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Efficient Computational Methods for Active Flow Control Using Adjoint Sensitivit...

Efficient Computational Methods for Active Flow Control Using Adjoint Sensitivities Active Flow Control (AFC) mechanisms have tremendous potential in improving flow characteristics in a wide variety of sectors. For effective AFC design, it is essential to determine the sensitivity of each of the control parameter... ver más

31/08/2022

QMUL

225K€

Presupuesto del proyecto: 225K€

Líder del proyecto

QUEEN MARY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-08-31

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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1 Participantes

QMUL

224.93K€ | Lider

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Duración del proyecto: 40 meses Fecha Inicio: 2019-04-15
Fecha Fin: 2022-08-31

Líder del proyecto

QUEEN MARY UNIVERSITY OF LONDON No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 225K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Active Flow Control (AFC) mechanisms have tremendous potential in improving flow characteristics in a wide variety of sectors. For effective AFC design, it is essential to determine the sensitivity of each of the control parameters to the flow property of interest. Adjoint methods provide the sensitivity of the objective function to any number of input parameters at a reasonable additional cost. They are based on simple RANS turbulence modelling, which captures poorly the physics of flow especially in the presence of complex flow features such as flow separation, which can be effectively eliminated by flow control. Recently developed Reynolds Stress Models (RSM) display significant improvement in the flow prediction capacity as compared to conventional RANS models, yet adjoints with RSM are not yet available. An alternative for accurate flow prediction is Large Eddy Simulation (LES). Unfortunately resolving the chaotic turbulent motion results in exponential growth of the gradients. Our first objective is to develop an effective discrete adjoint method with an accurate and stable RSM, and compute sensitivities in complex flows involving active control mechanism applied to realistic wing geometries. Although RSM outperforms conventional turbulence models in a host of applications, there is a need for further physics-based calibration for specific flows. Our second objective is to use the adjoint method to drive the model coefficients to their optimum value such that the model results match with high-fidelity simulation data yielding a better turbulence model, which can be applied for effective flow control design in bluff body with severe rear flow separation. Our third objective will be to develop adjoint approaches for chaotic LES flows using the hosting groups' innovative gappy checkpointing approach that retains the accuracy of the LES but regularises the chaotic motion for the reverse adjoint pass, hence avoiding the exponential blowup of the sensitivities.

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