H2020Индивидуална стипендия2020–2022

KAFKA · Efficient Computational Methods for Active Flow Control Using Adjoint Sensitivities

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-09-01 → 2022-08-31
Финансиране от ЕС: 224 934 €
Участници: 1
Схема: MSCA-IF

Линиите свързват координатора с партньорите.

Накратко на български

Методите за изчисляване на въздушните потоци се тестват чрез оптимизиране на форми като охлаждащите канали в газовите турбини. По-точните модели помагат за проектирането на по-ефективни аеродинамични елементи, като крила на самолети или ветрогенери.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Efficient Computational Methods for Active Flow Control Using Adjoint Sensitivities

Designing an effective fluid flow system such as a wind turbine or an aircraft wing requires a detailed understanding of the sensitivity of the objective function (for example, efficiency, lift, etc,) to a host of input parameters (for example the parameters determining the aerodynamic shape). Adjoint methods provide the sensitivity of the objective function to any number of input parameters at a reasonable cost. They are usually based on Reynolds-Averaged Navier-Stokes (RANS) models, which can be very inaccurate, especially in the presence of complex flow features such as flow separation. Thus, when the flow prediction itself has a significant error, the sensitivity obtained from the RANS-based adjoint method may not be useful in designing optimal aerodynamic configurations. Our first objective was to improve the accuracy of the RANS turbulence model for separated flows using high-fidelity LES data, that accurately captures the flow physics. The adjoint methodology was used to spatially vary the turbulence-model parameters such that the RANS flow matches the corresponding LES flow. Our next goal was to use the improved RANS turbulence model in shape optimization. The geometry considered was a 3D U-Bend geometry widely studied in literature and integral to gas-turbine cooling channels. The final shape obtained using the improved RANS model was seen to be distinctly different from a shape obtained using an uncorrected baseline RANS model clearly demonstrating the potential of our approach. Our third goal was to study the effect of a refined parametrization of the U-Bend geometry. To bring out the full potential of the KAFKA strategy, we formulated a four-fold increase in the design variables for U-Bend geometry creation and carried out a shape optimization using the LES-aided RANS model, which brought forth much richer design features unseen in a low-parameter design space.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

QUEEN MARY UNIVERSITY OF LONDON · LONDONКоординаторОбединеното кралство