Experimental and Numerical Investigation of the Flow in a Trailing Edge Ribbed Internal Cooling Passage

Baek, Seungchan; Lee, Sangjoon; Hwang, Wontae; Park, Jung Shin

doi:10.1115/1.4041868

Cited by 13 publications

(2 citation statements)

References 23 publications

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“…Although the solver includes a heat transfer analysis module, which we may consider for later optimization, in this present study, we only utilize and focus on its momentum solution (i.e., drag force). We note that the momentum solver has already been applied to the ribbed passage simulation for the internal cooling flow of gas turbine blades and was validated successfully with comparisons to experimental results in the study by Baek et al [37].…”

Section: Flow Simulationmentioning

confidence: 84%

Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing With Large Eddy Simulation

Lee,

Moazam Sheikh,

Lim

et al. 2024

Journal of Mechanical Design

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A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing Design-by-Morphing (DbM), Large Eddy Simulation (LES), and Bayesian Optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of 3 optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8 %. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity of computational design before materialization.

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Section: Flow Simulationmentioning

confidence: 84%

Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing With Large Eddy Simulation

Lee,

Moazam Sheikh,

Lim

et al. 2024

Journal of Mechanical Design

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“…Cukurel et al [15,16] adopted a blockage ratio (e/H) of 0.3, which appears near the trailing edge of the turbine blade [19,20]. In the mid-chord region, its typical value is approximately 0.1, and most previous studies have used this value.…”

Section: Introductionmentioning

confidence: 99%

Fully Coupled Large Eddy Simulation of Conjugate Heat Transfer in a Ribbed Channel with a 0.1 Blockage Ratio

2021

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Large eddy simulations are performed to analyze the conjugate heat transfer of turbulent flow in a ribbed channel with a heat-conducting solid wall. An immersed boundary method (IBM) is used to determine the effect of heat transfer in the solid region on that in the fluid region in a unitary computational domain. To satisfy the continuity of the heat flux at the solid–fluid interface, effective conductivity is introduced. By applying the IBM, it is possible to fully couple the convection on the fluid side and the conduction inside the solid and use a dynamic subgrid scale model in a Cartesian grid. The blockage ratio (e/H) is set at 0.1, which is typical for gas turbine blades. Through conjugate heat transfer analysis, it is confirmed that the heat transfer peak in front of the rib occurs because of the impinging of the reattached flow and not the influence of the thermal boundary condition. When the rib turbulator acts as a fin, its efficiency and effectiveness are predicted to be 98.9% and 8.32, respectively. When considering conjugate heat transfer, the total heat transfer rate is reduced by 3% compared with that of the isothermal wall. The typical Biot number at the internal cooling passage of a gas turbine is <0.1, and the use of the rib height as the characteristic length better represents the heat transfer of the rib.

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Magnetic resonance velocimetry in high-speed turbulent flows: sources of measurement errors and a new approach for higher accuracy

et al. 2020

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This study focuses on the measurement accuracy of Magnetic Resonance Velocimetry (MRV) in high-speed turbulent flows. One of the most prominent errors in MRV is the displacement error, which describes the misregistration of spatial coordinates and velocity components in moving fluids. Displacement errors are particularly critical for experiments with high flow velocity and high spatial resolution. The degree of displacement error also depends on the sequence structure of the MRV technique. In this study, two MRV sequence types are examined regarding their measurement capabilities in highspeed turbulent flows: a conventional MRV sequence based on the popular "4D FLOW" technique, and a newly developed sequence, named "SYNC SPI". Compared to conventional MRV, SYNC SPI is designed for high measurement accuracy, and not for imaging speed, which limits its application to statistically stationary flows. Both sequence types are evaluated in a flow experiment with a converging-diverging nozzle. Time-averaged results are presented for velocities up to 12 m/s at the throat. Supported by Particle Imaging Velocimetry, it is shown that SYNC SPI is capable of acquiring accurate velocity data in these highly turbulent flows. In contrast, the data from the conventional MRV sequence exhibits substantial displacement errors with a maximum displacement of 21 mm. The long acquisition time is the main disadvantage of the SYNC SPI sequence. Therefore, it is examined if undersampling and non-linear reconstruction, known as Compressed Sensing, can be utilized to make data acquisition more efficient. In the presented measurements, Compressed Sensing is successfully applied to shorten the acquisition time by up to 70% with almost no reduction in measurement accuracy.

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Experimental and Numerical Investigation of the Flow in a Trailing Edge Ribbed Internal Cooling Passage

Cited by 13 publications

References 23 publications

Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing With Large Eddy Simulation

Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing With Large Eddy Simulation

Fully Coupled Large Eddy Simulation of Conjugate Heat Transfer in a Ribbed Channel with a 0.1 Blockage Ratio

Magnetic resonance velocimetry in high-speed turbulent flows: sources of measurement errors and a new approach for higher accuracy

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