Experimental and Numerical Investigation of Secondary Flow Structures in an Annular Low Pressure Turbine Cascade Under Periodic Wake Impact—Part 2: Numerical Results

Winhart, Benjamin; Sinkwitz, Martin; Schramm, Andreas; Engelmann, David; Mare, Francesca di; Mailach, Ronald

doi:10.1115/1.4042283

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…This turbulence model is considered by more and more scholars as the preferred choice in the field of fluid machinery. Additionally, it has a good agreement with measurement data [26,27]. The transport equation is:…”

Section: Cfd Analysis Methodssupporting

confidence: 58%

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

2020

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The reliable design of the supercritical carbon dioxide (S-CO2) turbine is the core of the advanced S-CO2 power generation technology. However, the traditional computational fluid dynamics (CFD) method is usually applied in the S-CO2 turbine design-optimization, which is a high computational cost, high memory requirement, and long time-consuming solver. In this research, a flexible end-to-end deep learning approach is presented for the off-design performance prediction of the S-CO2 turbine based on physical fields reconstruction. Our approach consists of three steps: firstly, an optimal design of a 60,000 rpm S-CO2 turbine is established. Secondly, five design variables for off-design analysis are selected to reconstruct the temperature and pressure fields on the blade surface through a deconvolutional neural network. Finally, the power and efficiency of the turbine is predicted by a convolutional neural network according to reconstruction fields. The results show that the prediction approach not only outperforms five classical machine learning models but also focused on the physical mechanism of turbine design. In addition, once the deep model is well-trained, the calculation with graphics processing unit (GPU)-accelerated can quickly predict the physical fields and performance. This prediction approach requires less human intervention and has the advantages of being universal, flexible, and easy to implement.

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Section: Cfd Analysis Methodssupporting

confidence: 58%

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

2020

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“…Thus, especially in the hub region, a distinction between passage vortex (PV) and the pressure side leg of the horse shoe vortex (HSV-PL) is enabled for t/T BP = 1/3, as the HSV-PL slides beneath the PV and pushes it along the suction side trailing edge radially inward, before it blends again with the PV. As could be shown in detail in [52,53,55,56], this periodic and short-duration event is based on the upstream wake impact on the developing HSV-PL, which is massively diverted in the front part of the blade passage by the impinging wake structure. Also, the unsteadiness of the suction side corner separation, shaping the concentrated shed vortex (CSV) can be realized.…”

Section: Impact On the Secondary Flow Structuresmentioning

confidence: 98%

“…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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Broadband pressure spectra induced by unsteady flow in axial turbine

Duan,

Jing,

Zhang

et al. 2024

International Journal of Mechanical Sciences

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Experimental and Numerical Investigation of Secondary Flow Structures in an Annular Low Pressure Turbine Cascade Under Periodic Wake Impact—Part 2: Numerical Results

Cited by 14 publications

References 14 publications

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

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

Broadband pressure spectra induced by unsteady flow in axial turbine

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