Large Eddy Simulation of Periodic Wake Impact on Boundary Layer Transition Mechanisms on a Highly Loaded Low-Pressure Turbine Blade

Winhart, Benjamin; Sinkwitz, Martin; Schramm, Andreas; Post, Pascal; Mare, Francesca di

doi:10.1115/gt2020-14555

Cited by 5 publications

(3 citation statements)

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“…Xiaodong Ruan et al 11 showed the wake-inducing transition process of a suction surface boundary layer with experimental and large eddy simulation (LES) results. Benjamin W et al 12 used LES to compare two working conditions with or without wake and showed the presence of wake on the boundary layer separation inhibition region. Howell 13 noted the feasibility of making use of nonsteadiness, especially using wake to control loss, on high-lift blades and indicated that the wake has a constraining effect on boundary layer separation.…”

Section: Overviewmentioning

confidence: 99%

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Sun,

Huang,

Kang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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In a high-altitude cruising state, boundary layer separation exists in high-lift low-pressure turbines, and inflow conditions corresponding to different blade designs can directly affect the working efficiency of low-pressure turbines. In particular, the reduced frequency of wake and free-stream turbulence intensity in an inlet flow can greatly influence boundary layer separation and transition development. In this paper, the influence of different inflow turbulence intensities and reduced wake frequencies on the development of suction surface boundary layers in high-lift low-pressure turbines under the influence of upstream wakes is studied by numerical simulations and experiments. Due to the combination of inflow free-stream turbulence intensity and reduced wake frequency, many inflow conditions can be chosen in the design process, and the unsteady influence of upstream wakes complicates the boundary layer flow. In this paper, an RBF (radial basis function)-GA (genetic algorithm) machine learning method is used to explore the optimal inlet conditions corresponding to the minimum profile loss of the Pak-B profile. The search region of the free-stream turbulence intensity is 2%–4%, and the reduced frequency of the wake is changed by changing the flow coefficient, whose variation range is 0.7–1.3. It is found that the RBF-GA machine learning method can attain an inflow condition with a lower profile loss while using the same amount of computation and effort.

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Section: Overviewmentioning

confidence: 99%

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Sun,

Huang,

Kang

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…The presented investigations give a brief outline of key findings from recent publications by the authors, where the unsteady impact of periodic bar wakes on the flow field downstream of the stator row as well as on the stator profile pressures and boundary layers were discussed. For details, please see [51][52][53][54][55][56][57][58].…”

Section: Sub-project D-influence Of Periodic Wakes On the Transient Near-wall Flow In Anmentioning

confidence: 99%

Near-Wall Flow in Turbomachinery Cascades—Results of a German Collaborative Project

Engelmann

Sinkwitz

Mare

et al. 2021

IJTPP

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This article provides a summarizing account of the results obtained in the current collaborative work of four research institutes concerning near-wall flow in turbomachinery. Specific questions regarding the influences of boundary layer development on blades and endwalls as well as loss mechanisms due to secondary flow are investigated. These address skewness, periodical distortion, wake interaction and heat transfer, among others. Several test rigs with modifiable configurations are used for the experimental investigations including an axial low speed compressor, an axial high-speed wind tunnel, and an axial low-speed turbine. Approved stationary and time resolving measurements techniques are applied in combination with custom hot-film sensor-arrays. The experiments are complemented by URANS simulations, and one group focusses on turbulence-resolving simulations to elucidate the specific impact of rotation. Juxtaposing and interlacing their results the four groups provide a broad picture of the underlying phenomena, ranging from compressors to turbines, from isothermal to non-adiabatic, and from incompressible to compressible flows.

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“…For time integration, a non-iterative time advancement scheme is used in combination with a fractional step method for pressure-velocity coupling. The advective fluxes are treated by a bounded central scheme in order to introduce as low numerical dissipation as possible to avoid unphysical dampening of small turbulent structures [69].…”

Section: Test-cases and Numerical Setupmentioning

confidence: 99%

Generative Modeling of Turbulence

Drygala,

Winhart,

di Mare

et al. 2021

Preprint

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We present a mathematically well founded approach for the synthetic modeling of turbulent flows using generative adversarial networks (GAN). Based on the analysis of chaotic, deterministic systems in terms of ergodicity, we outline a mathematical proof that GAN can actually learn to sample state snapshots form the invariant measure of the chaotic system. Based on this analysis, we study a hierarchy of chaotic systems starting with the Lorenz attractor and then carry on to the modeling of turbulent flows with GAN. As training data, we use fields of velocity fluctuations obtained from large eddy simulations (LES). Two architectures are investigated in detail: we use a deep, convolutional GAN (DCGAN) to synthesise the turbulent flow around a cylinder. We furthermore simulate the flow around a low pressure turbine stator using the pix2pixHD architecture for a conditional DCGAN being conditioned on the position of a rotating wake in front of the stator. The settings of adversarial training and the effects of using specific GAN architectures are explained. We thereby show that GAN are efficient in simulating turbulence in technically challenging flow problems on the basis of a moderate amount of training date. GAN training and inference times significantly fall short when compared with classical numerical methods, in particular LES, while still providing turbulent flows in high resolution.

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Large Eddy Simulation of Periodic Wake Impact on Boundary Layer Transition Mechanisms on a Highly Loaded Low-Pressure Turbine Blade

Cited by 5 publications

References 0 publications

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Study of the influence of multiple factors on the boundary layer of a high-lift LPT with the RBF-GA method

Near-Wall Flow in Turbomachinery Cascades—Results of a German Collaborative Project

Generative Modeling of Turbulence

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