Acoustic and flow characteristics of an airfoil fitted with morphed trailing edges

Jawahar, Hasan Kamliya; Azarpeyvand, Mahdi; Silva, Carlos R. Illario da

doi:10.1016/j.expthermflusci.2020.110287

Cited by 11 publications

(4 citation statements)

References 34 publications

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“…Stochastic analysis. Arithmetic mean, standard deviation, and coefficient of variance of the wavelet coefficient modulus, i.e., lðjW x jÞ; rðjW x jÞ, and CV ðjW x jÞ, respectively, 8 were used to examine the stochastic characteristics of the surface pressure fluctuation for the various frequencies of interest. Figure 19 presents the stochastic analysis of the wavelet coefficient modulus from remote sensors at the slat (S1) and the leading-edge of the main-element (M1-M4) for the baseline, finlet-1, and finlet-2 configurations at the angles of attack a ¼ 18 .…”

Section: Persistence Spectrummentioning

confidence: 99%

“…High-lift components, such as slats consisting of broadband and narrowband features in particular, contribute greatly to the airframe noise. Several passive and active flow control methods, such as morphing structures, [1][2][3][4][5][6][7][8] porous materials, [9][10][11][12][13] surface treatments, 14 serrations, [15][16][17][18][19] and transverse jets, 20,21 are being investigated to minimize the overall airframe noise. Despite this, the noise related to the conventional slat and wing configurations remains unresolved.…”

Section: Introductionmentioning

confidence: 99%

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Aeroacoustic characteristics of slat finlets

2021

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An experimental study was performed on a 30P30N three-element high-lift airfoil fitted with different types of slat finlets and its noise reduction capabilities were assessed. To develop a better understanding of the noise reduction mechanism, simultaneous measurements of the unsteady surface pressure were taken at various locations at the vicinity of the slat cove and at far-field locations. While there was a small reduction in farfield noise for the fundamental peak, the use of slat finlets showed a substantial reduction in surface pressure fluctuations. The reduction of vortex shedding energy by the slat finlets also resulted in the reduction of nonpropagating hydrodynamic field between the slat and the main-element substantially reducing the near-field pressure spectra. Fourier and wavelet-based analysis along with high-order spectral analysis were provided for further confirmation of the observations and hypothesis. Finally, the wavelet coefficient findings showed spectral peaks as well as amplitudes modulated in time in the baseline case; however, these peaks were substantially reduced for the finlet configuration with larger spacing.

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Section: Persistence Spectrummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aeroacoustic characteristics of slat finlets

2021

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“…These studies have highlighted the significant impact of flap angle and position on the airfoil's aerodynamic performance. The experimental study on NACA 0012 airfoil with varying flap shapes demonstrated superior lift-drag performance in deformed flap configuration compared to articulated flap airfoil at low AoA [14]. The application of trailing edge flaps with micro baffles in wind turbine blades has shown promise in improving performance and delayed stall onset [15].…”

Section: Introductionmentioning

confidence: 97%

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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“…Active flow control mechanisms use a network of microsensors installed across the blade surface, which actively process sensor feedback to maintain or achieve optimal aerodynamic response through necessary local actuations [10,20]. Popular active control devices include flaps [21][22][23][24][25][26][27][28][29][30], plasma actuators [31], blowing/suction [32], and blade chord/camber/span/twist morphing [33][34][35][36][37][38], among others. Passive control techniques are characterized by simple structure, low cost, and easy implementation, but they offer a narrow adaptation range and poor regulation performance.…”

Section: Introductionmentioning

confidence: 99%