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Data-driven Modeling of an Actuated Turbulent Boundary Layer

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Data-driven Modeling of an Actuated Turbulent Boundary Layer

Daniel Fernex (Broschiert, Englisch)

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Abstract Active control of the turbulent boundary layer is a promising technique to reduce the skin friction drag, responsible for about 50% of the overall drag of an aircraft. The actuation investigated in this work consists in spanwise traveling transversal surface waves parametrized by the wavelength, the amplitude, and the period. A maximal drag reduction of 31% is obtained in the tested parameter range. Due to the numerical cost of the highly resolved simulations, only a sparse coverage of the parameter space can be achieved, and real-time computations, for applications such as closed-loop control, are out of reach. This work addresses these limitations with two novel data-driven algorithms, to 1) predict optimal actuation parameters and 2) model the unsteady flow response. In the first part, the self-similar response model (SSM), an empirical and physics-based surrogate method, is employed to model the drag reduction as function of the actuation parameters using a set of 71 large eddy simulations (LESs). In SSM, extrapolation beyond the training parameter range is enabled by two key components, namely the parametrized ridgeline, which indicates the evolution of the optimal response, and a self-similar behavior identified in the interpolation regime. The SSM results highlight the critical importance of the wavelength on the optimal drag reduction, and reveal a constant optimal period, even for varying wavelengths and amplitudes. The extrapolation accuracy for wavelengths 67% beyond the maximal training value is validated by two additional LESs, with low relative errors of 2:1% and 0:7%. In the second part, the unsteady flow dynamics are modeled with the cluster-based network modeling (CNM) algorithm. CNM reproduces the dynamics on a directed network, with nodes identified as coarse-grained states of the systems. The motion between the nodes is characterized by the transition times and probabilities, inferred from the data with high-order Markov chains. The model resolution is simply and reliably controlled with adaptive coarse-graining. CNM accurately reproduces the main dynamics of the actuated boundary layer, validated with the autocorrelation function and cluster probability distributions. The global stability of the method allows robust long-term predictions. Boundary layer quantities, such as the mean streamwise velocity profiles, the skin friction coefficient and Reynolds stresses are faithfully modeled. The modeled velocity field respects the mass conservation. A simple control-oriented formulation of CNM allows the dynamics prediction for new actuation settings beyond the training data. Weniger zeigen

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Produktinformationen

Technische Daten

Erscheinungsdatum

01.01.2021

Sprache

Englisch

EAN

9783947623402

Herausgeber

Technische Uni Braunschweig NFL

Serien- oder Bandtitel

NFL-Forschungsberichte

Sonderedition

Nein

Autor

Daniel Fernex

Seitenanzahl

120

Auflage

Einbandart

Broschiert

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