Effect of Gurney flap on the vortex-dominated flow over low-AR wings

Durai, Arivoli; Singh, Ishan

doi:10.1007/s00348-023-03605-y

Cited by 3 publications

(2 citation statements)

References 67 publications

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“…The utilization of segmentation and edge or object detection techniques is crucial to derive important flow physical quantities from output data obtained through imaging methods. Examples of features that may be sought include, the coordinates of a dye plume (Yadav 2018), oil or smoke patterns (Arivoli 2023), the position of a shock wave (Kováics et al 2023), vortical structures (Lindner et al 2020) or particle clusters (Metzger et al 2022), species concentrations and iso-surfaces (Zheng et al 2022), interfaces and zones (Reuther and Kähler 2018). The methodologies applied in this context span a range of experimental techniques (Tropea et al 2007), including chemiluminescence (CL) (Guethe et al 2012), laser induced fluorescence (LIF) (Eghtesad et al 2024) and phosphorescence (LIP) (Charogiannis 2013), filtered Rayleigh scattering (FRS) (Doll et al 2023), thermography (Astarita et al 2006), schlieren imaging and shadowgraphy (Settles and Hargather 2017), high-speed photography (Versluis 2013), dye injection (Di et al 2022), smoke visualization (Willmott et al 1997), oil film measurements (Cai et al 2022) as well as experiments involving shear-or temperature/pressuresensitive liquid crystals (SLC, TLC) (Ireland and Jones Jul 2000) and paints (TSP, PSP) (Gregory et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based image segmentation for instantaneous flame front extraction

Strässle,

Faldella,

Doll

2024

Exp Fluids

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This paper delves into the methodology employed in examining lean premixed turbulent flame fronts extracted from Planar Laser Induced Fluorescence (PLIF) images at elevated pressures. In such flow regimes, the PLIF signal suffers from significant collisional quenching, typically resulting in image data with low signal-to-noise ratio (SNR). This poses severe difficulties for conventional flame front extraction algorithms based on intensity gradients and requires intense user intervention to yield acceptable results. In this work, we propose Convolutional Neural Network (CNN)-based Deep Learning (DL) models as an alternative to problem specific conventional methods. The pretrained DL models were fine-tuned, employing data augmentation, on a small annotated dataset including a variety of conditions between SNR $$\approx$$ ≈ 1.6 to 2.6 and subsequently evaluated. All DL models significantly outperformed the best-scoring conventional implementation both quantitatively and visually, while having similar inference times. IoU-scores and Recall values were found to be up to a factor $$\approx$$ ≈ 1.2 and $$\approx$$ ≈ 2.5 higher, respectively, with $$\approx$$ ≈ 1.15 times improved Precision. Small-scale structures were captured much better with fewer erroneous predictions, becoming particularly pronounced for the lower SNR data investigated. Moreover, by applying artificially modeled noise, it was shown that the range of image conditions in terms of SNR that can be reliably processed extends well beyond the images included in the training data, and satisfactory segmentation performances were found for SNR as low as $$\approx$$ ≈ 1.1. The presented DL-based flame front detection algorithm marks a methodology with significantly increased detection performance, while a similar computational effort for inference is achieved and the need for user-based parameter tuning is eliminated. It enables a very accurate extraction of instantaneous flame fronts in large image datasets where supervised processing is infeasible, unlocking unprecedented possibilities for the study of flame dynamics and instability mechanisms at industry-relevant conditions.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based image segmentation for instantaneous flame front extraction

Strässle,

Faldella,

Doll

2024

Exp Fluids

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“…Their experimental results showed that a height of 2% chord length performed the best, but the simultaneous installation of Gurney flaps and vortex generators decreased the lift-to-drag ratio of the foil. Avivoli and Singh [26] explored the aerodynamic effects of Gurney flaps on three different low-aspect-ratio wings through experimental studies. Their findings revealed that Gurney flaps effectively increase the maximum lift coefficient and stall angle by augmenting the pressure difference between the wing's upper and lower surfaces, thereby enhancing the aerodynamic performance.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization Design of Dynamic Performance of Hydrofoil with Gurney Flap

