Unsteady wing boundary layer energization

Viets, Hermann; Ball, M.

doi:10.2514/6.1979-1631

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…The intense curvature in the morphology of the armlets of the herringbone riblet microstructure are also responsible for the production of a turbulent flow in the form of vortices in the immediate wake of the structure. It has previously been shown that vortices tend to alter the size of the separation region in the fluid flow from its trailing geometry by reducing pressure and by transporting the higher energy main stream flow inwards by energising the boundary layer [44]. The vortices generated by the armlets of the herringbone riblet microstructure are also responsible for energising the boundary layer and for preventing boundary layer flow separation [17, 45].…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

Patel

Akolekar

2022

Preprint

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“…The intense curvature in the morphology of the armlets of the herringbone riblet microstructure is also responsible for the production of a turbulent flow in the form of vortices in the immediate wake of the structure. It has previously been shown that vortices tend to alter the size of the separation region in the fluid flow from its trailing geometry by reducing pressure and by transporting the higher energy mainstream flow inwards by energizing the boundary layer [49]. The vortices generated by the armlets of the herringbone riblet microstructure are also responsible for energizing the boundary layer and preventing boundary layer flow separation [18,50].…”

Section: Mechanics Of Drag Reductionmentioning

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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“…The present paper will consider only the case of pulsating jets, although there seems to exist a similarity between these two flow control methods. There exists also ample experimental evidence in the literature, [3,7,18,21], for this kind of flow control. A recent detailed review is provided in [2] mentioned above.…”

Section: Introductionmentioning

confidence: 96%

Using Stability Theory to Analyse Configurations of Pulsed Jets for Flow Control

Skamnakis

Papailiou

2007

Flow Turbulence Combust

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During the last decade, efforts for simulating active flow control behavior by the use of pulsating jets have multiplied. In the present work, a URANS is used mostly for simulation, where the resulting flow characteristics can be reproduced with fair but adequate accuracy for engineering applications. This computational tool provides information concerning the effect on the flow, of the flow control. An additional computational tool is introduced, that of flow stability analysis, which allows to optimize the frequency and the position of the actuators (here pulsating jets). This tool will be developed through flow stability arguments. Both tools will be used within the context of the present paper and for reasons explained below, for suppressing only flow separation in internal flow cases. Once the computational tools are described/developed, they will be applied to a specific case. The optimization procedure will be demonstrated and discussed.

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Unsteady wing boundary layer energization

Cited by 7 publications

References 4 publications

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

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

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

Using Stability Theory to Analyse Configurations of Pulsed Jets for Flow Control

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