Characteristics of heat transfer on a turbine blade endwall under various inlet flow conditions

Choi, Woosung; Jung, Kuk Jin; Park, Jun Su

doi:10.1080/08916152.2020.1811804

Cited by 6 publications

(9 citation statements)

References 23 publications

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“…From the naphthalene sublimation experiment, a set of 22 high-fidelity samples are generated for the chosen range of input variables. As a result, the mass transfer distribution on the endwall from our previous work [30] is shown in Figure 2. This experiment is an HT experimental method and is good at reproducibility and therefore used for heat and mass transfer analogy.…”

Section: Experiments Of High-fidelity Samplementioning

confidence: 94%

Section: Design Of Experiments For Low-fidelity and High-fidelity Sam...mentioning

confidence: 99%

“…Figure 1 shows the design of experiments for both LF and HF data, where the simulations are used to create LF samples, while experiments are used to create HF samples. The LF and HF data from our previous work [30] for building the surrogate model are adopted in this present work. The HTC on the endwall of the turbine blade is mainly influenced by the external flow conditions such as the of the primary flow and BL thickness.…”

Section: Design Of Experiments For Low-fidelity and High-fidelity Sam...mentioning

confidence: 99%

“…The mass transfer distribution from the experiment is used to calculate the Sherwood number (Sh). Later, the Sherwood number is used to calculate the Nusselt number (Nu) using Equation (3), and HTC can be calculated using Equation ( 5) [30]. Hence, this experiment has the capability of prediction of HT; thus, the local HTC distribution on the endwall can also obtain, and this experimental method is more suitable for obtaining HF data to be compared with LF data from simulations.…”

Section: Experiments Of High-fidelity Samplementioning

confidence: 99%

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Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

et al. 2021

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This paper proposes a multi-fidelity surrogate (MFS) model for predicting the heat transfer coefficient (HTC) on the turbine blades. First, the low-fidelity (LF) and high-fidelity (HF) surrogates were built using LF-data from numerical simulation and HF-data from an experiment. To evaluate the prediction by these two surrogates, the averaged HTC distribution on the endwall of the gas turbine blade predicted by these two surrogates was compared for input variables as Reynolds number (Re) and boundary layer (BL) thickness. This shows that the prediction by LF surrogate is saturated with an increase in Re, while has monotonic behavior with an increase in BL thickness, which is contrary in general. The prediction by HF surrogate is linear with Re and is increased with BL thickness up to 30 mm and then decreased after 30 mm. Following this, a one-dimensional projection of the two-dimensional HTC distribution was presented to show the prediction tendency of the surrogates by varying the Re and fixing the BL thickness, and vice versa. Second, the MFS was built by combining the LF and HF data. The HTC distribution by the MFS model for the same input variables was shown with the HF data points. It is observed that the prediction by MFS is agreed well with the high-fidelity data. The MFS’s one-dimensional projection of the two-dimensional HTC distribution was compared with the LF surrogate prediction by varying the Re and fixing the BL thickness, and vice versa. This shows that the MFS model has more variations due to the included LF data. It is worth to mention that the averaged HTC distribution with an increase in boundary layer thickness predicted by the MFS is agreed well with the LF and HF data in the available dataset and has a large confidence interval between 30 and 50 mm due to the unavailable data in the specified range. To check the MFS accuracy, the root-mean-square error (RMSE) and error rate were evaluated to compare with the experimental uncertainty for a wide range of high-fidelity data. The present study shows that MFS would be expected to be an effective model for saving computing time and experimental costs.

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Section: Experiments Of High-fidelity Samplementioning

confidence: 94%

Section: Design Of Experiments For Low-fidelity and High-fidelity Sam...mentioning

confidence: 99%

Section: Design Of Experiments For Low-fidelity and High-fidelity Sam...mentioning

confidence: 99%

Section: Experiments Of High-fidelity Samplementioning

confidence: 99%

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Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

et al. 2021

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“…However, they did not consider the performance of the propulsion device under parametric changes, such as navigational speed. Choi et al [16] added a member bar in front of the vane to generate nonuniform conduit water flow and observed the heat transfer characteristic on the end wall; however, they did not study parametric changes, such as flow velocity, in the conduit. Cao et al [17] used the CFD method to calculate the hydraulic loss distribution of a waterjet propulsion device, and their calculation results showed that the loss in the propulsion pump was the highest, accounting for approximately half, followed by the loss inside the inlet passage.…”

Section: State Of the Artmentioning

confidence: 99%

Influence of the Inclined Pipe Section on the Performance of a Waterjet Propulsion Device

Chen¹,

Cao²,

Wang³

et al. 2021

Int. j. simul. model.

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A waterjet propulsion device is a new type of propulsion system whose inlet passage affects the performance of the entire device. To study the influence of the inlet passage with an inclined pipe section (IPS) on the hydrodynamic characteristics of the device, a numerical method was used to determine the influences of IPS's length L and rotational speed n on hydraulic performance and thrust characteristics. Results show that the head H of the device increases significantly from 3.29 m to 12.64 m as rotational speed n increases (700 r/min < n < 1300 r/min). Moreover, efficiency η increases from 61.37 % to 64.75 % and then decreases to 60.99 %. L exerts minimal effect on η, which reaches its maximum value when n = 900 r/min and L = 1.12 D (D is the outlet diameter of the inlet passage). Device thrust F gradually increases with an increase in n, and the increase rate continues to rise, while L has minimal effect on F. The results provide a reference for optimizing the inlet passage of a waterjet propulsion device.

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Film cooling effect of upstream jump coolant on turbine endwall with combustor-turbine interface misalignment

Zhang,

Li,

Shao

et al. 2024

International Journal of Thermal Sciences

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Characteristics of heat transfer on a turbine blade endwall under various inlet flow conditions

Cited by 6 publications

References 23 publications

Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

Multi-Fidelity Surrogate Models for Predicting Averaged Heat Transfer Coefficients on Endwall of Turbine Blades

Influence of the Inclined Pipe Section on the Performance of a Waterjet Propulsion Device

Film cooling effect of upstream jump coolant on turbine endwall with combustor-turbine interface misalignment

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