Aerodynamic Performance of Dragonfly-Inspired Corrugated Airfoils

Mulligan, Rosie; Rasool, Azfar

doi:10.1109/inmic50486.2020.9318145

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…These micro-flaps delay flow separation from the trailing edge of the wing and are highly effective in reducing drag by up to 10%, for angles of attack less than 5°as compared to a wing without any flaps. Mulligan and Rasool (2020) [14] noted that the specialized corrugated wings of a dragonfly are responsible for delaying flow separation by angles of attack up to 4°as compared to smooth wings. Other studies aimed at emulating the microstructure present on the dragonfly wing suggest a decrease in drag by about 13% on a wing with bio-inspired zig-zag edge microstructures and about 27% on a wing with zig-zag edge and pillar microstructures as compared to a flat plate wing [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Machine-learning based optimization of a biomimiced herringbone microstructure for superior aerodynamic performance

Patel,

Akolekar

2023

Eng. Res. Express

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Biomimicry involves drawing inspiration from nature's designs to create efficient systems. For instance, the unique herringbone riblet pattern found in bird feathers has proven effective to minimize drag. While attempts have been made to replicate this pattern on structures like plates and aerofoils, there has been a lack of comprehensive optimization of their overall design and of their constituent individual repeating structures. This study attempts to enhance the performance of individual components within the herringbone riblet pattern by leveraging computational fluid dynamics (CFD) and supervised machine learning to reduce drag. The paper outlines a systematic process involving the creation of 107 designs, parameterization, feature selection, generating targets using CFD simulations, and employing regression algorithms. From CFD calculations, the drag coefficients (Cd) for these designs are found, which serve as an input to train supervised learning models. Using the trained transformed target regressor model as a substitute to CFD, Cd values for 10,000 more randomly generated herringbone riblet designs are predicted. The design with the lowest predicted Cd is the optimized design. Notably, the regressed model exhibited an average prediction error rate of 6% on the testing data. The prediction of Cd for the optimized design demonstrated an error of 4% compared to its actual Cd value calculated through CFD. The study also delves into the mechanics of drag reduction in herringbone riblet structures. The resulting optimized microstructure design holds the potential for reducing drag in various applications such as aerospace, automotive, and marine crafts by integrating it onto their surfaces. This innovative approach could significantly transform drag reduction and open pathways to more efficient transportation systems.

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Section: Introductionmentioning

confidence: 99%

Machine-learning based optimization of a biomimiced herringbone microstructure for superior aerodynamic performance

Patel,

Akolekar

2023

Eng. Res. Express

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“…These micro-flaps delay flow separation from the trailing edge of the wing and are highly effective in reducing drag by up to 10%, for angles of attack less than 5 • as compared to a wing without any flaps. Mulligan and Rasool (2020) [13] noted that the specialised corrugated wings of a dragonfly are responsible for delaying flow separation by angles of attack up to 4 • as compared to smooth wings. Other studies aimed at emulating the microstructure present on the dragonfly wing suggest a decrease in drag by about 13% on a wing with bio-inspired zig-zag edge microstructures and about 27% on a wing with zig-zag edge and pillar microstructures as compared to a flat plate wing [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Optimisation of a Biomimiced Herringbone Microstructure for Superior Aerodynamic Performance

Patel

Akolekar

2022

Preprint

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Biomimicry involves taking inspiration from existing designs in nature to generate new and efficient systems. The feathers of birds which form a characteristic herringbone riblet shape are known to effectively reduce drag. This paper aims to optimise the individual constituent structure of a herringbone riblet pattern using a combination of computational fluid dynamics (CFD) and supervised machine learning algorithms to achieve the best possible reduction in drag. Initially, a herringbone riblet design is made by computer aided designing and is parameterised. By randomly varying these parameters, 107 additional designs are made and are subjected to CFD calculations to derive their drag coefficients (Cd). These designs are used to train a supervised learning model which is employed as an alternative to CFD for predicting the Cd of other 10000 randomly generated herringbone riblet designs. Amongst these, the design with the least predicted Cd is considered as the optimised design. The Cd prediction for the optimised design had an error of 4% with respect to its true Cd which was calculated by using CFD. The optimised design of this microstructure can be utilised for drag reduction of aeronautical, automotive or oceanic crafts by integrating them onto their surfaces.

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Aerodynamic Performance of Dragonfly-Inspired Corrugated Airfoils

Cited by 2 publications

References 12 publications

Machine-learning based optimization of a biomimiced herringbone microstructure for superior aerodynamic performance

Machine-learning based optimization of a biomimiced herringbone microstructure for superior aerodynamic performance

Machine-Learning Based Optimisation of a Biomimiced Herringbone Microstructure for Superior Aerodynamic Performance

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