Zhang,

Yao,

Ding

et al. 2024

JMSE

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The horizontal axis tidal turbine, as a crucial device for capturing tidal energy, has gained significant attention because it has better energy efficiency performance. Enhancing the performance of foils, a vital part of tidal turbine blades, can significantly improve tidal turbine performance. Among numerous methods to enhance the foil performance, the Gurney flap has gained significant attention due to its avoidance of complex structural design. Currently, there is limited research on optimizing the design of Gurney flaps while considering the dynamic performance of foils. In this study, the S809 foil with a blade cross-section was selected as the research subject, a multi-objective optimization design platform was created by integrating a multi-objective optimization algorithm with Computational Fluid Dy-namics (CFD) numerical simulation techniques. The objective of this platform is to enhance the dynamic performance of the hydrofoil by optimizing the geometric structure of the Gurney flap. The improvement of dynamic lift and the size of the dynamic stall hysteresis loop are used as objective variables in this study to evaluate the hydrofoil’s dynamic performance. The optimal Latin hypercube design method is used in the optimization process to choose sample locations, and the Kriging approximation model is used to determine the relationship between the design variables and the objective variables. Meanwhile, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is used to create a multi-objective optimization platform for solving the optimization problem, and the optimized results are validated using CFD. Comparative validation results show that quantifying the dynamic performance during hydrofoil pitching oscillation and using the optimal Latin hypercube design method and Kriging approximation model for optimizing the Gurney flap structure is rational and accurate. This study explores the mechanism of the Gurney flap through in-depth CFD numerical simulations and finds that the Gurney flap affects the flow characteristics at the hydrofoil’s trailing edge, thereby influencing the performance. It increases the pressure difference between the pressure and suction surfaces, thus enhancing the hydrofoil’s lift. Finally, this article provides three recommended parameters to improve the dynamic performance of the hydrofoil. This research can serve as a reference for the application of Gurney flaps in tidal turbine blade design.

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Experimental study of turbulent inflow on the aerodynamic performance of a wind turbine with Gurney flaps

Yang,

Wang

2024

Journal of Renewable and Sustainable Energy

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Nowadays, wind turbines operate within complex inflow environments. Meanwhile, installing Gurney flaps on existing wind turbines could enhance wind energy efficiency. However, limited research has been conducted on the variation of aerodynamic characteristics of a wind turbine equipped with Gurney flaps under turbulent inflow conditions. Hence, wind tunnel test comparisons were made between the output power, wind load, and wake characteristics of a model wind turbine with and without Gurney flaps. The results demonstrated a correlation between the additional power increase in the wind turbine equipped with Gurney flaps and the aerodynamic variation of the corresponding airfoil. Gurney flaps could be effective at higher tip speed ratios, and the power enhancement efficiency initially increased but then decreased as turbulence intensity increased from a low value to 19.0%. Installing Gurney flaps resulted in significant pulsation peaks within the original inertial sub-range. The time-averaged thrust coefficient shifts upward, but the difference decreases slightly under turbulent conditions. Wake analysis revealed that the presence of additional wake velocity deficits primarily concentrated within the near-wake region, which extends along the spanwise direction. These findings could enhance a better understanding of the aerodynamic performances of wind turbines installing Gurney flaps under varying turbulent flow conditions.

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Effect of Gurney flap on the vortex-dominated flow over low-AR wings

Cited by 3 publications

References 67 publications

Deep learning-based image segmentation for instantaneous flame front extraction

Deep learning-based image segmentation for instantaneous flame front extraction

Multi-Objective Optimization Design of Dynamic Performance of Hydrofoil with Gurney Flap

Experimental study of turbulent inflow on the aerodynamic performance of a wind turbine with Gurney flaps

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