation suppression of bionic water-jet impeller

Investigations on cavitation suppression of bionic water-jet impeller

Predicting aerodynamic characteristics of airfoils using artificial neural network

Fast Aerodynamics Prediction of Wedge Tail Airfoils Using Multi-head Perceptron Network

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